CN116887411A - 5G access network network slicing configuration method and system for distribution network control services - Google Patents
5G access network network slicing configuration method and system for distribution network control services Download PDFInfo
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- H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
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Abstract
本发明涉及一种配电网控制类业务的5G接入网网络切片配置方法及系统，其包括：为控制类业务切片分配初始带宽，并建立面向配电网控制类业务，以最小化频谱资源数量为目标，以通信速率以及端到端时延为约束的5G无线接入网网络切片配置优化模型；将带宽分配与具体的RB解耦，将优化模型转化为凸优化模型；将求解出的带宽分配变量进行取整处理，得到最终的优化结果，并划定软切片带宽。本发明能在满足配电网控制类业务网络服务质量的前提下尽可能减少RB资源的使用，提高系统的总体服务质量；可以在5G网络切片领域中应用。
The invention relates to a 5G access network network slice configuration method and system for distribution network control services, which includes: allocating initial bandwidth to control service slices, and establishing distribution network control services to minimize spectrum resources. A 5G wireless access network network slicing configuration optimization model with quantity as the goal and communication rate and end-to-end delay as constraints; decouple bandwidth allocation from specific RBs and transform the optimization model into a convex optimization model; convert the solved The bandwidth allocation variable is rounded to obtain the final optimization result, and the soft slice bandwidth is delineated. The present invention can reduce the use of RB resources as much as possible and improve the overall service quality of the system on the premise of satisfying the service quality of the distribution network control business network; it can be applied in the field of 5G network slicing.
Description
Technical Field
The invention relates to the technical field of 5G network slicing, in particular to a 5G access network slicing configuration method and system for power distribution network control type services.
Background
The 5G network slice has the advantages of being capable of being customized for the vertical industry, achieving optimal resource utilization, high reliability, safety isolation and the like, can effectively improve and enhance the quality of service (QoS, quality of service) of the power communication network, ensures the data transmission safety of power business, and supports the stable and efficient normal operation of a novel power system. The 5G network slice is mainly divided into three layers of a core network slice, a transmission network slice and an access network slice. Among other things, access network slicing is most challenging, and requires flexible, efficient resource sharing and customization to manage scarce spectrum resources. From the standpoint of guaranteeing QoS and network resource utilization of power distribution network equipment, the development of efficient service supply scheme requirements for 5G access network slicing is particularly urgent.
At present, a plurality of 5G access network slice configuration methods facing to power distribution network service exist. However, some conventional 5G access network slice configuration strategies only involve allocation of spectrum Resource Blocks (RBs), and fix the power allocated to the service nodes within the slice to a value, which may result in waste of spectrum resources. Some 5G access network slice configuration strategies only consider the communication rate requirements of service nodes in slices in the access network, and do not consider the end-to-end delay constraint of the service nodes, so that the service quality of the service nodes may not be satisfied. The 5G access network slice configuration strategy which simultaneously considers the power distribution and the RB distribution under the end-to-end time delay and the service quality constraint does not exist, so that the differentiated service requirements of the power distribution network control class are difficult to meet.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a 5G access network slice configuration method and system for power distribution network control type service, which can reduce the use of RB resources as much as possible on the premise of meeting the service quality of the power distribution network control type service network and improve the overall service quality of the system.
In order to achieve the above purpose, the present invention adopts the following technical scheme: A5G access network slice configuration method for a power distribution network control service comprises the following steps: initial bandwidth is distributed for control class service slices, a control class service oriented to a power distribution network is established, and a 5G wireless access network slice configuration optimization model with the minimum frequency spectrum resource quantity as a target and with the communication rate and the end-to-end time delay as constraints is set; decoupling bandwidth allocation and specific RBs, converting an optimization model into a convex optimization model, wherein the RBs are frequency spectrum resource blocks; and rounding the solved bandwidth allocation variable to obtain a final optimization result, and demarcating the bandwidth of the soft slice.
Further, allocating an initial bandwidth for the control class traffic slice includes:
setting the power of each service node according to the maximum transmitting power and the total number of the service nodes;
after obtaining the power distribution variable of each service node, finding out RB resources meeting the preset constraint condition for each service node.
Further, the preset constraint conditions are as follows:
wherein a is n,k An indicator variable for channel allocation; n represents the total number of channels allocated to the control class traffic of the distribution network; b (B) n RB size representing channel n; p is p k Representing the power allocated to service node k; h is a n,k Representing the channel factor of service node k on channel n; n (N) 0 Representing noise power spectral density;representing minimum rate requirements of service nodes;Representing the maximum tolerant delay from end to end of the service node k;Representing the transmission delay of the service node k in the access network; d (D) pr Representing the delay of the core network to the access network.
Further, the 5G wireless access network slice configuration optimization model is:
wherein a is n,k An indicator variable for channel allocation; n represents the total number of channels allocated to the control class traffic of the distribution network; b (B) n RB size representing channel n; p is p k Representing the power allocated to service node k; h is a n,k Representing the channel factor of service node k on channel n; n (N) 0 Representing noise power spectral density;representing minimum rate requirements of service nodes;Representing the maximum tolerant delay from end to end of the service node k;Representing the transmission delay of the service node k in the access network; d (D) pr Representing the delay from the core network to the access network; p (P) max Representing the maximum transmit power; k represents the total number of service nodes;
(a) minimum rate constraint for service node, (b) end-to-end delay constraint for service node, (c) maximum one service node per channel, (d) 0-1 variable for channel indication variable, (e) power constraint for service node.
Further, the convex optimization model is:
wherein n is k Representing the number of channel resources allocated to the service node; b represents RB size; h is a k Representing the channel gain from the base station to user k; l (L) k Indicating the size of the traffic node k transmitting data.
Further, converting the optimization model into a convex optimization model, comprising:
assuming that each service node experiences the same channel gain over all RBs, the channel conditions of the service node are decoupled from the particular RBs, and the base station is assumed to transmit at equal power over all allocated RBs of a single service node, thereby converting the optimization model into a convex optimization model.
Further, rounding the solved bandwidth allocation variable, including:
and finally, the base station allocates the corresponding number of channels and power for each service node, and the rest RBs are defined as soft slices for other slices to use.
A 5G access network slice configuration system for a power distribution network control class service, comprising: the model building module is used for distributing initial bandwidth for the control class service slices, building a control class service oriented to the power distribution network, and configuring an optimization model for the 5G wireless access network slices with the communication rate and the end-to-end time delay as constraints by taking the minimum frequency spectrum resource quantity as a target; the conversion solving module is used for decoupling bandwidth allocation from specific RBs, converting the optimization model into a convex optimization model, and enabling the RBs to be frequency spectrum resource blocks; and the optimization processing module is used for rounding the solved bandwidth allocation variable to obtain a final optimization result and demarcating the bandwidth of the soft slice.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods described above.
A computing apparatus, comprising: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods described above.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the method can meet the requirements of the power distribution network control service on the communication rate and the end-to-end time delay, reduce the use of RB resources as much as possible on the premise of meeting the service quality of the power distribution network control service network, and define the sharing of the soft slice and other network slices, thereby improving the overall service quality of the system.
2. The method and the system can realize the rapid distribution of the RB and the power of the control service of the power distribution network, so that the control service of the power distribution network responds to the high requirement of the control service of the power distribution network on time delay, and has very important application value for the control service of the power distribution network in the 5G background.
Drawings
Fig. 1 is a flowchart of a 5G access network slice configuration method for a power distribution network control service in an embodiment of the present invention;
FIG. 2 is a diagram of a power slice architecture in an embodiment of the invention;
FIG. 3 is a flow chart of a Lagrangian dual decomposition method in an embodiment of the present invention;
fig. 4 is a diagram showing the effect of the number of resources occupied by service nodes according to the number of service nodes in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which are obtained by a person skilled in the art based on the described embodiments of the invention, fall within the scope of protection of the invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Aiming at the problem that the 5G access network slice configuration strategy of power distribution and RB (frequency spectrum resource block) distribution under the constraint of end-to-end time delay and service quality is not considered at present, and the differentiated service requirement of the power distribution network control class is difficult to meet, the invention provides a 5G access network slice configuration method and a system of the power distribution network control class service, which comprise the following steps: allocating an initial bandwidth for the control class service slice; establishing a 5G wireless access network slice configuration optimization model which aims at minimizing the quantity of spectrum resources and takes communication rate and end-to-end time delay as constraints for a power distribution network control service; decoupling bandwidth allocation from specific RBs, converting the optimization model into a form that is easy to handle; solving the converted optimization model by utilizing a Lagrangian dual decomposition algorithm; rounding the solved bandwidth allocation variable to obtain a final optimization result, and defining the bandwidth of the soft slice; the invention can realize the rapid distribution of the bandwidth and the power of the power distribution network control service, thereby responding to the high requirement of the power distribution network control service on the time delay and having very important application value for the power distribution network control service in the 5G background.
In one embodiment of the invention, a 5G access network slice configuration method for a power distribution network control type service is provided. In this embodiment, as shown in fig. 1, the method includes the following steps:
1) Initial bandwidth is distributed for control class service slices, a control class service oriented to a power distribution network is established, and a 5G wireless access network slice configuration optimization model with the minimum frequency spectrum resource quantity as a target and with the communication rate and the end-to-end time delay as constraints is set;
2) Decoupling bandwidth allocation from a specific RB, and converting the optimization model into a convex optimization model;
3) And rounding the solved bandwidth allocation variable to obtain a final optimization result, and demarcating the bandwidth of the soft slice.
In the step 1), an initial bandwidth RB is allocated to the control class service slice, which includes the following steps:
1.1 Setting the power p of each service node k according to the maximum transmitting power and the total number of service nodes k ；
Wherein P is max Represents the maximum transmit power and K represents the total number of service nodes.
1.2 After obtaining the power allocation variable of each service node, finding out the RB resources meeting the preset constraint condition for each service node.
In this embodiment, the preset constraint conditions are:
wherein a is n,k An indicator variable for channel allocation; n represents the total number of channels allocated to the control class traffic of the distribution network; b (B) n RB size representing channel n; p is p k Representing the power allocated to service node k; h is a n,k Representing the channel factor of service node k on channel n; n (N) 0 Representing noise power spectral density;representing minimum rate requirements of service nodes;Representing the maximum tolerant delay from end to end of the service node k;Representing the transmission delay of the service node k in the access network; d (D) pr Representing the delay from the core network to the access network; l (L) k Representing the size of transmission data of the service node k; t (T) slot Representing the duration of each time slot; l (L) packet A frame length representing a core network transmission; r is R L Representing the rate of the line; d (D) p Representing the processing delay of the core network.
In this embodiment, the base station firstly allocates power to each service node on average, calculates the number of RBs required according to the communication requirements of the service nodes on the premise of allocating power on average, allocates the number of resources required for each service node, and completes initial allocation of RBs.
In the step 1), the distribution network control service comprises intelligent distributed distribution automation, distributed power supply and millisecond-level accurate load control.
In the step 2), the power slicing architecture is as shown in fig. 2, and the objective function of the optimization model is set to be the minimum number of RBs used; and setting model constraint as service node speed, service node end-to-end time delay and base station total power. The 5G wireless access network slice configuration optimization model is as follows:
wherein a is n,k An indicator variable for channel allocation; n represents the total number of channels allocated to the control class traffic of the distribution network; b (B) n RB size representing channel n; p is p k Representing the power allocated to service node k; h is a n,k Representing the channel factor of service node k on channel n; n (N) 0 Representing noise power spectral density;representing minimum rate requirements of service nodes;Representing the maximum tolerant delay from end to end of the service node k;Representing the transmission delay of the service node k in the access network; d (D) pr Representing the delay from the core network to the access network; p (P) max Representing the maximum transmit power; k represents the total number of service nodes; p represents a power allocation vector;
wherein, (a) is minimum rate constraint of service node, (b) is end-to-end delay constraint of service node, (c) indicates that each channel can only serve one service node at most, (d) indicates that channel indication variable is 0-1 variable, (e) and (f) is power constraint of service node.
In the above step 2), since the 5G radio access network slice configuration optimization model established in the present embodiment is a mixed integer nonlinear programming (Mixed Integer Nonlinear Programming, MINLP), which is an NP-hard problem, it is difficult to solve. Therefore, a certain transformation is required to be introduced to convert the model into a form which is easy to process, the RB allocation indicating variable in the established optimization model is decoupled from the specific RB, a new RB allocation variable is introduced, and the 0-1 variable in the optimization model is eliminated, so that the new optimization model which is easy to process is obtained. To simplify the problem, the optimization model is converted into a convex optimization model, specifically:
assuming that each service node experiences the same channel gain over all RBs, the channel conditions of the service node are decoupled from the particular RBs, and the base station is assumed to transmit at equal power over all allocated RBs of a single service node, thereby converting the optimization model into a convex optimization model.
Therefore, the conversion of the optimization model into a convex optimization model is:
wherein n is k Representing the number of channel resources allocated to the service node, B representing the RB size; h is a k Representing the channel gain from the base station to user k; l (L) k Indicating the size of the traffic node k transmitting data.
In the step 2), as shown in fig. 3, the lagrangian dual decomposition algorithm is adopted to solve the optimization model after transformation, and the method comprises the following steps:
2.1 A Lagrangian function of a 5G wireless access network slice configuration optimization model is established and is subjected to bias guide;
in this embodiment, it can be seen that the objective function in the established optimization model is a convex function, and the first and second constraints areIn the form of (a), this is a convex function. The objective function and constraints in the optimization model are both convex, so the problem is solved using Lagrangian dual decomposition. The Lagrangian function of the optimization model is as follows:
the lagrangian function is biased against the power and channel allocation variables, respectively:
2.2 Updating the service node RB and the power allocation variable using the lagrangian factor;
specifically, let the offset equal to 0 updates the power and channel allocation variables.
2.3 Updating the Lagrangian factor, updating the service node RB and the power allocation variable, and repeating until convergence;
the updating step of the Lagrangian multiplier is as follows:
wherein [ x ]] + Represents max (x, 0).
In the step 3), the solved bandwidth allocation variable is rounded, specifically: the solved bandwidth allocation variable n k Performing upward rounding processing to obtain the number of actual channels required by each service node, and finally, the base station allocates the corresponding number of channels and power for each service node, and divides the rest RBs into soft slices for other slices to useIncreasing the quality of service of the overall system as shown in fig. 4. The number of RBs required by the algorithm of the present invention is reduced by 18.4% compared to the algorithm of average power allocation.
In one embodiment of the present invention, there is provided a 5G access network slice configuration system for a power distribution network control class service, including:
the model building module is used for distributing initial bandwidth for the control class service slices, building a control class service oriented to the power distribution network, and configuring an optimization model for the 5G wireless access network slices with the communication rate and the end-to-end time delay as constraints by taking the minimum frequency spectrum resource quantity as a target;
the conversion solving module is used for decoupling bandwidth allocation from a specific RB and converting the optimization model into a convex optimization model;
and the optimization processing module is used for rounding the solved bandwidth allocation variable to obtain a final optimization result and demarcating the bandwidth of the soft slice.
In the above embodiment, allocating an initial bandwidth for the control class service slice includes:
setting the power of each service node according to the maximum transmitting power and the total number of the service nodes;
after obtaining the power distribution variable of each service node, finding out RB resources meeting preset constraint for each service node.
In the above embodiment, the preset constraint is:
wherein a is n,k An indicator variable for channel allocation; n represents the total number of channels allocated to the control class traffic of the distribution network; b (B) n RB size representing channel n; p is p k Representing the power allocated to service node k; h is a n,k Representing the channel factor of service node k on channel n; n (N) 0 Representing noise power spectral density;representing minimum service nodeRate requirements;Representing the maximum tolerant delay from end to end of the service node k;Representing the transmission delay of the service node k in the access network; d (D) pr Representing the delay of the core network to the access network.
In the above embodiment, the power distribution network control service includes intelligent distributed power distribution automation, distributed power supply and millisecond-level accurate load control.
In the above embodiment, the 5G radio access network slice configuration optimization model is:
wherein a is n,k An indicator variable for channel allocation; n represents the total number of channels allocated to the control class traffic of the distribution network; b (B) n RB size representing channel n; p is p k Representing the power allocated to service node k; h is a n,k Representing the channel factor of service node k on channel n; n (N) 0 Representing noise power spectral density;representing minimum rate requirements of service nodes;Representing the maximum tolerant delay from end to end of the service node k;Representing the transmission delay of the service node k in the access network; d (D) pr Representing the delay from the core network to the access network; p (P) max Representing the maximum transmit power; k represents the total number of service nodes;
(a) minimum rate constraint for service node, (b) end-to-end delay constraint for service node, (c) maximum one service node per channel, (d) 0-1 variable for channel indication variable, (e) power constraint for service node.
In the above embodiment, the convex optimization model is:
wherein n is k The number of channel resources allocated to the service node is represented, and B represents the RB size.
In the above embodiment, converting the optimization model into the convex optimization model includes:
assuming that each service node experiences the same channel gain over all RBs, the channel conditions of the service node are decoupled from the particular RBs, and the base station is assumed to transmit at equal power over all allocated RBs of a single service node, thereby converting the optimization model into a convex optimization model.
In the above embodiment, the rounding processing is performed on the solved bandwidth allocation variable, including:
and finally, the base station allocates the corresponding number of channels and power for each service node, and the rest RBs are defined as soft slices for other slices to use.
The system provided in this embodiment is used to execute the above method embodiments, and specific flow and details refer to the above embodiments, which are not described herein.
A computing device provided in an embodiment of the present invention may be a terminal, which may include: a processor (processor), a communication interface (Communications Interface), a memory (memory), a display screen, and an input device. The processor, the communication interface and the memory complete communication with each other through a communication bus. The processor is configured to provide computing and control capabilities. The memory comprises a non-volatile storage medium storing an operating system and a computer program which when executed by the processor implements the method of the above embodiments; the internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a manager network, NFC (near field communication) or other technologies. The display screen can be a liquid crystal display screen or an electronic ink display screen, the input device can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computing equipment, and can also be an external keyboard, a touch pad or a mouse and the like. The processor may invoke logic instructions in memory.
Further, the logic instructions in the memory described above may be implemented in the form of software functional units and stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In one embodiment of the present invention, a computer program product is provided, the computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are capable of performing the methods provided by the method embodiments described above.
In one embodiment of the present invention, a non-transitory computer readable storage medium storing server instructions that cause a computer to perform the methods provided by the above embodiments is provided.
The foregoing embodiment provides a computer readable storage medium, which has similar principles and technical effects to those of the foregoing method embodiment, and will not be described herein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. 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.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The 5G access network slice configuration method for the power distribution network control type service is characterized by comprising the following steps of:
initial bandwidth is distributed for control class service slices, a control class service oriented to a power distribution network is established, and a 5G wireless access network slice configuration optimization model with the minimum frequency spectrum resource quantity as a target and with the communication rate and the end-to-end time delay as constraints is set;
decoupling bandwidth allocation and RB, converting the optimization model into a convex optimization model, wherein RB is a frequency spectrum resource block;
and rounding the solved bandwidth allocation variable to obtain a final optimization result, and demarcating the bandwidth of the soft slice.
2. The method for configuring a 5G access network slice for a control class service of a power distribution network of claim 1, wherein allocating an initial bandwidth for the control class service slice comprises:
setting the power of each service node according to the maximum transmitting power and the total number of the service nodes;
after obtaining the power distribution variable of each service node, finding out RB resources meeting the preset constraint condition for each service node.
3. The method for configuring the 5G access network slices of the power distribution network control type service according to claim 2, wherein the preset constraint conditions are:
wherein a is n,k An indicator variable for channel allocation; n represents the total number of channels allocated to the control class traffic of the distribution network; b (B) n RB size representing channel n; p is p k Representing the power allocated to service node k; h is a n,k Representing the channel factor of service node k on channel n; n (N) 0 Representing noise power spectral density;representing minimum rate requirements of service nodes;Representing the maximum tolerant delay from end to end of the service node k;Representing the transmission delay of the service node k in the access network; d (D) pr Representing the delay of the core network to the access network.
4. The method for configuring the 5G access network slice of the power distribution network control class service according to claim 1, wherein the 5G radio access network slice configuration optimization model is:
wherein a is n,k An indicator variable for channel allocation; n represents the total number of channels allocated to the control class traffic of the distribution network; b (B) n RB size representing channel n; p is p k Representing the power allocated to service node k; h is a n,k Representing the channel factor of service node k on channel n; n (N) 0 Representing noise power spectral density;representing minimum rate requirements of service nodes;Representing the maximum tolerant delay from end to end of the service node k;Representing the transmission delay of the service node k in the access network; d (D) pr Representing the delay from the core network to the access network; p (P) max Representing the maximum transmit power; k represents the total number of service nodes;
(a) minimum rate constraint for service node, (b) end-to-end delay constraint for service node, (c) maximum one service node per channel, (d) 0-1 variable for channel indication variable, (e) power constraint for service node.
5. The method for configuring a 5G access network slice for a power distribution network control class service according to claim 4, wherein the convex optimization model is:
wherein n is k Representing the number of channel resources allocated to the service node; b represents RB size; h is a k Representing the channel gain from the base station to user k; l (L) k Indicating the size of the traffic node k transmitting data.
6. The method for configuring a 5G access network slice for a power distribution network control class service according to claim 1, wherein converting the optimization model into a convex optimization model comprises:
assuming that each service node experiences the same channel gain over all RBs, the channel conditions of the service node are decoupled from the particular RBs, and the base station is assumed to transmit at equal power over all allocated RBs of a single service node, thereby converting the optimization model into a convex optimization model.
7. The method for configuring the 5G access network slices of the power distribution network control service according to claim 1, wherein the rounding processing of the solved bandwidth allocation variable comprises:
and finally, the base station allocates the corresponding number of channels and power for each service node, and the rest RBs are defined as soft slices for other slices to use.
8. A 5G access network slice configuration system for a power distribution network control class service, comprising:
the model building module is used for distributing initial bandwidth for the control class service slices, building a control class service oriented to the power distribution network, and configuring an optimization model for the 5G wireless access network slices with the communication rate and the end-to-end time delay as constraints by taking the minimum frequency spectrum resource quantity as a target;
the conversion solving module is used for decoupling bandwidth allocation from specific RBs, converting the optimization model into a convex optimization model, and enabling the RBs to be frequency spectrum resource blocks;
and the optimization processing module is used for rounding the solved bandwidth allocation variable to obtain a final optimization result and demarcating the bandwidth of the soft slice.
9. A computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-7.
10. A computing device, comprising: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-7.
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