CN114867128B - A satellite internet of things random access adaptive method, device and storage medium - Google Patents
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Abstract
The invention discloses a satellite internet of things random access self-adaption method, a device and a storage medium, which comprise the steps of carrying out spread spectrum anti-interference analysis based on a pre-built low-orbit satellite internet of things model, determining whether spread spectrum and spread spectrum code length are used in a random access mode according to interference conditions in a current time channel, acquiring preamble state information, estimating current time slot load capacity by using a maximum likelihood estimation method according to the preamble state information, integrating the estimated current time slot load capacity, using machine learning as history data of machine learning to predict load capacity of a plurality of time slots in the future to obtain a future load capacity predicted value, and determining a self-adaption switching random access mode or keeping the current mode according to the determined whether the spread spectrum and spread spectrum code length and the future load capacity predicted value are used in the random access mode. The throughput of the system can be improved, the communication quality can be ensured under the condition of the deterioration of the signal-to-interference ratio, and the throughput is improved.
Description
Technical Field
The invention belongs to the technical field of satellite Internet of things, and relates to a satellite Internet of things random access self-adaption method, a device and a storage medium.
Background
In recent years, with the continuous improvement of the requirements of people for message transmission, the information and communication technology is continuously advanced, and under the background, the concept of the internet of things is attracting a great deal of attention. Because the internet of things can network and informatize the physical world, integrate and communicate the two separated worlds of the physical world and the information world, the internet of things becomes an important component for supporting the intelligent industry, realizes numerous functions of intelligent environment monitoring, waste management, traffic control, intelligent metering, identification positioning, tracking management and the like, promotes the rapid development of each intelligent industry, and provides more and more convenience for the life of people. The internet of things has become an important component of a new generation of information technology, is regarded as a third industrial revolution of development of world information industry after computers and the internet, has become one of important power of development of the current society, and has a wide market prospect.
However, internet of things services based on ground base stations are generally limited by geographical environments (e.g., oceans, deserts), because the ground base stations in these areas are difficult to build and maintain, and are also vulnerable to damage in the event of a natural disaster (e.g., an earthquake).
To solve this problem, students began to study the application potential of the remote wide area network in low orbit satellites as a supplement to the terrestrial internet of things.
For the satellite internet of things system, the service has certain burstiness and randomness and has smaller data volume, and the resource allocation of the multiple access technology based on fixed allocation is not flexible enough, so that unnecessary waste of resources can be caused, and the satellite internet of things system is not suitable for being applied to the satellite internet of things system. Compared with the multiple access technology based on fixed allocation, the multiple access protocol based on competition has the advantages of high flexibility, easiness in implementation, low signaling overhead and high transmission efficiency, and is a communication protocol suitable for being applied to a satellite Internet of things system. But in medium and high load situations, the throughput of the contention-based multiple access protocol system may be significantly or even drastically reduced.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a satellite internet of things random access self-adaptive method which is used for performing anti-interference processing by adopting the most suitable spread spectrum code length according to channel environments under different interference conditions under a low-orbit satellite internet of things scene model. By comparing the predicted value of the load amount estimation with the upper and lower thresholds and combining the selected spread spectrum code length, a proper random access mode is selected in a self-adaptive mode, and the throughput of the system is improved as much as possible under the condition of resisting interference in a channel.
The invention provides a satellite internet of things random access self-adaptive scheme based on environment awareness, which comprises a low-orbit internet of things modeling, anti-interference analysis, load capacity estimation and prediction and a random access self-adaptive scheme. The modeling and anti-interference analysis of the low-rail Internet of things are that a low-rail Internet of things scene is established firstly, different spread spectrum conditions are analyzed under the scene, anti-interference capacity is improved, load quantity estimation and prediction are that in the communication process, load quantity conditions of a current time slot are estimated according to prior time slot state information, all estimation results are integrated to serve as historical data of machine learning, then the load quantity conditions of future time slots are predicted by machine learning, and a random access self-adaption scheme is that on the premise that whether spread spectrum and the length of spread spectrum codes are determined by anti-interference analysis, an upper threshold and a lower threshold of the self-adaption scheme are set, and the random access scheme is adaptively switched through a load prediction value. The method can adaptively switch different random access schemes according to the interference condition and the load condition, can improve the throughput of the system under the condition that the interference is negligible, can ensure the communication quality under the condition that the signal-to-interference ratio is deteriorated, and can improve the throughput.
The technical scheme adopted by the invention is as follows:
In a first aspect, a random access adaptive method for satellite internet of things is provided, including:
based on a pre-constructed low-orbit satellite internet of things model, performing spread spectrum anti-interference analysis, and determining whether to use spread spectrum and spread spectrum code length in a random access mode by combining interference conditions in a current time channel;
Acquiring preamble state information, estimating current time slot load capacity by using a maximum likelihood estimation method according to the preamble state information, integrating the estimated current time slot load capacity, and predicting the load capacity of a plurality of time slots in the future by using machine learning as historical data of machine learning to obtain a future load capacity predicted value;
And determining a self-adaptive switching random access mode or keeping a current mode according to the determined whether to use the spreading and the spreading code length and the future load capacity predicted value in the random access mode.
In some embodiments, the low orbit satellite internet of things model comprises:
In the low-orbit satellite Internet of things model, an Internet of things terminal covered by satellite beams transmits data packets to a low-orbit satellite in a random access mode through an uplink;
The height of the low orbit satellite from the satellite point below the satellite is H, the beam radius is R, the area is V=pi R 2, N t terminals are arranged in the area, the transmitting power of the terminals is represented as P t, and the large-scale path loss g (d k) is as follows:
Where d k is the distance between the kth terminal and the low-orbit satellite, Is wavelength, c is speed of light, f c is carrier frequency, η is path loss coefficient;
the transmitted signal of the terminal is s 1, and the received signal r 1 at the low-orbit satellite receiver is:
S k is a k terminal transmitting signal, N 0 is system noise power, and G r represents receiving gain of the low-orbit satellite antenna, which satisfies the following formula:
Where μ= 2.07123sin (θ)/sin (θ 3dB),θ3dB is the beam gain at the angle corresponding to the 3dB attenuation relative to the beam center, J 1 and J 3 are first and third order bessel functions, D is the antenna aperture, and ζ is the antenna efficiency).
In some embodiments, the processing gain G p of the receiver is
Wherein B b is the frequency bandwidth occupied by the signal before being spread, B ss is the frequency bandwidth occupied by the signal after being spread, S/N out is the output signal-to-noise power ratio of the receiver despreader, and S/N in is the input signal-to-noise power ratio of the receiver.
In some embodiments, acquiring the preamble state information, estimating the current slot load with a method of maximum likelihood estimation according to the preamble state information includes:
analyzing the occurrence probability of different preamble states;
S i is used to indicate the state that the ith preamble is in, s i =0 indicates that the preamble is not selected, namely in an idle state, s i =1 indicates that the preamble is just selected by one terminal, namely the random access process can be completed, the state is called as a successful state, s i is larger than or equal to 2 indicates that two or more terminals select the preamble, and when the preamble is detected as a collision state, the probabilities that the ith preamble is in the 3 states are respectively:
wherein, N p represents the available preamble number of the current time slot, N a represents the access application number of the current time slot;
After completing a random access time slot, the satellite detects all the preamble information, namely the number n 1 of idle preambles, the number n 2 of successfully accessed preambles and the number n 3 of collision preambles, and adopts a maximum likelihood estimation method to estimate the access scale of the current time slot, wherein the expression of a maximum likelihood function P is as follows:
the value of N a when P takes the maximum value is the estimated value of the current time slot application access Namely:
In some embodiments, determining the adaptive handover random access mode or the current mode according to the determined whether to use the spreading and spreading code length and the load prediction value in the random access mode includes:
determining an upper threshold ceiling and a lower threshold floor of the adaptive random access scheme according to the selected spread spectrum code length;
comparing the obtained future load predicted value with the determined upper threshold ceiling and lower threshold floor;
responding to the future load quantity predicted value being larger than an upper threshold ceiling, and adaptively selecting a corresponding spread spectrum SA or SA mode;
Responding to the future load quantity predicted value being smaller than a lower threshold floor, and adaptively selecting a corresponding spread CRDSA or CRDSA mode;
And responding to the future load quantity predicted value being between the upper threshold value ceiling and the lower threshold value floor, and keeping the current access mode unchanged.
In some embodiments, the upper threshold is (1+e) X, the lower threshold is (1-e) X, X represents the normalized load value at the intersection of the two selected access mode throughputs, and e represents the error of the load estimation method.
In a second aspect, a random access adaptive device for satellite internet of things is provided, which is characterized in that the device includes:
the spread spectrum determining module is configured to perform spread spectrum anti-interference analysis based on a pre-constructed low-orbit satellite Internet of things model and determine whether to use spread spectrum and spread spectrum code length in a random access mode according to interference conditions in a current time channel;
The future load quantity prediction module is configured to acquire the state information of the lead code, estimate the current time slot load quantity by using a maximum likelihood estimation method according to the state information of the lead code, integrate the estimated current time slot load quantity, and use the estimated current time slot load quantity as historical data of machine learning to predict the load quantity of a plurality of time slots in the future by using the machine learning to obtain a future load quantity prediction value;
and the access mode determining module is configured to determine a mode of self-adaptive switching random access or keep a current mode according to the determined whether to use the spreading and the spreading code length in the random access mode and the future load quantity predicted value.
In a third aspect, a satellite internet of things random access adaptive device is provided, including a processor and a storage medium;
The storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to the first aspect.
In a fourth aspect, a storage medium is provided, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to the first aspect.
The satellite-based Internet of things random access adaptive method has the beneficial effects that the modeling and anti-interference analysis of the low-orbit Internet of things are realized by firstly establishing a low-orbit Internet of things scene, analyzing the improvement of anti-interference capability of different spread spectrum conditions in the scene, estimating and predicting the load capacity of the current time slot according to prior time slot state information in the communication process, integrating all estimation results to serve as historical data of machine learning, and predicting the load capacity of a future time slot by using machine learning, wherein the random access adaptive scheme is to set an upper threshold and a lower threshold of the adaptive scheme on the premise that whether spread spectrum and the length of a spread spectrum code are determined by anti-interference analysis, and adaptively switch the random access scheme through a load predicted value. The method can adaptively switch different random access schemes according to the interference condition and the load condition, can improve the throughput of the system under the condition that the interference is negligible, can ensure the communication quality under the condition that the signal-to-interference ratio is deteriorated, and can improve the throughput.
Drawings
FIG. 1 is a block diagram of a method for implementing an embodiment of the present invention;
FIG. 2 is a view of a scene model in an embodiment of the invention;
FIG. 3 is a comparative simulation diagram of the anti-interference capability of the spread spectrum according to the method of the present invention;
FIG. 4 is a simulation diagram of maximum likelihood capacity estimation for a method of an embodiment of the present invention;
FIG. 5 is a machine learning load prediction simulation diagram of a method of an embodiment of the present invention;
fig. 6 is a simulation diagram of the result of a random access adaptive scheme according to the method of the embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and examples. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
In the description of the present invention, the meaning of a number is one or more, the meaning of a number is two or more, and greater than, less than, exceeding, etc. are understood to exclude the present number, and the meaning of a number is understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, the descriptions of the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Example 1
A random access adaptive method for a satellite Internet of things comprises the following steps:
based on a pre-constructed low-orbit satellite internet of things model, performing spread spectrum anti-interference analysis, and determining whether to use spread spectrum and spread spectrum code length in a random access mode by combining interference conditions in a current time channel;
Acquiring preamble state information, estimating current time slot load capacity by using a maximum likelihood estimation method according to the preamble state information, integrating the estimated current time slot load capacity, and predicting the load capacity of a plurality of time slots in the future by using machine learning as historical data of machine learning to obtain a future load capacity predicted value;
And determining a self-adaptive switching random access mode or keeping a current mode according to the determined whether to use the spreading and the spreading code length and the future load capacity predicted value in the random access mode.
In some embodiments, as shown in fig. 1, the method is a satellite internet of things random access adaptive scheme based on environment awareness, and the method comprises three parts of low-orbit internet of things modeling and anti-interference analysis, load estimation and prediction, and random access adaptive scheme, and specifically comprises the following steps:
1. low-rail Internet of things modeling and anti-interference analysis
The modeling and anti-interference analysis process of the low-rail Internet of things comprises the following steps:
(1.1) constructing a low-orbit satellite Internet of things model. The method comprises the steps of firstly, transmitting a data packet to a low-orbit satellite by an Internet of things terminal covered by a satellite beam through an uplink in a random access mode, then, demodulating the data packet of the Internet of things terminal by a satellite receiver, and finally, transmitting demodulated information to a ground gateway through an inter-satellite link and a feeder line link.
Considering that a low-orbit satellite has a height H from a point below the satellite, a beam radius R, and an area v=pi R 2, N t terminals are disposed in the area, and the transmitting power of the terminal is denoted as P t, the large-scale path loss can be written as:
Where d k is the distance between the kth terminal and the low-orbit satellite, Is the wavelength, c is the speed of light, f c is the carrier frequency, η is the path loss coefficient. Let us consider that the transmitted signal of the terminal is s 1 and the received signal r 1 at the low-orbit satellite receiver can be written as:
S k is a k terminal transmitting signal, N 0 is system noise power, and G r represents receiving gain of the low-orbit satellite antenna, which satisfies the following formula:
Where μ= 2.07123sin (θ)/sin (θ 3dB),θ3dB is the beam gain at the angle corresponding to the 3dB attenuation relative to the beam center, J 1 and J 3 are first and third order bessel functions, D is the antenna aperture, and ζ is the antenna efficiency).
(1.2) We consider analyzing the improvement of the interference immunity of the system by spreading codes of different lengths. In measuring the anti-interference capability of a spread spectrum communication system, a concept of "processing gain" is generally introduced for description. The processing gain G p is defined as the ratio of the output signal to noise power ratio (S/N) out of the receiver despreader to the input signal to noise power ratio (S/N) in of the receiver, i.e
Considering that the power spectral density of the interference and noise entering the receiver-related despreader is uniformly distributed and the spectral density is N 0, the power of the interference and noise input to the receiver-related despreader is N in=N0Bss, where B ss is the bandwidth of the receiver. Let the signal power at the receiver dependent despreader input be P, then the signal to noise ratio at the receiver dependent despreader input be (S/N) in=P/N0Bss. After the correlation processing of the receiver despreader, the signal can pass through the filter with the bandwidth of B b without distortion, so that the energy of the signal is not lost, and the power of the output signal of the receiver is still P. Only a small portion of the energy of the interference and noise passes through the filter with bandwidth B b and a large portion of the energy is filtered by the filter, so that the processing gain G p of the receiver is
Where B b is the bandwidth occupied by the signal before the spread spectrum processing, and B ss is the bandwidth occupied by the signal after the spread spectrum processing. This illustrates that the processing gain of a spread spectrum receiver is proportional to its spread spectrum signal bandwidth B ss (the single-sided bandwidth of the signal before despreading) and inversely proportional to the bandwidth of the non-spread spectrum signal B b (the single-sided bandwidth of the signal after despreading).
2. Load amount estimation and prediction
The load amount estimation and prediction specifically comprises the following steps:
(2.1) first, the probability of occurrence of different preamble states is analyzed. S i indicates the state of the i-th preamble. s i =0 indicates that the preamble is not selected, i.e. in an idle state, s i =1 indicates that the preamble is just selected by one terminal, i.e. the random access procedure can be completed, the state is called as a successful state, s i is larger than or equal to 2 indicates that two or more terminals select the preamble, and the probability that the ith preamble is in the 3 states is respectively:
Where N p represents the number of available preambles for the current slot and N a represents the number of access applications for the current slot.
(2.2) After completing one random access slot, the satellite may detect all preamble information, i.e., the number of idle preambles n 1, the number of successful access preambles n 2, and the number of collision preambles n 3. The access scale of the current time slot is estimated by adopting a maximum likelihood estimation method, and the maximum likelihood function expression is as follows:
The value of N a is the estimated value of the current time slot application access when the maximum value is obtained Namely:
(2.3) by the maximum likelihood estimation method adopted in (2.2), we can get all slot loading situations before the current slot. Then, we integrate the obtained slot load results and send them to the machine learning module. The historical data is analyzed through a machine learning means, so that the load capacity condition of a plurality of time slots in the future can be predicted.
3. Random access adaptive scheme
The random access self-adaptive scheme specifically comprises the following steps:
And (3.1) according to the analysis of the step (1.2), combining the interference condition in the channel at the current moment, deciding whether to adopt a spread spectrum means, and selecting a proper spread spectrum code length if the spread spectrum is needed.
(3.2) In order to reduce the influence of the load estimation error and the frequent switching access mode on the system efficiency, an upper threshold and a lower threshold may be set in the adaptive switching scheme, and a region between the upper threshold and the lower threshold may be used as a transition-free region. Since the probability of selecting the wrong access method based on the load estimation value is extremely low in the interval (0, (1-e) X) and the interval ((1+e) X, maxG), we consider to study to set the conversion-free region (X-a, x+a) symmetrical with respect to X in the interval ((1-e) X, (1+e) X). X represents the normalized load value at the intersection of the throughput of the two selected access modes, e represents the error of the load estimation method, maxG represents the maximum load value considered for the study.
When the value of a varies within the interval (0, eX), we find that the error probability of the scheme selected according to the load estimation value is 50% for the interval (X-a, X+a), so it is concluded that the accuracy of the scheme selected according to the load estimation value does not increase with the increase of the transition-free zone interval within the considered ((1-e) X, (1+e) X), but the frequency of the transition in a manner can be reduced, i.e. the system efficiency is improved. Therefore ((1-e) X, (1+e) X) is selected as the conversion-free region most suitably, i.e., the upper threshold is (1+e) X and the lower threshold is (1-e) X.
(3.3) Determining an upper threshold value threshold ceiling and a lower threshold value threshold floor of the adaptive random access scheme according to the spreading code length selected in (3.1). Here, since the load prediction scheme error is about 2%, when e is set to 2%, the upper threshold is 1.02·x, and the lower threshold is 0.98·x, which are the upper and lower thresholds corresponding to the partial spread code lengths as shown in table 1.
And (3.4) comparing the future load predicted value obtained in the step (2.3) with an upper threshold value threshold ceiling and a lower threshold value threshold floor determined in the step (3.2), adaptively selecting a corresponding spread spectrum SA or SA mode larger than the upper threshold value threshold ceiling, adaptively selecting a corresponding spread spectrum CRDSA or CRDSA mode smaller than the lower threshold value threshold floor, and maintaining the last round of access mode unchanged between the upper threshold value and the lower threshold value.
TABLE 1
The relevant parameter settings for the simulation of fig. 3 are as follows:
Parameters (parameters) | Parameter value | Parameters (parameters) | Parameter value |
Data length | 10000000 | Carrier-to-interference ratio (dB) | -20~10 |
Modulation scheme | BPSK | Spreading code type | Walsh code |
The relevant parameter settings for the simulation of fig. 4 are as follows:
the relevant parameter settings for the simulation of fig. 5 are as follows:
Parameters (parameters) | Parameter value | Parameters (parameters) | Parameter value |
Number of terminals | 100000 Times | Learning time slots | 300~2300 |
Number of observation time slots | 4000 | Predicting time slots | 2300~2500 |
The relevant parameter settings for the simulation of fig. 6 are as follows:
Parameters (parameters) | Parameter value | Parameters (parameters) | Parameter value |
Load range | 0~10 | Monte Carlo times | 5000 |
Fig. 2 is a low orbit satellite internet of things scene modeling, including different geographic environments and internet of things terminal types.
Fig. 3 shows the error rate at different interference levels for different spreading code lengths.
Fig. 4 is a simulation diagram of maximum likelihood load estimation.
FIG. 5 is a machine learning load prediction simulation graph.
Fig. 6 is a simulation diagram of adaptive random access scheme throughput and packet loss rate for different spreading code lengths. Comparing several simulation graphs, we can see that under the condition that the interference can be ignored, the self-adaptive scheme can also improve throughput and reduce the packet loss rate without spreading, and under the condition that the signal-to-interference ratio is deteriorated, we introduce spreading, and as the length of the spreading code is increased, the throughput is also improved and the packet loss rate is reduced.
Example 2
The embodiment provides a satellite internet of things random access self-adaptive device, which is characterized by comprising:
the spread spectrum determining module is configured to perform spread spectrum anti-interference analysis based on a pre-constructed low-orbit satellite Internet of things model and determine whether to use spread spectrum and spread spectrum code length in a random access mode according to interference conditions in a current time channel;
The future load quantity prediction module is configured to acquire the state information of the lead code, estimate the current time slot load quantity by using a maximum likelihood estimation method according to the state information of the lead code, integrate the estimated current time slot load quantity, and use the estimated current time slot load quantity as historical data of machine learning to predict the load quantity of a plurality of time slots in the future by using the machine learning to obtain a future load quantity prediction value;
and the access mode determining module is configured to determine a mode of self-adaptive switching random access or keep a current mode according to the determined whether to use the spreading and the spreading code length in the random access mode and the future load quantity predicted value.
The embodiment also provides a satellite internet of things random access self-adaptive device, which comprises a processor and a storage medium;
The storage medium is used for storing instructions;
the processor is operative according to the instructions to perform the steps of the method according to embodiment 1.
Example 3
The present embodiment provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method described in embodiment 1.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
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