WO2025208420A1

WO2025208420A1 - Network-side artificial intelligence based model measurement prediction

Info

Publication number: WO2025208420A1
Application number: PCT/CN2024/085872
Authority: WO
Inventors: Fangli Xu; Alexander Sirotkin; Haijing Hu; Peng Cheng; Ping-Heng Kuo; Ralf ROSSBACH; Yuqin Chen; Zhibin Wu
Original assignee: Apple Inc
Current assignee: Apple Inc
Priority date: 2024-04-03
Filing date: 2024-04-03
Publication date: 2025-10-09
Anticipated expiration: 2026-10-03

Abstract

A method of enabling network-side artificial intelligence (AI) based model measurements by a user equipment (UE). The method includes decoding, from a network, configuration information for dedicated training measurement events, wherein the dedicated training measurement events correspond to a set of defined measurement events used by the network, and the dedicated training measurement events are used exclusively for training one or more network-side AI based models; performing, by the UE, one or more measurements based on the dedicated training measurement events; and encoding, for transmission to the network, one or more measurement reports based on the dedicated training measurement events, wherein the measurement reports are used for training the one or more network-side AI based models.

Description

NETWORK-SIDE ARTIFICIAL INTELLIGENCE BASED MODEL MEASUREMENT PREDICTION

FIELD

Embodiments of the invention relate to wireless communications, including apparatuses, systems, and methods for enabling network-side artificial intelligence based model measurements in a cellular communications network.

DESCRIPTION OF THE RELATED ART

Wireless communication systems are rapidly growing in usage. In recent years, wireless devices such as smart phones and tablet computers have become increasingly sophisticated. In addition to supporting telephone calls, many mobile devices now provide access to the internet, email, text messaging, and navigation using the global positioning system (GPS) and are capable of operating sophisticated applications that utilize these functionalities.

Long Term Evolution (LTE) has been the technology of choice for the majority of wireless network operators worldwide, providing mobile broadband data and high-speed Internet access to their subscriber base. LTE was first proposed in 2004 and was first standardized in 2008. Since then, as usage of wireless communication systems has expanded exponentially, demand has risen for wireless network operators to support a higher capacity for a higher density of mobile broadband users. In 2015, a study of a new radio access technology began and, in 2017, a first release of Fifth Generation New Radio (5G NR) was standardized.

5G-NR, also simply referred to as NR, provides, as compared to LTE, a higher capacity for a higher density of mobile broadband users, while also supporting device-to-device, ultra-reliable, and massive machine type communications with lower latency and/or lower battery consumption. Further, NR may allow for more flexible UE scheduling as compared to current LTE. Consequently, efforts are being made in ongoing developments of 5G-NR to take advantage of higher throughputs possible at higher frequencies.

SUMMARY

Embodiments relate to wireless communications, and more particularly to apparatuses, systems, and methods for an apparatus of a user equipment (UE) , the apparatus comprising one or more processors, coupled to a memory, configured to: decode, from a network, configuration information for dedicated training measurement events, wherein the dedicated training measurement events correspond to a set of defined measurement events used by the network, and the dedicated training measurement events are used exclusively for training one or more network-side AI based models; perform, by the UE, one or more measurements based on the dedicated training measurement events; and encode, for transmission to the network, one or more measurement reports based on the dedicated training measurement events, wherein the measurement reports are used for training the one or more network-side AI based models.

Other embodiments relate to an apparatus of a base station (e.g., base station (base station) ) , the apparatus comprising one or more processors, coupled to a memory, configured to: encode, for transmission to a user equipment (UE) , configuration information for dedicated training measurement events to enable the UE to perform, by the UE, one or more measurements based on the dedicated training measurement events, wherein the dedicated training measurement events correspond to a set of defined measurement events used by the network, and the dedicated training measurement events are used exclusively for training one or more network-side AI based models; and decode, from the UE, one or more measurement reports based on the dedicated training measurement events, wherein the measurement reports are used for training the one or more network-side AI based models.

The techniques described herein may be implemented in and/or used with a number of different types of devices, including but not limited to unmanned aerial vehicles (UAVs) , unmanned aerial controllers (UACs) , base stations, access points, cellular phones, tablet computers, wearable computing devices, portable media players, and any of various other computing devices.

This Summary is intended to provide a brief overview of some of the subject matter described in this document. Accordingly, it will be appreciated that the above-described features are merely examples and should not be construed to narrow the scope or spirit of the subject matter described herein in any way. Other features, aspects, and advantages of the subject matter described herein will become apparent from the following Detailed Description, Figures, and Claims.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the present subject matter can be obtained when the following detailed description of various embodiments is considered in conjunction with the following drawings, in which:

FIG. 1A illustrates an example wireless communication system according to some embodiments.

FIG. 1 B illustrates an example of a base station and an access point in communication with a user equipment (UE) device, according to some embodiments.

FIG. 2 illustrates an example block diagram of a base station, according to some embodiments.

FIG. 3 illustrates an example block diagram of a server according to some embodiments.

FIG. 4 illustrates an example block diagram of a UE according to some embodiments.

FIG. 5 illustrates an example block diagram of cellular communication circuitry, according to some embodiments.

FIG. 6 illustrates an example of a baseband processor architecture for a UE, according to some embodiments.

FIG. 7 illustrates an example block diagram of an interface of baseband circuitry according to some embodiments.

FIG. 8 illustrates an example of a control plane protocol stack in accordance with some embodiments.

FIG. 9 illustrates an example timing diagram signaling between a user equipment (UE) and base station (e.g., a base station (base station) ) for enabling network-side artificial intelligence measurement prediction according to some embodiments.

FIG. 10 illustrates an example flow chart of a method of enabling network-side artificial intelligence based model measurement at a user equipment (UE) , according to some embodiments.

FIG. 11 illustrates an example flow chart of a method of enabling network-side artificial intelligence based model inference measurement at a base station, according to some embodiments.

While the features described herein may be susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and are herein described in detail. It should be understood, however, that the drawings and detailed description thereto are not intended to be limiting to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the subject matter as defined by the appended claims.

DETAILED DESCRIPTION

Terms

The following is a glossary of terms used in this disclosure:

Memory Medium –Any of various types of non-transitory memory devices or storage devices. The term “memory medium” is intended to include an installation medium, e.g., a CD-ROM, floppy disks, or tape device; a computer system memory or random-access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc. ; a non-volatile memory such as a Flash, magnetic media, e.g., a hard drive, or optical storage; registers, or other similar types of memory elements, etc. The memory medium may include other types of non-transitory memory as well or combinations thereof. In addition, the memory medium may be located in a first computer system in which the programs are executed or may be located in a second different computer system which connects to the first computer system over a network, such as the Internet. In the latter instance, the second computer system may provide program instructions to the first computer for execution. The term “memory medium” may include two or more memory mediums which may reside in different locations, e.g., in different computer systems that are connected over a network. The memory medium may store program instructions (e.g., embodied as computer programs) that may be executed by one or more processors.

Carrier Medium –a memory medium as described above, as well as a physical transmission medium, such as a bus, network, and/or other physical transmission medium that conveys signals such as electrical, electromagnetic, or digital signals.

Programmable Hardware Element includes various hardware devices comprising multiple programmable function blocks connected via a programmable interconnect. Examples include FPGAs (Field Programmable Gate Arrays) , PLDs (Programmable Logic Devices) , FPOAs (Field Programmable Object Arrays) , and CPLDs (Complex PLDs) . The programmable function blocks may range from fine grained (combinatorial logic or look up tables) to coarse grained (arithmetic logic units or processor cores) . A programmable hardware element may also be referred to as "reconfigurable logic” .

Computer System (or Computer) –any of various types of computing or processing systems, including a personal computer system (PC) , mainframe computer system, workstation, network appliance, Internet appliance, personal digital assistant (PDA) , television system, grid computing system, or other device or combinations of devices. In general, the term "computer system" can be broadly defined to encompass any device (or combination of devices) having at least one processor that executes instructions from a memory medium.

User Equipment (UE) (or “UE Device” ) –any of various types of computer systems devices which are mobile or portable and which performs wireless communications. Examples of UE devices include mobile telephones or smart phones (e.g., iPhone^TM, Android^TM-based phones) , portable gaming devices (e.g., Nintendo DS^TM, PlayStation Portable^TM, Gameboy Advance^TM, iPhone^TM) , laptops, wearable devices (e.g., smart watch, smart glasses) , PDAs, portable Internet devices, music players, data storage devices, other handheld devices, unmanned aerial vehicles (UAVs) (e.g., drones) , UAV controllers (UACs) , and so forth. In general, the term “UE” or “UE device” can be broadly defined to encompass any electronic, computing, and/or telecommunications device (or combination of devices) which is easily transported by a user and capable of wireless communication.

Base Station –The term "Base Station" has the full breadth of its ordinary meaning, and at least includes a wireless communication station installed at a fixed location and used to communicate as part of a wireless telephone system or radio system.

Processing Element (or Processor) –refers to various elements or combinations of elements that are capable of performing a function in a device, such as a user equipment or a cellular network device. Processing elements may include, for example: processors and associated memory, portions or circuits of individual processor cores, entire processor cores, processor arrays, circuits such as an ASIC (Application Specific Integrated Circuit) , programmable hardware elements such as a field programmable gate array (FPGA) , as well any of various combinations of the above.

Channel -a medium used to convey information from a sender (transmitter) to a receiver. It should be noted that since characteristics of the term “channel” may differ according to different wireless protocols, the term “channel” as used herein may be considered as being used in a manner that is consistent with the standard of the type of device with reference to which the term is used. In some standards, channel widths may be variable (e.g., depending on device capability, band conditions, etc. ) . For example, LTE may support scalable channel bandwidths from 1.4 MHz to 20MHz. 5G NR can support scalable channel bandwidths from 5 MHz to 100 MHz in Frequency Range 1 (FR1) and up to 400 MHz in FR2. In other radio access technologies, WLAN channels may be 22 MHz wide while Bluetooth channels may be 1 MHz wide. Other protocols and standards may include different definitions of channels. Furthermore, some standards may define and use multiple types of channels, e.g., different channels for uplink or downlink and/or different channels for different uses such as data, control information, etc.

Band -The term "band" has the full breadth of its ordinary meaning, and at least includes a section of spectrum (e.g., radio frequency spectrum) in which channels are used or set aside for the same purpose.

Automatically –refers to an action or operation performed by a computer system (e.g., software executed by the computer system) or device (e.g., circuitry, programmable hardware elements, ASICs, etc. ) , without user input directly specifying or performing the action or operation. Thus, the term "automatically" is in contrast to an operation being manually performed or specified by the user, where the user provides input to directly perform the operation. An automatic procedure may be initiated by input provided by the user, but the subsequent actions that are performed “automatically” are not specified by the user, i.e., are not performed “manually” , where the user specifies each action to perform. For example, a user filling out an electronic form by selecting each field and providing input specifying information (e.g., by typing information, selecting check boxes, radio selections, etc. ) is filling out the form manually, even though the computer system will update the form in response to the user actions. The form may be automatically filled out by the computer system where the computer system (e.g., software executing on the computer system) analyzes the fields of the form and fills in the form without any user input specifying the answers to the fields. As indicated above, the user may invoke the automatic filling of the form but is not involved in the actual filling of the form (e.g., the user is not manually specifying answers to fields but rather they are being automatically completed) . The present specification provides various examples of operations being automatically performed in response to actions the user has taken.

Approximately -refers to a value that is almost correct or exact. For example, approximately may refer to a value that is within 1 to 10 percent of the exact (or desired) value. It should be noted, however, that the actual threshold value (or tolerance) may be application dependent. For example, in some embodiments, “approximately” may mean within 0.1%of some specified or desired value, while in various other embodiments, the threshold may be, for example, 2%, 3%, 5%, and so forth, as desired or as set by the particular application.

Concurrent –refers to parallel execution or performance, where tasks, processes, or programs are performed in an at least partially overlapping manner. For example, concurrency may be implemented using “strong” or strict parallelism, where tasks are performed (at least partially) in parallel on respective computational elements, or using “weak parallelism” , where the tasks are performed in an interleaved manner, e.g., by time multiplexing of execution threads.

Legacy -The 3rd Generation Partnership Project (3GPP) produces specifications that define 3GPP technologies. 3GPP specifications cover cellular telecommunications technologies, including radio access, core network and service capabilities, which provide a complete system description for mobile telecommunications. 3GPP uses a system of parallel “Releases” that provides developers with a stable platform for the implementation of features at a given point and then allows for the addition of new functionality in subsequent releases. Release 17 was released in 2022. Release 18 (Rel-18) , at the time of this disclosure, is nearing release on June 22, 2024, as its specifications have been largely defined. Accordingly, implementations and concepts compatible with Rel-18, or previous Releases, are sometimes referred to herein as “Legacy Releases. ” One or more embodiments of the present disclosure may be adopted in future Releases, e.g., Release 19.

Various components may be described as “configured to” perform a task or tasks. In such contexts, “configured to” is a broad recitation generally meaning “having structure that” performs the task or tasks during operation. As such, the component can be configured to perform the task even when the component is not currently performing that task (e.g., a set of electrical conductors may be configured to electrically connect a module to another module, even when the two modules are not connected) . In some contexts, “configured to” may be a broad recitation of structure generally meaning “having circuitry that” performs the task or tasks during operation. As such, the component can be configured to perform the task even when the component is not currently on. In general, the circuitry that forms the structure corresponding to “configured to” may include hardware circuits.

Various components may be described as performing a task or tasks, for convenience in the description. Such descriptions should be interpreted as including the phrase “configured to. ” Reciting a component that is configured to perform one or more tasks is expressly intended not to invoke 35 U.S.C. § 112 (f) interpretation for that component.

The example embodiments may be further understood with reference to the following description and the related appended drawings, wherein like elements are provided with the same reference numerals. The example embodiments relate to enabling network-side artificial intelligence based model measurement prediction.

The example embodiments are described with regard to communication between a base station and a user equipment (UE) . However, reference to a base station or a UE is merely provided for illustrative purposes. The example embodiments may be utilized with any electronic component that may establish a connection to a network and is configured with the hardware, software, and/or firmware to support enabling network-side artificial intelligence based model inference validation. Therefore, the base station or UE as described herein is used to represent any appropriate type of electronic component.

The example embodiments are also described with regard to a fifth generation (5G) New Radio (NR) network that may configure a UE to control the UE side for enabling network-side artificial intelligence based model measurement prediction. However, reference to a 5G NR network is merely provided for illustrative purposes. The example embodiments may be utilized with any appropriate type of network.

As described the mechanisms of the illustrated embodiments provide a user equipment (UE) , comprising one or more processors, coupled to a memory, may be configured to: decode, from a network, configuration information for dedicated training measurement events, wherein the dedicated training measurement events correspond to a set of defined measurement events used by the network, and the dedicated training measurement events are used exclusively for training one or more network-side AI based models; perform, by the UE, one or more measurements based on the dedicated training measurement events; and encode, for transmission to the network, one or more measurement reports based on the dedicated training measurement events, wherein the measurement reports are used for training the one or more network-side AI based models.

Throughout this description various information elements (IEs) are referred to by specific names. It should be understood that these names are only examples and the IEs carrying the information referred to throughout this description may be referred to by other names by various entities.

FIGs. 1A and 1B: Communication Systems

FIG. 1A illustrates a simplified example wireless communication system, according to some embodiments. It is noted that the system of FIG. 1A is merely one example of a possible system, and that features of this disclosure may be implemented in any of various systems, as desired.

As shown, the example wireless communication system includes a base station 102A which communicates over a transmission medium with one or more user devices 106A, 106B, etc., through 106N. Each of the user devices may be referred to herein as a “user equipment” (UE) . Thus, the user devices 106 are referred to as UEs or UE devices.

The base station (BS) 102A may be a base transceiver station (BTS) or cell site (a “cellular base station” ) and may include hardware that enables wireless communication with the UEs 106A through 106N.

The communication area (or coverage area) of the base station may be referred to as a “cell. ” The base station 102A and the UEs 106 may be configured to communicate over the transmission medium using any of various radio access technologies (RATs) , also referred to as wireless communication technologies, or telecommunication standards, such as GSM, UMTS (associated with, for example, WCDMA or TD-SCDMA air interfaces) , LTE, LTE-Advanced (LTE-A) , 5G new radio (5G NR) , HSPA, 3GPP2 CDMA2000 (e.g., 1xRTT, 1xEV-DO, HRPD, eHRPD) , etc. Note that if the base station 102A is implemented in the context of LTE, also referred to as the Evolved Universal Terrestrial Radio Access Network (E-UTRAN, it may alternately be referred to as an 'eNodeB' or ‘eNB’ . Note that if the base station 102A is implemented in the context of 5G NR, it may alternately be referred to as ‘gNodeB’ or ‘base station’ .

As shown, the base station 102A may also be equipped to communicate with a network 100 (e.g., a core network of a cellular service provider, a telecommunication network such as a public switched telephone network (PSTN) , and/or the Internet, among various possibilities) . Thus, the base station 102A may facilitate communication between the user devices and/or between the user devices and the network 100. In particular, the cellular base station 102A may provide UEs 106 with various telecommunication capabilities, such as voice, SMS and/or data services.

Base station 102A and other similar base stations (such as base stations 102B…102N) operating according to the same or a different cellular communication standard may thus be provided as a network of cells, which may provide continuous or nearly continuous overlapping service to UEs 106A-N and similar devices over a geographic area via one or more cellular communication standards.

Thus, while base station 102A may act as a “serving cell” for UEs 106A-N as illustrated in FIG. 1A, each UE 106 may also be capable of receiving signals from (and possibly within communication range of) one or more other cells (which might be provided by base stations 102B-N and/or any other base stations) , which may be referred to as “neighboring cells” . Such cells may also be capable of facilitating communication between user devices and/or between user devices and the network 100. Such cells may include “macro” cells, “micro” cells, “pico” cells, and/or cells which provide any of various other granularities of service area size. For example, base stations 102A-B illustrated in FIG. 1A might be macro cells, while base station 102N might be a micro cell. Other configurations are also possible.

In some embodiments, base station 102A may be a next generation base station, e.g., a 5G New Radio (5G NR) base station, or “base station” . In some embodiments, a base station may be connected to a legacy evolved packet core (EPC) network and/or to a NR core (NRC) network. In addition, a base station cell may include one or more transition and reception points (TRPs) . In addition, a UE capable of operating according to 5G NR may be connected to one or more TRPs within one or more base stations.

Note that a UE 106 may be capable of communicating using multiple wireless communication standards. For example, the UE 106 may be configured to communicate using a wireless networking (e.g., Wi-Fi) and/or peer-to-peer wireless communication protocol (e.g., Bluetooth, Wi-Fi peer-to-peer, etc. ) in addition to at least one cellular communication protocol (e.g., GSM, UMTS (associated with, for example, WCDMA or TD-SCDMA air interfaces) , LTE, LTE-A, 5G NR, HSPA, 3GPP2 CDMA2000 (e.g., 1xRTT, 1xEV-DO, HRPD, eHRPD) , etc. ) . The UE 106 may also or alternatively be configured to communicate using one or more global navigational satellite systems (GNSS, e.g., GPS or GLONASS) , one or more mobile television broadcasting standards (e.g., ATSC-M/H or DVB-H) , and/or any other wireless communication protocol, if desired. Other combinations of wireless communication standards (including more than two wireless communication standards) are also possible.

FIG. 1B illustrates user equipment 106 (e.g., one of the devices 106A through 106N) in communication with a base station 102 and an access point 112, according to some embodiments. The UE 106 may be a device with both cellular communication capability and non-cellular communication capability (e.g., Bluetooth, Wi-Fi, and so forth) such as a mobile phone, a hand-held device, a computer or a tablet, or virtually any type of wireless device.

The UE 106 may include a processor that is configured to execute program instructions stored in memory. The UE 106 may perform any of the method embodiments described herein by executing such stored instructions. Alternatively, or in addition, the UE 106 may include a programmable hardware element such as an FPGA (field-programmable gate array) that is configured to perform any of the method embodiments described herein, or any portion of any of the method embodiments described herein.

The UE 106 may include one or more antennas for communicating using one or more wireless communication protocols or technologies. In some embodiments, the UE 106 may be configured to communicate using, for example, CDMA2000 (1xRTT /1xEV-DO /HRPD /eHRPD) , LTE/LTE-Advanced, or 5G NR using a single shared radio and/or GSM, LTE, LTE-Advanced, or 5G NR using the single shared radio. The shared radio may couple to a single antenna, or may couple to multiple antennas (e.g., for MIMO) for performing wireless communications. In general, a radio may include any combination of a baseband processor, analog RF signal processing circuitry (e.g., including filters, mixers, oscillators, amplifiers, etc. ) , or digital processing circuitry (e.g., for digital modulation as well as other digital processing) . Similarly, the radio may implement one or more receive and transmit chains using the aforementioned hardware. For example, the UE 106 may share one or more parts of a receive and/or transmit chain between multiple wireless communication technologies, such as those discussed above.

In some embodiments, the UE 106 may include separate transmit and/or receive chains (e.g., including separate antennas and other radio components) for each wireless communication protocol with which it is configured to communicate. As a further possibility, the UE 106 may include one or more radios which are shared between multiple wireless communication protocols, and one or more radios which are used exclusively by a single wireless communication protocol. For example, the UE 106 might include a shared radio for communicating using either LTE or 5G NR (or LTE or 1xRTTor LTE or GSM) , and separate radios for communicating using each of Wi-Fi and Bluetooth. Other configurations are also possible.

FIG. 2: Block Diagram of a Base Station

FIG. 2 illustrates an example block diagram of a base station 102, according to some embodiments. It is noted that the base station of FIG. 2 is merely one example of a possible base station. As shown, the base station 102 may include processor (s) 204 which may execute program instructions for the base station 102. The processor (s) 204 may also be coupled to memory management unit (MMU) 240, which may be configured to receive addresses from the processor (s) 204 and translate those addresses to locations in memory (e.g., memory 260 and read only memory (ROM) 250) or to other circuits or devices.

The base station 102 may include at least one network port 270. The network port 270 may be configured to couple to a telephone network and provide a plurality of devices, such as UE devices 106, access to the telephone network as described above in FIGs. 1a, 1 b and 2.

The network port 270 (or an additional network port) may also or alternatively be configured to couple to a cellular network, e.g., a core network of a cellular service provider. The core network may provide mobility related services and/or other services to a plurality of devices, such as UE devices 106. In some cases, the network port 270 may couple to a telephone network via the core network, and/or the core network may provide a telephone network (e.g., among other UE devices serviced by the cellular service provider) .

In some embodiments, base station 102 may be a next generation base station, e.g., a 5G New Radio (5G NR) base station, or “base station” . In such embodiments, base station 102 may be connected to a legacy evolved packet core (EPC) network and/or to a NR core (NRC) network. In addition, base station 102 may be considered a 5G NR cell and may include one or more transition and reception points (TRPs) . In addition, a UE capable of operating according to 5G NR may be connected to one or more TRPs within one or more base stations.

The base station 102 may include at least one antenna 234, and possibly multiple antennas. The at least one antenna 234 may be configured to operate as a wireless transceiver and may be further configured to communicate with UE devices 106 via radio 230. The antenna 234 communicates with the radio 230 via communication chain 232. Communication chain 232 may be a receive chain, a transmit chain or both. The radio 230 may be configured to communicate via various wireless communication standards, including, but not limited to, 5G NR, LTE, LTE-A, GSM, UMTS, CDMA2000, Wi-Fi, etc.

The base station 102 may be configured to communicate wirelessly using multiple wireless communication standards. In some instances, the base station 102 may include multiple radios, which may enable the base station 102 to communicate according to multiple wireless communication technologies. For example, as one possibility, the base station 102 may include an LTE radio for performing communication according to LTE as well as a 5G NR radio for performing communication according to 5G NR. In such a case, the base station 102 may be capable of operating as both an LTE base station and a 5G NR base station. As another possibility, the base station 102 may include a multi-mode radio which is capable of performing communications according to any of multiple wireless communication technologies (e.g., 5G NR and Wi-Fi, LTE and Wi-Fi, LTE and UMTS, LTE and CDMA2000, UMTS and GSM, etc. ) .

As described further subsequently herein, the BS 102 may include hardware and software components for implementing or supporting implementation of features described herein. The processor 204 of the base station 102 may be configured to implement or support implementation of part or all of the methods described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium) . Alternatively, the processor 204 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array) , or as an ASIC (Application Specific Integrated Circuit) , or a combination thereof. Alternatively (or in addition) the processor 204 of the BS 102, in conjunction with one or more of the other components 230, 232, 234, 240, 250, 260, 270 may be configured to implement or support implementation of part or all of the features described herein.

In addition, as described herein, processor (s) 204 may be comprised of one or more processing elements. In other words, one or more processing elements may be included in processor (s) 204. Thus, processor (s) 204 may include one or more integrated circuits (ICs) that are configured to perform the functions of processor (s) 204. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc. ) configured to perform the functions of processor (s) 204.

Further, as described herein, radio 230 may be comprised of one or more processing elements. In other words, one or more processing elements may be included in radio 230. Thus, radio 230 may include one or more integrated circuits (ICs) that are configured to perform the functions of radio 230. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc. ) configured to perform the functions of radio 230.

FIG. 3: Block Diagram of a Server

FIG. 3 illustrates an example block diagram of a server 104, according to some embodiments. It is noted that the server of FIG. 3 is merely one example of a possible server. As shown, the server 104 may include processor (s) 344 which may execute program instructions for the server 104. The processor (s) 344 may also be coupled to memory management unit (MMU) 374, which may be configured to receive addresses from the processor (s) 344 and translate those addresses to locations in memory (e.g., memory 364 and read only memory (ROM) 354) or to other circuits or devices.

The server 104 may be configured to provide a plurality of devices, such as base station 102, and UE devices 106 access to network functions, e.g., as further described herein.

In some embodiments, the server 104 may be part of a radio access network, such as a 5G New Radio (5G NR) radio access network. In some embodiments, the server 104 may be connected to a legacy evolved packet core (EPC) network and/or to a NR core (NRC) network.

As described herein, the server 104 may include hardware and software components for implementing or supporting implementation of features described herein. The processor 344 of the server 104 may be configured to implement or support implementation of part or all of the methods described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium) . Alternatively, the processor 344 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array) , or as an ASIC (Application Specific Integrated Circuit) , or a combination thereof. Alternatively (or in addition) the processor 344 of the server 104, in conjunction with one or more of the other components 354, 364, and/or 374 may be configured to implement or support implementation of part or all of the features described herein.

In addition, as described herein, processor (s) 344 may be comprised of one or more processing elements. In other words, one or more processing elements may be included in processor (s) 344. Thus, processor (s) 344 may include one or more integrated circuits (ICs) that are configured to perform the functions of processor (s) 344. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc. ) configured to perform the functions of processor (s) 344.

FIG. 4: Block Diagram of a User Equipment

FIG. 4 illustrates an example simplified block diagram of a communication device 106, according to some embodiments. It is noted that the block diagram of the communication device of FIG. 4 is only one example of a possible communication device. According to embodiments, communication device 106 may be a user equipment (UE) device, a mobile device or mobile station, a wireless device or wireless station, a desktop computer or computing device, a mobile computing device (e.g., a laptop, notebook, or portable computing device) , a tablet, an unmanned aerial vehicle (UAV) , a UAV controller (UAC) and/or a combination of devices, among other devices. As shown, the communication device 106 may include a set of components 400 configured to perform core functions. For example, this set of components may be implemented as a system on chip (SOC) , which may include portions for various purposes. Alternatively, this set of components 400 may be implemented as separate components or groups of components for the various purposes. The set of components 400 may be coupled (e.g., communicatively; directly or indirectly) to various other circuits of the communication device 106.

For example, the communication device 106 may include various types of memory (e.g., including NAND flash 410) , an input/output interface such as connector I/F 420 (e.g., for connecting to a computer system; dock; charging station; input devices, such as a microphone, camera, keyboard; output devices, such as speakers; etc. ) , the display 460, which may be integrated with or external to the communication device 106, and cellular communication circuitry 430 such as for 5G NR, LTE, GSM, etc., and short to medium range wireless communication circuitry 429 (e.g., Bluetooth^TM and WLAN circuitry) . In some embodiments, communication device 106 may include wired communication circuitry (not shown) , such as a network interface card, e.g., for Ethernet.

The cellular communication circuitry 430 may couple (e.g., communicatively; directly or indirectly) to one or more antennas, such as antennas 435 and 436 as shown. The short to medium range wireless communication circuitry 429 may also couple (e.g., communicatively; directly or indirectly) to one or more antennas, such as antennas 437 and 438 as shown. Alternatively, the short to medium range wireless communication circuitry 429 may couple (e.g., communicatively; directly or indirectly) to the antennas 435 and 436 in addition to, or instead of, coupling (e.g., communicatively; directly or indirectly) to the antennas 437 and 438. The short to medium range wireless communication circuitry 429 and/or cellular communication circuitry 430 may include multiple receive chains and/or multiple transmit chains for receiving and/or transmitting multiple spatial streams, such as in a multiple-input multiple output (MIMO) configuration.

In some embodiments, as further described below, cellular communication circuitry 430 may include dedicated receive chains (including and/or coupled to, e.g., communicatively; directly or indirectly. dedicated processors and/or radios) for multiple RATs (e.g., a first receive chain for LTE and a second receive chain for 5G NR) . In addition, in some embodiments, cellular communication circuitry 430 may include a single transmit chain that may be switched between radios dedicated to specific RATs. For example, a first radio may be dedicated to a first RAT, e.g., LTE, and may be in communication with a dedicated receive chain and a transmit chain shared with an additional radio, e.g., a second radio that may be dedicated to a second RAT, e.g., 5G NR, and may be in communication with a dedicated receive chain and the shared transmit chain.

The communication device 106 may also include and/or be configured for use with one or more user interface elements. The user interface elements may include any of various elements, such as display 460 (which may be a touchscreen display) , a keyboard (which may be a discrete keyboard or may be implemented as part of a touchscreen display) , a mouse, a microphone and/or speakers, one or more cameras, one or more buttons, and/or any of various other elements capable of providing information to a user and/or receiving or interpreting user input.

The communication device 106 may further include one or more smart cards 445 that include SIM (Subscriber Identity Module) functionality, such as one or more UICC (s) (Universal Integrated Circuit Card (s) ) cards 445. Note that the term “SIM” or “SIM entity” is intended to include any of various types of SIM implementations or SIM functionality, such as the one or more UICC (s) cards 445, one or more eUICCs, one or more eSIMs, either removable or embedded, etc. In some embodiments, the UE 106 may include at least two SIMs. Each SIM may execute one or more SIM applications and/or otherwise implement SIM functionality. Thus, each SIM may be a single smart card that may be embedded, e.g., may be soldered onto a circuit board in the UE 106, or each SIM 410 may be implemented as a removable smart card. Thus, the SIM (s) may be one or more removable smart cards (such as UICC cards, which are sometimes referred to as “SIM cards” ) , and/or the SIMs 410 may be one or more embedded cards (such as embedded UICCs (eUICCs) , which are sometimes referred to as “eSIMs” or “eSIM cards” ) . In some embodiments (such as when the SIM (s) include an eUICC) , one or more of the SIM (s) may implement embedded SIM (eSIM) functionality; in such an embodiment, a single one of the SIM (s) may execute multiple SIM applications. Each of the SIMs may include components such as a processor and/or a memory; instructions for performing SIM/eSIM functionality may be stored in the memory and executed by the processor. In some embodiments, the UE 106 may include a combination of removable smart cards and fixed/non-removable smart cards (such as one or more eUICC cards that implement eSIM functionality) , as desired. For example, the UE 106 may comprise two embedded SIMs, two removable SIMs, or a combination of one embedded SIMs and one removable SIMs. Various other SIM configurations are also contemplated.

As noted above, in some embodiments, the UE 106 may include two or more SIMs. The inclusion of two or more SIMs in the UE 106 may allow the UE 106 to support two different telephone numbers and may allow the UE 106 to communicate on corresponding two or more respective networks. For example, a first SIM may support a first RAT such as LTE, and a second SIM 410 supports a second RAT such as 5G NR. Other implementations and RATs are of course possible. In some embodiments, when the UE 106 comprises two SIMs, the UE 106 may support Dual SIM Dual Active (DSDA) functionality. The DSDA functionality may allow the UE 106 to be simultaneously connected to two networks (and use two different RATs) at the same time, or to simultaneously maintain two connections supported by two different SIMs using the same or different RATs on the same or different networks. The DSDA functionality may also allow the UE 106 to simultaneously receive voice calls or data traffic on either phone number. In certain embodiments the voice call may be a packet switched communication. In other words, the voice call may be received using voice over LTE (VoLTE) technology and/or voice over NR (VoNR) technology. In some embodiments, the UE 106 may support Dual SIM Dual Standby (DSDS) functionality. The DSDS functionality may allow either of the two SIMs in the UE 106 to be on standby waiting for a voice call and/or data connection. In DSDS, when a call/data is established on one SIM, the other SIM is no longer active. In some embodiments, DSDx functionality (either DSDA or DSDS functionality) may be implemented with a single SIM (e.g., a eUICC) that executes multiple SIM applications for different carriers and/or RATs.

As shown, the SOC 400 may include processor (s) 402, which may execute program instructions for the communication device 106 and display circuitry 404, which may perform graphics processing and provide display signals to the display 460. The processor (s) 402 may also be coupled to memory management unit (MMU) 440, which may be configured to receive addresses from the processor (s) 402 and translate those addresses to locations in memory (e.g., memory 406, read only memory (ROM) 450, NAND flash memory 410) and/or to other circuits or devices, such as the display circuitry 404, short to medium range wireless communication circuitry 429, cellular communication circuitry 430, connector I/F 420, and/or display 460. The MMU 440 may be configured to perform memory protection and page table translation or set up. In some embodiments, the MMU 440 may be included as a portion of the processor (s) 402.

As described herein, the communication device 106 may include hardware and software components for implementing the above features for a communication device 106 to communicate a scheduling profile for power savings to a network. The processor 402 of the communication device 106 may be configured to implement part or all of the features described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium) . Alternatively (or in addition) , processor 402 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array) , or as an ASIC (Application Specific Integrated Circuit) . Alternatively (or in addition) the processor 402 of the communication device 106, in conjunction with one or more of the other components 400, 404, 406, 410, 420, 429, 430, 440, 445, 450, 460 may be configured to implement part or all of the features described herein.

In addition, as described herein, processor 402 may include one or more processing elements. Thus, processor 402 may include one or more integrated circuits (ICs) that are configured to perform the functions of processor 402. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc. ) configured to perform the functions of processor (s) 402.

Further, as described herein, cellular communication circuitry 430 and short to medium range wireless communication circuitry 429 may each include one or more processing elements. In other words, one or more processing elements may be included in cellular communication circuitry 430 and, similarly, one or more processing elements may be included in short to medium range wireless communication circuitry 429. Thus, cellular communication circuitry 430 may include one or more integrated circuits (ICs) that are configured to perform the functions of cellular communication circuitry 430. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc. ) configured to perform the functions of cellular communication circuitry 430. Similarly, the short to medium range wireless communication circuitry 429 may include one or more ICs that are configured to perform the functions of short to medium range wireless communication circuitry 429. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc. ) configured to perform the functions of short to medium range wireless communication circuitry 429.

FIG. 5: Block Diagram of Cellular Communication Circuitry

FIG. 5 illustrates an example simplified block diagram of cellular communication circuitry, according to some embodiments. It is noted that the block diagram of the cellular communication circuitry of FIG. 5 is only one example of a possible cellular communication circuit. According to embodiments, cellular communication circuitry 530, which may be cellular communication circuitry 430, may be included in a communication device, such as communication device 106 described above. As noted above, communication device 106 may be a user equipment (UE) device, a mobile device or mobile station, a wireless device or wireless station, a desktop computer or computing device, a mobile computing device (e.g., a laptop, notebook, or portable computing device) , a tablet and/or a combination of devices, among other devices.

The cellular communication circuitry 530 may couple (e.g., communicatively; directly or indirectly) to one or more antennas, such as antennas 435a-b and 436 as shown (in FIG. 4) . In some embodiments, cellular communication circuitry 530 may include dedicated receive chains (including and/or coupled to, e.g., communicatively; directly or indirectly. dedicated processors and/or radios) for multiple RATs (e.g., a first receive chain for LTE and a second receive chain for 5G NR) . For example, as shown in FIG. 5, cellular communication circuitry 530 may include a modem 510 and a modem 520. Modem 510 may be configured for communications according to a first RAT, e.g., such as LTE or LTE-A, and modem 520 may be configured for communications according to a second RAT, e.g., such as 5G NR.

As shown, modem 510 may include one or more processors 512 and a memory 516 in communication with processors 512. Modem 510 may be in communication with a radio frequency (RF) front end 535. RF front end 535 may include circuitry for transmitting and receiving radio signals. For example, RF front end 535 may include receive circuitry (RX) 532 and transmit circuitry (TX) 534. In some embodiments, receive circuitry 532 may be in communication with downlink (DL) front end 550, which may include circuitry for receiving radio signals via antenna 335a.

Similarly, modem 520 may include one or more processors 522 and a memory 526 in communication with processors 522. Modem 520 may be in communication with an RF front end 540. RF front end 540 may include circuitry for transmitting and receiving radio signals. For example, RF front end 540 may include receive circuitry 542 and transmit circuitry 544. In some embodiments, receive circuitry 542 may be in communication with DL front end 560, which may include circuitry for receiving radio signals via antenna 335b.

In some embodiments, a switch 570 may couple transmit circuitry 534 to uplink (UL) front end 572. In addition, switch 570 may couple transmit circuitry 544 to UL front end 572. UL front end 572 may include circuitry for transmitting radio signals via antenna 336. Thus, when cellular communication circuitry 530 receives instructions to transmit according to the first RAT (e.g., as supported via modem 510) , switch 570 may be switched to a first state that allows modem 510 to transmit signals according to the first RAT (e.g., via a transmit chain that includes transmit circuitry 534 and UL front end 572) . Similarly, when cellular communication circuitry 530 receives instructions to transmit according to the second RAT (e.g., as supported via modem 520) , switch 570 may be switched to a second state that allows modem 520 to transmit signals according to the second RAT (e.g., via a transmit chain that includes transmit circuitry 544 and UL front end 572) .

As described herein, the modem 510 may include hardware and software components for implementing the above features or for time division multiplexing UL data for NSA NR operations, as well as the various other techniques described herein. The processors 512 may be configured to implement part or all of the features described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium) . Alternatively (or in addition) , processor 512 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array) , or as an ASIC (Application Specific Integrated Circuit) . Alternatively (or in addition) the processor 512, in conjunction with one or more of the other components 530, 532, 534, 535, 550, 570, 572, 335a, 335b, and 336 may be configured to implement part or all of the features described herein.

In addition, as described herein, processors 512 may include one or more processing elements. Thus, processors 512 may include one or more integrated circuits (ICs) that are configured to perform the functions of processors 512. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc. ) configured to perform the functions of processors 512.

The processors 522 may be configured to implement part or all of the features described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium) . Alternatively (or in addition) , processor 522 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array) , or as an ASIC (Application Specific Integrated Circuit) . Alternatively (or in addition) the processor 522, in conjunction with one or more of the other components 540, 542, 544, 550, 570, 572, 335a, 335b, and 336 may be configured to implement part or all of the features described herein.

In addition, as described herein, processors 522 may include one or more processing elements. Thus, processors 522 may include one or more integrated circuits (ICs) that are configured to perform the functions of processors 522. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc. ) configured to perform the functions of processors 522.

FIG. 6: Block Diagram of a Baseband Processor Architecture for a UE

FIG. 6 illustrates example components of a device 600 in accordance with some embodiments. It is noted that the device of FIG. 6 is merely one example of a possible system, and that features of this disclosure may be implemented in any of various UEs, as desired.

In some embodiments, the device 600 may include application circuitry 602, baseband circuitry 604, Radio Frequency (RF) circuitry 606, front-end module (FEM) circuitry 608, one or more antennas 610, and power management circuitry (PMC) 612 coupled together at least as shown. The components of the illustrated device 600 may be included in a UE 106 or a RAN node 102A. In some embodiments, the device 600 may include less elements (e.g., a RAN node may not utilize application circuitry 602, and instead include a processor/controller to process IP data received from an EPC) . In some embodiments, the device 600 may include additional elements such as, for example, memory/storage, display, camera, sensor, or input/output (I/O) interface. In other embodiments, the components described below may be included in more than one device (e.g., said circuitries may be separately included in more than one device for Cloud-RAN (C-RAN) implementations) .

The application circuitry 602 may include one or more application processors. For example, the application circuitry 602 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. The processor (s) may include any combination of general-purpose processors and dedicated processors (e.g., graphics processors, application processors, etc. ) . The processors may be coupled with or may include memory/storage and may be configured to execute instructions stored in the memory/storage to enable various applications or operating systems to run on the device 600. In some embodiments, processors of application circuitry 602 may process IP data packets received from an EPC.

The baseband circuitry 604 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. The baseband circuitry 604 may include one or more baseband processors or control logic to process baseband signals received from a receive signal path of the RF circuitry 606 and to generate baseband signals for a transmit signal path of the RF circuitry 606. Baseband processing circuity 604 may interface with the application circuitry 602 for generation and processing of the baseband signals and for controlling operations of the RF circuitry 606. For example, in some embodiments, the baseband circuitry 604 may include a third generation (3G) baseband processor 604A, a fourth generation (4G) baseband processor 604B, a fifth generation (5G) baseband processor 604C, or other baseband processor (s) 604D for other existing generations, generations in development or to be developed in the future (e.g., second generation (2G) , sixth generation (6G) , etc. ) . The baseband circuitry 604 (e.g., one or more of baseband processors 604A-D) may handle various radio control functions that enable communication with one or more radio networks via the RF circuitry 606. In other embodiments, some or all of the functionality of baseband processors 604A-D may be included in modules stored in the memory 604G and executed via a Central Processing Unit (CPU) 604E. The radio control functions may include, but are not limited to, signal modulation/demodulation, encoding/decoding, radio frequency shifting, etc. In some embodiments, modulation/demodulation circuitry of the baseband circuitry 604 may include Fast-Fourier Transform (FFT) , precoding, or constellation mapping/demapping functionality. In some embodiments, encoding/decoding circuitry of the baseband circuitry 604 may include convolution, tail-biting convolution, turbo, Viterbi, or Low Density Parity Check (LDPC) encoder/decoder functionality. Embodiments of modulation/demodulation and encoder/decoder functionality are not limited to these examples and may include other suitable functionality in other embodiments.

In some embodiments, the baseband circuitry 604 may include one or more audio digital signal processor (s) (DSP) 604F. The audio DSP (s) 604F may be include elements for compression/decompression and echo cancellation and may include other suitable processing elements in other embodiments. Components of the baseband circuitry may be suitably combined in a single chip, a single chipset, or disposed on a same circuit board in some embodiments. In some embodiments, some or all of the constituent components of the baseband circuitry 604 and the application circuitry 602 may be implemented together such as, for example, on a system on a chip (SOC) .

In some embodiments, the baseband circuitry 604 may provide for communication compatible with one or more radio technologies. For example, in some embodiments, the baseband circuitry 604 may support communication with an evolved universal terrestrial radio access network (EUTRAN) or other wireless metropolitan area networks (WMAN) , a wireless local area network (WLAN) , a wireless personal area network (WPAN) . Embodiments in which the baseband circuitry 604 is configured to support radio communications of more than one wireless protocol may be referred to as multi-mode baseband circuitry.

RF circuitry 606 may enable communication with wireless networks using modulated electromagnetic radiation through a non-solid medium. In various embodiments, the RF circuitry 606 may include switches, filters, amplifiers, etc. to facilitate the communication with the wireless network. RF circuitry 606 may include a receive signal path which may include circuitry to down-convert RF signals received from the FEM circuitry 608 and provide baseband signals to the baseband circuitry 604. RF circuitry 606 may also include a transmit signal path which may include circuitry to up-convert baseband signals provided by the baseband circuitry 604 and provide RF output signals to the FEM circuitry 608 for transmission.

In some embodiments, the receive signal path of the RF circuitry 606 may include mixer circuitry 606a, amplifier circuitry 606b and filter circuitry 606c. In some embodiments, the transmit signal path of the RF circuitry 606 may include filter circuitry 606c and mixer circuitry 606a. RF circuitry 606 may also include synthesizer circuitry 606d for synthesizing a frequency for use by the mixer circuitry 606a of the receive signal path and the transmit signal path. In some embodiments, the mixer circuitry 606a of the receive signal path may be configured to down-convert RF signals received from the FEM circuitry 608 based on the synthesized frequency provided by synthesizer circuitry 606d. The amplifier circuitry 606b may be configured to amplify the down-converted signals and the filter circuitry 606c may be a low-pass filter (LPF) or band-pass filter (BPF) configured to remove unwanted signals from the down-converted signals to generate output baseband signals. Output baseband signals may be provided to the baseband circuitry 604 for further processing. In some embodiments, the output baseband signals may be zero-frequency baseband signals, although this is not a necessity. In some embodiments, mixer circuitry 606a of the receive signal path may comprise passive mixers, although the scope of the embodiments is not limited in this respect.

In some embodiments, the mixer circuitry 606a of the transmit signal path may be configured to up-convert input baseband signals based on the synthesized frequency provided by the synthesizer circuitry 606d to generate RF output signals for the FEM circuitry 608. The baseband signals may be provided by the baseband circuitry 604 and may be filtered by filter circuitry 606c.

In some embodiments, the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a of the transmit signal path may include two or more mixers and may be arranged for quadrature downconversion and upconversion, respectively. In some embodiments, the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a of the transmit signal path may include two or more mixers and may be arranged for image rejection (e.g., Hartley image rejection) . In some embodiments, the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a may be arranged for direct downconversion and direct upconversion, respectively. In some embodiments, the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a of the transmit signal path may be configured for super-heterodyne operation.

In some embodiments, the output baseband signals, and the input baseband signals may be analog baseband signals, although the scope of the embodiments is not limited in this respect. In some alternate embodiments, the output baseband signals, and the input baseband signals may be digital baseband signals. In these alternate embodiments, the RF circuitry 606 may include analog-to-digital converter (ADC) and digital-to-analog converter (DAC) circuitry and the baseband circuitry 604 may include a digital baseband interface to communicate with the RF circuitry 606.

In some dual-mode embodiments, a separate radio IC circuitry may be provided for processing signals for each spectrum, although the scope of the embodiments is not limited in this respect.

In some embodiments, the synthesizer circuitry 606d may be a fractional-N synthesizer or a fractional N/N+1 synthesizer, although the scope of the embodiments is not limited in this respect as other types of frequency synthesizers may be suitable. For example, synthesizer circuitry 606d may be a delta-sigma synthesizer, a frequency multiplier, or a synthesizer comprising a phase-locked loop with a frequency divider.

The synthesizer circuitry 606d may be configured to synthesize an output frequency for use by the mixer circuitry 606a of the RF circuitry 606 based on a frequency input and a divider control input. In some embodiments, the synthesizer circuitry 606d may be a fractional N/N+1 synthesizer.

In some embodiments, frequency input may be provided by a voltage controlled oscillator (VCO) , although that is not a necessity. Divider control input may be provided by either the baseband circuitry 604 or the applications processor 602 depending on the desired output frequency. In some embodiments, a divider control input (e.g., N) may be determined from a look-up table based on a channel indicated by the applications processor 602.

Synthesizer circuitry 606d of the RF circuitry 606 may include a divider, a delay-locked loop (DLL) , a multiplexer and a phase accumulator. In some embodiments, the divider may be a dual modulus divider (DMD) and the phase accumulator may be a digital phase accumulator (DPA) . In some embodiments, the DMD may be configured to divide the input signal by either N or N+1 (e.g., based on a carry out) to provide a fractional division ratio. In some example embodiments, the DLL may include a set of cascaded, tunable, delay elements, a phase detector, a charge pump and a D-type flip-flop. In these embodiments, the delay elements may be configured to break a VCO period up into Nd equal packets of phase, where Nd is the number of delay elements in the delay line. In this way, the DLL provides negative feedback to help ensure that the total delay through the delay line is one VCO cycle.

In some embodiments, synthesizer circuitry 606d may be configured to generate a carrier frequency as the output frequency, while in other embodiments, the output frequency may be a multiple of the carrier frequency (e.g., twice the carrier frequency, four times the carrier frequency) and used in conjunction with quadrature generator and divider circuitry to generate multiple signals at the carrier frequency with multiple different phases with respect to each other. In some embodiments, the output frequency may be a LO frequency (fLO) . In some embodiments, the RF circuitry 606 may include an IQ/polar converter.

FEM circuitry 608 may include a receive signal path which may include circuitry configured to operate on RF signals received from one or more antennas 610, amplify the received signals and provide the amplified versions of the received signals to the RF circuitry 606 for further processing. FEM circuitry 608 may also include a transmit signal path which may include circuitry configured to amplify signals for transmission provided by the RF circuitry 606 for transmission by one or more of the one or more antennas 610. In various embodiments, the amplification through the transmit or receive signal paths may be done solely in the RF circuitry 606, solely in the FEM 608, or in both the RF circuitry 606 and the FEM 608.

In some embodiments, the FEM circuitry 608 may include a TX/RX switch to switch between transmit mode and receive mode operation. The FEM circuitry may include a receive signal path and a transmit signal path. The receive signal path of the FEM circuitry may include an LNA to amplify received RF signals and provide the amplified received RF signals as an output (e.g., to the RF circuitry 606) . The transmit signal path of the FEM circuitry 608 may include a power amplifier (PA) to amplify input RF signals (e.g., provided by RF circuitry 606) , and one or more filters to generate RF signals for subsequent transmission (e.g., by one or more of the one or more antennas 610) .

In some embodiments, the PMC 612 may manage power provided to the baseband circuitry 604. In particular, the PMC 612 may control power-source selection, voltage scaling, battery charging, or DC-to-DC conversion. The PMC 612 may often be included when the device 600 is capable of being powered by a battery, for example, when the device is included in a UE. The PMC 612 may increase the power conversion efficiency while providing desirable implementation size and heat dissipation characteristics.

While FIG. 6 shows the PMC 612 coupled only with the baseband circuitry 604, in other embodiments the PMC 612 may be additionally or alternatively coupled with, and perform similar power management operations for, other components such as, but not limited to, application circuitry 602, RF circuitry 606, or FEM 608.

In some embodiments, the PMC 612 may control, or otherwise be part of, various power saving mechanisms of the device 600. For example, if the device 600 is in a radio resource control_Connected (RRC_Connected) state, where it is still connected to the RAN node as it expects to receive traffic shortly, then it may enter a state known as Discontinuous Reception Mode (DRX) after a period of inactivity. During this state, the device 600 may power down for brief intervals of time and thus save power.

If there is no data traffic activity for an extended period of time, then the device 600 may transition off to an RRC_Idle state, where it disconnects from the network and does not perform operations such as channel quality feedback, handover, etc. The device 600 goes into a very low power state and it performs paging where again it periodically wakes up to listen to the network and then powers down again. The device 600 may not receive data in this state, in order to receive data, it will transition back to RRC_Connected state.

An additional power saving mode may allow a device to be unavailable to the network for periods longer than a paging interval (ranging from seconds to a few hours) . During this time, the device is totally unreachable to the network and may power down completely. Any data sent during this time incurs a large delay and it is assumed the delay is acceptable.

Processors of the application circuitry 602 and processors of the baseband circuitry 604 may be used to execute elements of one or more instances of a protocol stack. For example, processors of the baseband circuitry 604, alone or in combination, may be used execute Layer 3, Layer 2, or Layer 1 functionality, while processors of the application circuitry 604 may utilize data (e.g., packet data) received from these layers and further execute Layer 4 functionality (e.g., transmission communication protocol (TCP) and user datagram protocol (UDP) layers) . As referred to herein, Layer 3 (L3) may comprise a radio resource control (RRC) layer, described in further detail below. As referred to herein, Layer 2 (L2) may comprise a medium access control (MAC) layer, a radio link control (RLC) layer, and a packet data convergence protocol (PDCP) layer, described in further detail below. As referred to herein, Layer 1 (L1) may comprise a physical (PHY) layer of a UE/RAN node, described in further detail below. Accordingly, the baseband circuitry 604 can be used to encode a message for transmission between a UE and a base station, or decode a message received between a UE and a base station.

FIG. 7: Block Diagram of an Interface of Baseband Circuitry

FIG. 7 illustrates example interfaces of baseband circuitry in accordance with some embodiments. It is noted that the baseband circuitry of FIG. 7 is merely one example of a possible circuitry, and that features of this disclosure may be implemented in any of various systems, as desired.

As discussed above, the baseband circuitry 604 of FIG. 6 may comprise processors 604A-604E and a memory 604G utilized by said processors. Each of the processors 604A-604E may include a memory interface, 704A-704E, respectively, to send/receive data to/from the memory 604G.

The baseband circuitry 604 may further include one or more interfaces to communicatively couple to other circuitries/devices, such as a memory interface 712 (e.g., an interface to send/receive data to/from memory external to the baseband circuitry 604) , an application circuitry interface 714 (e.g., an interface to send/receive data to/from the application circuitry 602 of FIG. 6) , an RF circuitry interface 716 (e.g., an interface to send/receive data to/from RF circuitry 606 of FIG. 6) , a wireless hardware connectivity interface 718 (e.g., an interface to send/receive data to/from Near Field Communication (NFC) components, components (e.g., Low Energy) , components, and other communication components) , and a power management interface 720 (e.g., an interface to send/receive power or control signals to/from the PMC 612.

FIG. 8: Control Plane Protocol Stack

FIG. 8 is an illustration of a control plane protocol stack in accordance with some embodiments. In this embodiment, a control plane 800 is shown as a communications protocol stack between the UE 106a (or alternatively, the UE 106b) , the RAN node 102A (or alternatively, the RAN node 102B) , and the mobility management entity (MME) 621.

The PHY layer 801 may transmit or receive information used by the MAC layer 802 over one or more air interfaces. The PHY layer 801 may further perform link adaptation or adaptive modulation and coding (AMC) , power control, cell search (e.g., for initial synchronization and handover purposes) , and other measurements used by higher layers, such as the RRC layer 805. The PHY layer 801 may still further perform error detection on the transport channels, forward error correction (FEC) coding/decoding of the transport channels, modulation/demodulation of physical channels, interleaving, rate matching, mapping onto physical channels, and Multiple Input Multiple Output (MIMO) antenna processing.

The MAC layer 802 may perform mapping between logical channels and transport channels, multiplexing of MAC service data units (SDUs) from one or more logical channels onto transport blocks (TB) to be delivered to PHY via transport channels, de-multiplexing MAC SDUs to one or more logical channels from transport blocks (TB) delivered from the PHY via transport channels, multiplexing MAC SDUs onto TBs, scheduling information reporting, error correction through hybrid automatic repeat request (HARQ) , and logical channel prioritization.

The RLC layer 803 may operate in a plurality of modes of operation, including: Transparent Mode (TM) , Unacknowledged Mode (UM) , and Acknowledged Mode (AM) . The RLC layer 803 may execute transfer of upper layer protocol data units (PDUs) , error correction through automatic repeat request (ARQ) for AM data transfers, and concatenation, segmentation and reassembly of RLC SDUs for UM and AM data transfers. The RLC layer 803 may also execute re-segmentation of RLC data PDUs for AM data transfers, reorder RLC data PDUs for UM and AM data transfers, detect duplicate data for UM and AM data transfers, discard RLC SDUs for UM and AM data transfers, detect protocol errors for AM data transfers, and perform RLC re-establishment.

The PDCP layer 804 may execute header compression and decompression of IP data, maintain PDCP Sequence Numbers (SNs) , perform in-sequence delivery of upper layer PDUs at re-establishment of lower layers, eliminate duplicates of lower layer SDUs at re-establishment of lower layers for radio bearers mapped on RLC AM, cipher and decipher control plane data, perform integrity protection and integrity verification of control plane data, control timer-based discard of data, and perform security operations (e.g., ciphering, deciphering, integrity protection, integrity verification, etc. ) .

The main services and functions of the RRC layer 805 may include broadcast of system information (e.g., included in Master Information Blocks (MIBs) or System Information Blocks (SIBs) related to the non-access stratum (NAS) ) , broadcast of system information related to the access stratum (AS) , paging, establishment, maintenance and release of an RRC connection between the UE and E-UTRAN (e.g., RRC connection paging, RRC connection establishment, RRC connection modification, and RRC connection release) , establishment, configuration, maintenance and release of point to point Radio Bearers, security functions including key management, inter radio access technology (RAT) mobility, and measurement configuration for UE measurement reporting. Said MIBs and SIBs may comprise one or more information elements (IEs) , which may each comprise individual data fields or data structures.

The UE 601 and the RAN node 102A may utilize a Uu interface (e.g., an LTE-Uu interface) to exchange control plane data via a protocol stack comprising the PHY layer 801, the MAC layer 802, the RLC layer 803, the PDCP layer 804, and the RRC layer 805.

The non-access stratum (NAS) protocols 806 form the highest stratum of the control plane between the UE 601 and the MME 621. The NAS protocols 806 support the mobility of the UE 601 and the session management procedures to establish and maintain IP connectivity between the UE 601 and the P-GW 623.

The S1 Application Protocol (S1-AP) layer 815 may support the functions of the S1 interface and comprise Elementary Procedures (EPs) . An EP is a unit of interaction between the RAN node 102A and the CN 100. The S1-AP layer services may comprise two groups: UE-associated services and non UE-associated services. These services perform functions including, but not limited to: E-UTRAN Radio Access Bearer (E-RAB) management, UE capability indication, mobility, NAS signaling transport, RAN Information Management (RIM) , and configuration transfer.

The Stream Control Transmission Protocol (SCTP) layer (alternatively referred to as the SCTP/IP layer) 814 may ensure reliable delivery of signaling messages between the RAN node 102A and the MME 621 based, in part, on the IP protocol, supported by the IP layer 813. The L2 layer 812 and the L1 layer 811 may refer to communication links (e.g., wired or wireless) used by the RAN node and the MME to exchange information.

The RAN node 102A and the MME 621 may utilize an S1-MME interface to exchange control plane data via a protocol stack comprising the L1 layer 811, the L2 layer 812, the IP layer 813, the SCTP layer 814, and the S1-AP layer 815.

AI/ML Based Handover Failure and Radio Link Failure Prediction

Wireless communication systems provide mobility by enabling user equipment (UEs) to move between cells via a process referred to as handover. Handover occurs when a mobile UE switches from one cell to another neighboring cell. Mechanisms have been established to help ensure a smooth transition between cells. NR supports different types of handover that were not supported in the previous 4G LTE specification. The basic handover in NR has been based on LTE handover mechanisms in which the network controls UE mobility based on UE measurement reporting. This measurement reporting typically involves Layer 3 (L3) measurements of neighbor cells and reporting from the UE to the eNB.

It should be noted that 5G NR enables various advanced capabilities as compared to LTE, and one existing procedure that can benefit from enhancement leveraging these 5G technologies is the radio link failure (RLF) mechanism. RLF refers to cases where the radio link quality deteriorates below certain thresholds such that communication between a user equipment (UE) and serving base station is disrupted. The current RLF procedure has some limitations in that it reacts to failures only after they have already occurred, rather than proactively avoiding them. The procedure also relies on a limited set of reference signal measurements that may not fully capture emerging radio link problems. Additionally, downlink signals and uplink signals are assessed independently even though they are often correlated in indicating radio link conditions.

These gaps present use cases where advanced algorithms like artificial intelligence (AI) and machine learning (ML) , coupled with coordination between the UE and next generation NodeB (base station) , can provide more predictive identification of risk of impending radio link failures. By intelligently fusing multiple radio link indicators and legacy measurements, failures can potentially be predicted ahead of time using AI/ML, allowing mitigating actions like handovers to prevent deterioration rather than simply reacting to RLF events. Enabling such predictive failure management can further improve reliability mechanisms as 5G networks continue to advance.

FIG. 9: Timing Diagram for enabling network-side artificial intelligence (AI) based model measurement prediction.

In cellular systems, AI-based mobility involves a network using artificial intelligence (AI) /machine learning (ML) models to predict and optimize handover decisions, considering factors such as cell-level measurements, inter-cell beam-level measurements, and potential handover failures. AI/ML-aided mobility for network-triggered layer 3 (L3) -based handover offers potential benefits and gains, particularly in aspects like AI/ML-based RRM measurement and event inference (e.g. prediction) .

AI-based mobility considers cell-level measurement prediction (intra and inter-frequency) , inter-cell beam-level measurement prediction for L3 mobility, HO failure/Radio Link Failure (RLF) prediction, and measurement events prediction. The evaluation of AI-based mobility should consider HO performance Key Performance Indicators (KPIs) (e.g., ping-pong HO, HO Failure (HOF) /RLF, Time of stay, Handover interruption, prediction accuracy, and measurement reduction) and complexity tradeoffs. Potential AI mobility-specific enhancements should be based on a general framework for AI/ML in the air interface.

Thus, the focus of the illustrated embodiments, as described herein, is on network-sided AI/ML model training for mobility. Network-sided measurement and event prediction can be trained in the network using the existing measurement framework without any enhancements in the standard. However, a problem arises when the network may configure the User Equipment (UE) to perform more measurements than needed during the training phase, raising concerns about user acceptance and participation in network-based training. Thus, a need exists to ensure that the UE participates in network-based training only when and if acceptable by the user.

The mechanisms of the illustrated embodiments provide for dedicated measurement events setup by the network for training. In one example, the illustrated embodiments define dedicated measurement events exclusively for network-sided AI/ML model training. These new measurement events are used exclusively for network-sided model training purposes. That is, the events are intended to be used exclusively for training purposes and not for other tasks such as, for example, mobility management. To ensure the proper usage of these dedicated measurement events, a standard (e.g., standard operational protocol) should prohibit the network 1020 from using any other measurement events (e.g., pre-existing or measurement events) for the purpose of AI/ML model training. This restriction would help maintain a clear separation between the measurements used for training and those used for other network functions. Alternatively, the network may be allowed to use pre-existing or other measurement events measurements for training, as long as the network doesn’ t schedule such measurements solely for training purposes.

In another example, a new optional capability may be defined for these dedicated network AI/ML training measurements, and the network is only allowed to configure the dedicated network AI/ML model training measurements if user consent (e.g., via a user’s UE) has been provided.

Thus, new dedicated training measurement events are specifically designed for network-sided AI/ML model training. These new dedicated training measurement events are to be used for collecting measurements for training purposes of network based AI/ML models. However, apart from these new dedicated events, the existing or legacy measurement framework, which is already used for mobility management and other network functions, will continue to operate as usual. In other words, the introduction of the new dedicated training measurement events will not replace or disrupt the existing measurement framework.

The UE may treat measurement objects used for network training differently than those used for mobility, and the details may be left for UE implementation. Said differently, when the UE is configured with measurement objects for network-sided AI/ML model training, the UE may handle these measurement objects differently compared to the measurement objects used for mobility purposes. The specific details of how the UE treats these training-related measurement objects can be left to the UE's implementation, providing flexibility for different UE manufacturers or models.

The UE may be permitted to skip some measurements related to network training. For example, if the UE's battery level is low, the UE may prioritize essential functions and choose not to perform certain training-related measurements to conserve power. This flexibility ensures that the UE can manage its resources effectively while still participating in network training when possible. Furthermore, the dedicated training measurements associated with network training may have relaxed performance requirements compared to those used for mobility. In some cases, there may be no strict performance requirements or no requirements at all for training-related measurements.

To differentiate between the measurement events used for network-sided AI model training and those used for other purposes, it may be implicitly assumed that the events defined are used exclusively for training, without the need for explicit signaling. Alternatively, a flag or indicator can be included in the measurement configuration to explicitly mark the dedicated training measurements as being used for network-sided AI model training purposes. This flag would provide a clear distinction between training-related events and other measurement events.

Moreover, the mechanisms of the illustrated embodiments provide the specific measurement events that can be used for network-sided AI/ML model training. In one example, there may be two sets of events: existing events A1-A6 and the new dedicated training measurement events AT1-AT6.

The 3GPP legacy measurement events A1-A6 can be considered the most likely candidates for network-sided AI model training for mobility. These 3GPP legacy measurement events are already defined in the legacy 3GPP specification measurement framework (e.g., 3GPP Release 18) and are used for various purposes, such as determining when to trigger handovers or adjust network parameters. In one example, the specific conditions for the following legacy measurement events comprise:

- Event A1: Serving cell becomes better than a specified threshold.

- Event A2: Serving cell becomes worse than a specified threshold.

- Event A3: Neighbor cell becomes offset better than the serving cell (SpCell) .

- Event A4: Neighbor cell becomes better than a specified threshold.

- Event A5: Serving cell (SpCell) becomes worse than threshold1, and neighbor cell becomes better than threshold2.

- Event A6: Neighbor cell becomes offset better than the secondary cell (SCell) .

While these 3GPP legacy measurement events can be used for network-sided model training, the present solution proposes defining a new set of events specifically tailored for AI/ML model training purposes. These new dedicated training measurement events, referred to as AT1-AT6, mirror the existing 3GPP legacy measurement events A1-A6 but are designed to be used exclusively for network-sided AI/ML model training.

The new dedicated training measurement events AT1-AT6 are defined as follows:

- Event AT1: Serving cell becomes better than a specified threshold (for training) .

- Event AT2: Serving cell becomes worse than a specified threshold (for training) .

- Event AT3: Neighbor cell becomes offset better than the serving cell (SpCell) (for training) .

- Event AT4: Neighbor cell becomes better than a specified threshold (for training) .

- Event AT5: Serving cell (SpCell) becomes worse than threshold1, and neighbor cell becomes better than threshold2 (for training) .

- Event AT6: Neighbor cell becomes offset better than the secondary cell (SCell) (for training) .

In one example, the new dedicated training measurement events AT1-AT6 can be considered as a first set of new dedicated training measurement events AT1-AT6. By introducing these new dedicated training measurement events specifically for network-sided AI/ML model training, the mechanisms of the illustrated embodiments provide a dedicated set of measurements that can be used exclusively for training purposes. This separation allows for a clear distinction between measurements used for training and those used for other network functions, enabling more focused and efficient data collection for AI/ML model development and the capability to control the collection of data for network-sided AI/ML model training from the UE.

In another example, these new dedicated training measurement events are specifically designed to facilitate network-sided model training and provide the network with information about when measurements change above a configured threshold compared to the previously reported measurement.

Additional new dedicated training measurement events can be defined as:

- Event AT7: Serving cell becomes better than a specified threshold relative to the previous AT7 report.

- Event AT8: Serving cell becomes worse than a specified threshold relative to the previous AT8 report.

- Event AT9: Neighbor cell becomes offset better than the serving cell (SpCell) relative to the previous AT9 report.

- Event AT10: Neighbor cell becomes better than a specified threshold relative to the previous AT10 report.

The new dedicated training measurement events AT7-T10 can be considered as a second set of dedicated training measurement events These dedicated training measurement events (AT7-AT10) are designed to capture changes in measurements relative to the previously reported values. By comparing the current measurement to the measurement in a previous report, the network can identify significant changes or improvements in the serving cell or neighbor cells.

The dedicated training measurement events AT1-AT10, as disclosed herein, may have a separate capability and require user consent, which will be discussed in more detail later. This means that the UE needs to have a specific capability to support these events, and the user (e.g., the UE) can provide explicit consent for the network to configure and use these events for training purposes. That is, in one example, the network may only configure these training measurement events for a UE if user consent from the UE has been obtained.

Additionally, for these new dedicated training measurement events, an initial value (e.g., “event 0” ) may be configured by the network for these events. For example, the network may set an initial value of -50dBm. The first event report would then be relative to this initial value, providing a starting point for measuring changes in the signal strength.

Alternatively, when a new dedicated training measurement event is configured, the UE can immediately report it to the network, even if the triggering conditions for the event are not met. This approach ensures that the network receives an initial measurement report from the UE as soon as the event is configured, establishing a baseline for subsequent reports.

The mechanisms of the illustrated embodiments also provide for additional new dedicated training measurement events specifically designed for cell/beam level prediction in the context of network-sided AI/ML model training.

Two new dedicated training measurement events may also be defined as 1) EventX and 2) EventY. The new dedicated training measurement EventX may be triggered when the best cell changes and is associated with the same measurement object or frequency. In this context, the "best cell" may refer to a cell with the highest measured signal quality or strength among all cells associated with the same measurement object or frequency. When the best cell changes, meaning that a different cell becomes the strongest or highest-quality cell, EventX is triggered to inform the network about this change.

The new dedicated training measurement EventY may be triggered when the best beam changes, but the cell remains the same. That is, EventY is triggered when the beam with a highest measured signal quality or strength changes, but the change occurs within the same cell. This means that the UE remains connected to the same cell, but the best beam within that cell has changed.

The mechanisms of the illustrated embodiments also provide for adjusting the measurement periodicity based on the UE's velocity and/or mobility state. The measurement periodicity may refer to the frequency at which the UE performs and reports measurements to the network. By adapting the measurement periodicity according to the UE's mobility characteristics, the network can optimize the balance between measurement accuracy and resource efficiency.

Thus, a measurement periodicity rate for the UE may be adjusted for performing the new dedicated training measurements based the velocity state and/or the mobility state of the UE. The velocity state and the mobility state may include a high mobility state corresponding to a short measurement periodicity relative to a default measurement periodicity; a mid-mobility state corresponding to the default measurement periodicity; a low mobility state corresponding to a long measurement periodicity relative to the default measurement periodicity; and a stationary state corresponding to a longest measurement periodicity.

The UE can also be configured to perform joint periodic and event triggered measurement in which the UE provides measurements when an event is triggered or when a timer expires. In a variant of this solution, an event may be triggered if the delta compared to the previous measured quantity is larger than a threshold during a configured time period. The amount of the delta (the threshold) can depend on the speed/mobility state of the UE. The threshold is likely to be shorter in this variant.

In another example, the mechanisms of the illustrated embodiments also provide for a simplified approach where existing measurement events (e.g., A1-A6) can be re-used for training and there are no new dedicated measurement events defined (e.g. AT1 –AT10) . In terms of signaling, the only change is the addition of a flag to a measurement object indicating that it is used for network sided model training and not for mobility.

Additionally, the mechanisms of the illustrated embodiments provide for a new Self-Organizing Network (SON) report. By using the new SON report to gather measurements for network-sided AI/ML model training, the UE may report multiple measurements at once, thus resulting in reduced signaling overhead and power. An additional advantage is clear separation between measurements for mobility and measurements for network optimization. The SON report defined may either be implicitly assumed to be used exclusively for training or there may be a flag in the measurement configuration.

The SON report may also be referred to as the "measurements for training report, ” which is specifically defined to collect measurements for network-sided training for transmission from the UE to the network via the base station. When configured, the UE may store measurements for network-sided training in a new variable called "VarMeasTraining-Report. " To indicate the availability of the Measurements for Training Report to the network, the UE utilizes a standard SON mechanism.

A new availability indication Information Element (IE) may be defined as “UE-TrainingMeasurementsAvailable. ” The UE communicates its availability to the network by including the UE-TrainingMeasurementsAvailable IE in one or more of the following messages: RRCReestablishmentComplete, RRCReconfigurationComplete, RRCResumeComplete, or RRCSetupComplete.

The network retrieves this information using the established SON mechanism. The UEInformationRequest message is enhanced to include an indication that instructs the UE to provide measurements for training purposes. Similarly, the UEInformationResponse message is extended to carry the logged measurements for training.

By defining a dedicated SON report for training measurements and introducing a new variable to store these measurements, Solution 2 provides a structured approach to collecting and managing network-sided training data. The use of existing SON mechanisms, with enhancements to accommodate training-related indications and data transfer, ensures a seamless integration with the current network infrastructure.

In the various operations, as described herein, the same measurements and measurement events (e.g., AT1-AT10) may be leveraged and used in performing network-sided AI/ML training measurements at the UE for the SON report. However, there is a key difference in how the UE handles these measurements when an event occurs. For example, in a first solution, where the UE immediately reports the measurements upon an event occurrence, a second solution may enable the UE to simply store and log the measurements without instantly reporting them to the network.

Thus, in one embodiment, the VarMeasTraining-Report variable, which stores the logged measurements for training, may be a list of MeasResults. Consequently, the UEInformationResponse message can be used to transmit the logged measurements to the network and is enhanced to carry a list of MeasResults. Alternatively, another embodiment provides a MeasResultIdleNR Information Element (IE) can be used.

In another example, the mechanisms of the illustrated embodiments provide for an enhanced minimization of drive tests (MDT) report for gathering the new dedicated training measurement for training the one or more network-side AI based models and then sending the enhanced MDT report to the network. The enhanced MDT report includes the one or more new dedicated training measurements for training the one or more network-side AI based models.

In some embodiments, the enhanced MDT report may be sent to a Trace Collection Entity (TCE) via a Trace Activation next generation-application protocol (NG-AP) message when the training the one or more network-side AI based models is performed in an Operations, Administration, and Maintenance (OAM) domain. Also, the MDT report uses a LogMeasReport message that is in a UEInformationResponse message. In other embodiments, the enhanced MDT report defined in this example solution can either be implicitly assumed to be used exclusively for training or there may be a flag in the measurement configuration indicating so.

In some embodiments, the MDT uses Trace, which (on the network side) supports functionality of sending the MDT report to the TCE. This can be used if network sided model training is performed, not in the gNB, but in another network entity (e.g. in the OAM) . A Trace Activation NG-AP message may be enhanced to include a Trace Collection Entity IP Address and Trace Collection Entity uniform resource identifier (URI) and can be re-used if network-sided model training entity is the same as TCE. Alternatively, Trace Activation NG-AP message can be enhanced to include a model training entity (MTE) internet protocol (IP) address and an MTE URI. In either option, the measurements collected and reported by a UE for network sided training are forwarded to either TCE or Model Training Entity.

Again, it should be noted that the network may only be allowed to configure the UE to report measurements for network sided model training if the user provided consent. Such consent may be stored in the unified data management (UDM) functionality and provided by the Core Network to RAN -as in the current 5G user consent framework. If a more UE-friendly user consent framework is defined and developed, such framework may be enhanced to allow the UE to provide (or not) consent for the collection of data from the UE for the training of network sided AI/ML models.

For further explanation, FIG. 9 illustrates an example timing diagram signaling between a user equipment (UE) and a network (e.g., a base station) for enabling artificial intelligence (AI) based model measurement prediction at the network according to some embodiments. Also, FIG. 9 provides an example illustration of a UE 106 communicating with a base station 102. In various embodiments, some of the signaling shown may be performed concurrently, in a different order than shown, or may be omitted. Additional signaling may also be performed as desired. As shown, this signaling may flow as follows as one example embodiment.

The signaling shown in FIG. 9 may be used in conjunction with any of the systems, methods, and/or devices. In various embodiments, some of the signaling shown may be performed concurrently, in a different order than shown, or may be omitted. Additional signaling may also be performed as desired. As shown, this signaling may flow as follows as one example embodiment.

The signaling may begin with a network (e.g., base station 102) transmitting 902, to a UE, such as UE 106, configuration information for dedicated training measurement events, where the dedicated training measurement events correspond to a set of defined measurement events used by the network, and the dedicated training measurement events are used exclusively for training one or more network-side AI based models (e.g., the AI-based model 920) .

In one aspect, the dedicated training measurement events include a first set of events that corresponds to the set of defined measurement events. In another example, the dedicated training measurement events are associated with the UE having capability to support the dedicated training measurement events and require consent of the UE prior to configuring the UE to perform the dedicated training measurement events. In one example, the dedicated training measurement events include one or more of 1) a first event that is triggered when a cell with a highest measurement quality changes and is associated with a similar measurement object or frequency with a previous cell, and/or 2) a second event that is triggered when cell with a highest measurement quality changes, but the change occurs with the cell.

The UE can perform 904 one or more measurements (e.g., dedicated training measurements) based on the dedicated training measurement events.

The UE can send 906 to the network 1020 (e.g., via the base station 102) , one or more measurement reports based on the dedicated training measurement events, where the measurement reports are used for training the one or more network-side AI based models. In one aspect, the one or more measurements (e.g., dedicated training measurements) may be included in a measurement report.

The signaling may also include the UE monitoring 908 performance of the one or more AI based models 920. Also, the signaling may include the base station 102 or network 1020 monitoring 910 performance of the one or more AI based models 920.

FIG. 10: Flow Chart for a Method of enabling network-side artificial intelligence based model measurement at a UE.

FIG. 10 illustrates an example flow chart of a method 1000 of enabling network-side artificial intelligence based model inference validation, at a UE, according to some embodiments.

The method shown in FIG. 10 may be used in conjunction with any of the systems, methods, or devices illustrated in the Figures, among other devices. In various embodiments, some of the method elements shown may be performed concurrently, in a different order than shown, or may be omitted. Additional method elements may also be performed as desired.

In accordance with an embodiment, a method 1000, for decoding, from a network, configuration information for dedicated training measurement events, wherein the dedicated training measurement events correspond to a set of defined measurement events used by the network, and the dedicated training measurement events are used exclusively for training one or more network-side AI based models, as in block 1010. The method 100 may further comprise performing, by the UE, one or more measurements based on the dedicated training measurement events, as in block 1012. The method 100 may further comprise encoding, for transmission to the network, one or more measurement reports based on the dedicated training measurement events, where the measurement reports are used for training the one or more network-side AI based model, as in block 1014.

In some embodiments, the dedicated training measurement events include a first set of events that corresponds to the set of defined measurement events.

In some embodiments, the method 1000 further comprises decoding, from the network, configuration information for a second set of events that corresponds to the set of defined measurement events to facilitate the training the one or more network-side AI based models associated with the network, wherein the second set of events are triggered when the one or more measurements is greater than a configured threshold compared to a previously reported measurement.

In some embodiments, the dedicated training measurement events are associated with the UE having capability to support the dedicated training measurement events and require consent of the UE prior to configuring the UE to perform the dedicated training measurement events.

In some embodiments, the method 1000 further comprises decoding, from the network, an initial value for each of the dedicated training measurement events, where the initial value is relative to a first dedicated training measurement event.

In some embodiments, the dedicated training measurement events include one or more of: 1) a first event that is triggered when a cell with a highest measurement quality changes and is associated with a similar measurement object or frequency with a previous cell; and 2) a second event that is triggered when cell with a highest measurement quality changes, but the change occurs with the cell.

In some embodiments, the method 1000 further comprises adjusting a measurement periodicity rate for performing the one or more measurements based on one or more of a velocity state and a mobility state, wherein the velocity state and the mobility state include one or more of: 1) a high mobility state corresponding to a short measurement periodicity relative to a default measurement periodicity; 2) a mid-mobility state corresponding to the default measurement periodicity; 3) a low mobility state corresponding to a long measurement periodicity relative to the default measurement periodicity; and 4) a stationary state corresponding to a longest measurement periodicity.

In some embodiments, the method 1000 further comprises performing joint periodic and event-triggered measurements; and encoding, for transmission to the network, the joint periodic and event-triggered measurements based on an event trigger or upon expiration of a timer.

In some embodiments, the measurement event is triggered if a delta between a previous measured quantity is greater than a threshold during a configured time period, wherein the threshold is dependent on a speed state and mobility state of the UE.

In some embodiments, the method 1000 further comprises decoding, from the network, a flag in a measurement object indicating that the measurement object is used only for training the one or more network-side AI based models.

In some embodiments, the method 1000 further comprises decoding, from the network, configuration information for a dedicated self-organizing network (SON) report for collecting the one or more measurements used exclusively for training the one or more network-side AI based models; and encoding, for transmission to the network entity, the dedicated SON report, wherein the dedicated SON report includes multiple measurements used for training the one or more network-side AI based models.

In some embodiments, the dedicated SON report is a measurement for training Report.

In some embodiments, the method 1000 further comprises encoding, for transmission to the network entity, a new availability indication information element (IE) referred to as UE-TrainingMeasurementsAvailable in one or more of a RRCReestablishmentComplete message, a RRCReconfigurationComplete message, a RRCResumeComplete message, and a RRCSetupComplete message to indicate the availability of the dedicated SON report.

In some embodiments, the method 1000 further comprises encoding, from the network, an enhanced UEInformationRequest message that indicates to the UE to provide measurements for training the one or more network-side AI based models; and encoding, for transmission to the network, an enhanced UEInformationResponse message that carries the one or more measurements that are logged for training the one or more network-side AI based models.

In some embodiments, the one or more measurements included in the dedicated SON report are equivalent to measurements and measurement events defined for the dedicated training measurement events in claim 1.

In some embodiments, the method 1000 further comprises logging, by the UE, the measurements when a measurement event of the set of defined measurement events occurs.

In some embodiments, the method 1000 further comprises storing the one or more measurements used exclusively for training the one or more network-side AI based models in a variable, wherein the variable is a VarMeasTraining-Report.

In some embodiments, the VarMeasTraining-Report variable is a list of MeasResults message and an enhanced UEInformationResponse message includes the list of MeasResults message.

In some embodiments, the VarMeasTraining-Report variable uses a structure similar to a MeasResultIdleNR information element (IE) .

In some embodiments, the method 1000 further comprises decoding, from the network, configuration information for an enhanced minimization of drive tests (MDT) report for collecting the one or more measurements for training the one or more network-side AI based models; and encoding, for transmission to the network, the enhanced MDT report, wherein the enhanced MDT report includes the one or more measurements for training the one or more network-side AI based models.

In some embodiments, the enhanced MDT report uses a LogMeasReport message that is in a UEInformationResponse message. In other embodiments, the enhanced MDT report is sent to a Trace Collection Entity (TCE) via a Trace Activation NG-AP message when the training the one or more network-side AI based models is performed in an Operations, Administration, and Maintenance (OAM) domain.

In some embodiments, the Trace Activation NG-AP message is enhanced to include a model training entity (MTE) IP address and an MTE URI.

In some embodiments, the method 1000 further comprises indicating, by the UE, support for measurement reporting for training the one or more network-side AI based models using an optional UE capability.

In some embodiments, the optional UE capability is defined for the dedicated training measurement events or all measurement events.

In some embodiments, the method 1000 further comprises encoding, for transmission to the network, a user consent to enable the UE to be configured to report measurements for the training the one or more network-side AI based models, wherein the user consent is stored in a Unified Data Management (UDM) entity and provided by a core network to the radio access network (RAN) .

In some embodiments, the method 1000 further comprises using an enhanced user consent to enable the UE to provide or not provide consent for performing the one or more measurements based on the dedicated training measurement events.

In some embodiments, an apparatus is disclosed that is configured to cause a user equipment (UE) to perform any of the operations of the method 1000.

In some embodiments, an apparatus is disclosed that is configured to cause a base station to assist with performing any of the operations of the method 1000.

FIG. 11: Flow Chart for a Method of enabling network-side artificial intelligence based model measurement by a base station.

FIG. 11 illustrates an example flow chart of a method 1100 of enabling network-side artificial intelligence based model measurement, at a base station, according to some embodiments.

The method shown in FIG. 11 may be used in conjunction with any of the systems, methods, or devices illustrated in the Figures, among other devices. In various embodiments, some of the method elements shown may be performed concurrently, in a different order than shown, or may be omitted. Additional method elements may also be performed as desired.

In accordance with an embodiment, a method 1100, for enabling network-side artificial intelligence based model measurement prediction, comprises encoding, for transmission to a user equipment (UE) , configuration information for dedicated training measurement events to enable the UE to perform, by the UE, one or more measurements based on the dedicated training measurement events, wherein the dedicated training measurement events correspond to a set of defined measurement events used by the network, and the dedicated training measurement events are used exclusively for training one or more network-side AI based models, as in block 1110.

The method 1100 further comprises decoding, from the UE, one or more measurement reports based on the dedicated training measurement events, wherein the measurement reports are used for training the one or more network-side AI based models, as in block 1112.

In some embodiments, the method 1100 further comprises encoding, for transmission to the UE, configuration information for a second set of events that corresponds to the set of defined measurement events to facilitate the training the one or more network-side AI based models associated with the network, wherein the second set of events are triggered when the one or more measurements is greater than a configured threshold compared to a previously reported measurement.

In some embodiments, the method 1100 further comprises encoding, for transmission to the UE, an initial value for each of the dedicated training measurement events; and encoding, for transmission to the network, the initial value even if the triggering conditions are not met.

In some embodiments, the method 1100 further comprises encoding, for transmission to the UE, a flag in a measurement object indicating that the measurement object is used only for training the one or more network-side AI based models.

In some embodiments, the method 1100 further comprises encoding, for transmission to the UE, configuration information for a dedicated self-organizing network (SON) report for collecting the one or more measurements used exclusively for training the one or more network-side AI based models, where the dedicated SON report is a measurement for training Report.

In some embodiments, the method 1100 further comprises encoding, for transmission to the UE, configuration information for an enhanced minimization of drive tests (MDT) report for collecting the one or more measurements for training the one or more network-side AI based models.

In some embodiments, the method 1100 further comprises decoding, from the UE, the joint periodic and event-triggered measurements based on an event trigger or upon expiration of a timer.

In some embodiments, the method 1100 further comprises decoding, from the UE, the dedicated SON report, wherein the dedicated SON report includes multiple measurements used for training the one or more network-side AI based models.

In some embodiments, the method 1100 further comprises decoding, from the UE, a new availability indication information element (IE) referred to as UE-TrainingMeasurementsAvailable in one or more of a RRCReestablishmentComplete message, a RRCReconfigurationComplete message, a RRCResumeComplete message, and a RRCSetupComplete message to indicate the availability of the dedicated SON report.

In some embodiments, the method 1100 further comprises decoding, from the UE, an enhanced UEInformationRequest message that indicates to the UE to provide measurements for training the one or more network-side AI based models.

In some embodiments, the method 1100 further comprises decoding, from the UE, an enhanced UEInformationResponse message that carries the one or more measurements that are logged for training the one or more network-side AI based models; and the enhanced MDT report, wherein the enhanced MDT report includes the one or more measurements for training the one or more network-side AI based models.

In some embodiments, the method 1100 further comprises decoding, from the UE, a user consent indicating to the network the UE is enabled to be configured to report measurements for the training the one or more network-side AI based models, wherein the user consent is stored in a Unified Data Management (UDM) entity and provided by a core network to the radio access network (RAN) .

In some embodiments, an apparatus is disclosed that is configured to cause a base station to perform any of the operations of the method 1100.

In some embodiments, an apparatus is disclosed that is configured to cause a user equipment (UE) to assist with performing any of the operations of the method 1100.

In some embodiments, a computer program product is disclosed, comprising computer instructions which, when executed by one or more processors, perform any of the operations described with respect to the method 1200.

Embodiments of the present disclosure may be realized in any of various forms. For example, some embodiments may be realized as a computer-implemented method, a computer readable memory medium, or a computer system. Other embodiments may be realized using one or more custom-designed hardware devices such as ASICs. Still other embodiments may be realized using one or more programmable hardware elements such as FPGAs.

In some embodiments, a non-transitory computer-readable memory medium may be configured so that it stores program instructions and/or data, where the program instructions, if executed by a computer system, cause the computer system to perform a method, e.g., any of the method embodiments described herein, or, any combination of the method embodiments described herein, or, any subset of any of the method embodiments described herein, or, any combination of such subsets.

In some embodiments, a device (e.g., a UE 106) may be configured to include a processor (or a set of processors) and a memory medium, where the memory medium stores program instructions, where the processor is configured to read and execute the program instructions from the memory medium, where the program instructions are executable to implement any of the various method embodiments described herein (or, any combination of the method embodiments described herein, or, any subset of any of the method embodiments described herein, or, any combination of such subsets) . The device may be realized in any of various forms.

Any of the methods described herein for operating a user equipment (UE) may be the basis of a corresponding method for operating a base station, by interpreting each message/signal X received by the UE in the downlink as message/signal X transmitted by the base station, and each message/signal Y transmitted in the uplink by the UE as a message/signal Y received by the base station.

Although the embodiments above have been described in considerable detail, numerous variations and modifications will become apparent to those skilled in the art once the above disclosure is fully appreciated. It is intended that the following claims be interpreted to embrace all such variations and modifications.

Claims

A method of enabling network-side artificial intelligence (AI) based model measurements by a user equipment (UE) in a wireless communication system, comprising:

decoding, from a network, configuration information for dedicated training measurement events, wherein the dedicated training measurement events correspond to a set of defined measurement events used by the network, and the dedicated training measurement events are used exclusively for training one or more network-side AI based models;

performing, by the UE, one or more measurements based on the dedicated training measurement events; and

encoding, for transmission to the network, one or more measurement reports based on the dedicated training measurement events, wherein the measurement reports are used for training the one or more network-side AI based models.
The method of claim 1, wherein the dedicated training measurement events include a first set of events that corresponds to the set of defined measurement events.
The method of claim 1, wherein the set of defined measurement events comprise one or more of:

an event wherein a serving cell measurement for training is greater than a specified threshold (for training) ;

an event wherein a serving cell measurement for training becomes less than a specified threshold;

an event wherein a neighbor cell measurement for training becomes offset more than the serving cell (SpCell) ;

an event wherein a neighbor cell measurement for training becomes greater than a specified threshold;

an event wherein a serving cell (SpCell) measurement for training becomes less than a threshold1, and a neighbor cell measurement for training becomes greater than a threshold 2;

an event wherein a neighbor cell measurement for training becomes offset more than a secondary cell (SCell) .
The method of claim 1, wherein the one or more measurement reports comprise one or more of:

an event wherein a serving cell measurement for training becomes greater than a specified threshold relative to a previous AT7 report.

an event wherein a serving cell measurement for training becomes less than a specified threshold relative to a previous AT8 report.

an event wherein a neighbor cell measurement for training becomes offset more than a serving cell (SpCell) relative to a previous AT9 report.

an event wherein a neighbor cell measurement for training becomes greater than a specified threshold relative to a previous AT10 report.
The method of claim 1, further comprising decoding, from the network, configuration information for a second set of events that corresponds to the set of defined measurement events to facilitate the training of the one or more network-side AI based models associated with the network, wherein the second set of events are triggered when the one or more measurements is greater than a configured threshold compared to a previously reported measurement.
The method of claim 1, wherein the dedicated training measurement events are associated with the UE having a capability to support the dedicated training measurement events and require consent from the UE to perform measurements for the dedicated training measurement events prior to configuring the UE to perform measurements for the dedicated training measurement events.
The method of claim 1, further comprising decoding, from the network, an initial value for each of the dedicated training measurement events, wherein the initial value is used to determine a relative value to a first measurement performed for one or more of the dedicated training measurement events.
The method of claim 1, wherein the dedicated training measurement events include one or more of:

a first event that is triggered when a cell with a highest measurement quality changes and is associated with a similar measurement object or frequency with a previous cell; and

a second event that is triggered when cell with a highest measurement quality changes but the change occurs with the cell.
The method of claim 1, further comprising adjusting a measurement periodicity rate for performing the one or more measurements based on one or more of a velocity state and a mobility state, wherein the velocity state and the mobility state include one or more of:

a high mobility state corresponding to a short measurement periodicity relative to a default measurement periodicity;

a mid-mobility state corresponding to the default measurement periodicity;

a low mobility state corresponding to a long measurement periodicity relative to the default measurement periodicity; and

a stationary state corresponding to a longest measurement periodicity.
The method of claim 1, further comprising:

performing joint periodic and event-triggered measurements; and

encoding, for transmission to the network, the joint periodic and event-triggered measurements based on an event trigger or upon expiration of a timer.
The method of claim 1, wherein a measurement event is triggered if a delta between a previous measured quantity is greater than a threshold during a configured time period, wherein the threshold is dependent on a speed state and mobility state of the UE.
The method of claim 1, further comprising decoding, from the network, a flag in a measurement object indicating that the measurement object is used only for training the one or more network-side AI based models.
The method of claim 1, further comprising:

decoding configuration information, received from the network, for a dedicated self-organizing network (SON) report for collecting the one or more measurements used exclusively for training the one or more network-side AI based models; and

encoding, for transmission to the network, the dedicated SON report, wherein the dedicated SON report includes multiple measurements used for training the one or more network-side AI based models.
The method of claim 13, wherein the dedicated SON report is a measurement for training Report.
The method of claim 13, further comprising encoding, for transmission to the network, a new availability indication information element (IE) referred to as UE-TrainingMeasurementsAvailable in one or more of a RRCReestablishmentComplete message, a RRCReconfigurationComplete message, a RRCResumeComplete message, and a RRCSetupComplete message to indicate the availability of the dedicated SON report.
The method of claim 13, further comprising:

encoding, for transmission to the network, an enhanced UEInformationRequest message that indicates to the UE to provide measurements for training the one or more network-side AI based models; and

encoding, for transmission to the network, an enhanced UEInformationResponse message that carries the one or more measurements that are logged for training the one or more network-side AI based models.
The method of claim 13, wherein the one or more measurements included in the dedicated SON report are equivalent to measurements and measurement events defined for the dedicated training measurement events in claim 1.
The method of claim 13, further comprising logging, by the UE, the measurements when a measurement event of the set of defined measurement events occurs.
The method of claim 13, further comprising storing the one or more measurements used exclusively for training the one or more network-side AI based models in a variable, wherein the variable is a VarMeasTraining-Report.
The method of claim 19, wherein the VarMeasTraining-Report variable is a list of MeasResults message and an enhanced UEInformationResponse message includes the list of MeasResults message.
The method of claim 19, wherein the VarMeasTraining-Report variable uses a structure similar to a MeasResultIdleNR information element (IE) .
The method of claim 1, further comprising:

decoding, from the network, configuration information for an enhanced minimization of drive tests (MDT) report for collecting the one or more measurements for training the one or more network-side AI based models; and

encoding, for transmission to the network, the enhanced MDT report, wherein the enhanced MDT report includes the one or more measurements for training the one or more network-side AI based models.
The method of claim 22, wherein the enhanced MDT report uses a LogMeasReport message that is in a UEInformationResponse message.
The method of claim 22, wherein the enhanced MDT report is sent to a Trace Collection Entity (TCE) via a Trace Activation NG-AP message when the training the one or more network-side AI based models is performed in an Operations, Administration, and Maintenance (OAM) domain.
The method of claim 24, wherein the Trace Activation NG-AP message is enhanced to include a model training entity (MTE) IP address and an MTE URI.
The method of claim 1, further comprising indicating, by the UE, support for measurement reporting for training the one or more network-side AI based models using an optional UE capability.
The method of claim 26, wherein the optional UE capability is defined for the dedicated training measurement events or all measurement events.
The method of claim 1, further comprising encoding, for transmission to the network, a user consent to enable the UE to be configured to report measurements for the training the one or more network-side AI based models, wherein the user consent is stored in a Unified Data Management (UDM) entity and provided by a core network to a radio access network (RAN) .
The method of claim 28, further comprising using an enhanced user consent to enable the UE to provide or not provide consent for performing the one or more measurements based on the dedicated training measurement events.
An apparatus configured to cause a user equipment (UE) to perform any of the methods of claims 1 to 29.
A baseband processor configured to cause a user equipment (UE) to perform one or more of the method claims 1 to 29.
An apparatus of a user equipment (UE) comprising:

one or more processors, coupled to a memory, configured to:

decode, from a network, configuration information for dedicated training measurement events, wherein the dedicated training measurement events correspond to a set of defined measurement events used by the network, and the dedicated training measurement events are used exclusively for training one or more network-side AI based models;

perform, by the UE, one or more measurements based on the dedicated training measurement events; and

encode, for transmission to the network, one or more measurement reports based on the dedicated training measurement events, wherein the measurement reports are used for training the one or more network-side AI based models.
An apparatus of a base station comprising:

one or more processors, coupled to a memory, configured to:

encode, for transmission to a user equipment (UE) , configuration information for dedicated training measurement events to enable the UE to perform, by the UE, one or more measurements based on the dedicated training measurement events, wherein the dedicated training measurement events correspond to a set of defined measurement events used by a network, and the dedicated training measurement events are used exclusively for training one or more network-side AI based models; and

decode, from the UE, one or more measurement reports based on the dedicated training measurement events, wherein the measurement reports are used for training the one or more network-side AI based models.
A computer program product, comprising computer instructions which, when executed by one or more processors, perform any of the operations described herein.