Wireless network control system based on MEC and URLLC and resource allocation method thereof

Abstract

The invention relates to the technical field of wireless communication and control, in particular to a wireless network control system based on MEC and URLLC and a resource allocation method thereof. According to the wireless network control system provided by the invention, on one hand, the MEC technology is adopted to distribute calculation tasks to a plurality of base stations, so that the calculation burden of a central server is reduced, and the calculation efficiency and expandability of the system are improved; and on the other hand, a large-scale multiple-input-multiple-output technology is adopted in the base station, and a time division multiple access protocol is adopted in interaction between the base station and the subsystem, so that the utilization rate of communication resources is effectively improved, interference among users is reduced, and implementation of URLLC is ensured. The method supports cooperative control of a plurality of subsystems, is suitable for a large-scale industrial Internet of Things scene, and has a wide application prospect. According to the invention, the problems of large base station calculation load and poor communication control stability in the existing wireless network control system adopting wireless communication are solved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication and control technologies, and more specifically, to: 1. A wireless network control system based on MEC (Mobile Edge Computing) and URLLC (Ultra-Reliable Low-Latency Communication); 2. A resource allocation method for the system. The present invention is applicable to scenarios such as industrial automation and the Internet of Things that require low-latency and high-reliability control. Background Art

[0002] With the rapid development of industrial automation, wireless network control systems are increasingly widely used in industrial Internet of Things. Considering stability, traditional wireless network control systems usually still rely on wired communication, which reduces flexibility and scalability.

[0003] As a direction of technological development, wireless network control systems can achieve remote control and data collection through wireless communication technologies, thereby reducing system costs and improving flexibility, but there are challenges such as communication latency, reliability, and computing resource allocation. Specifically, existing wireless network control systems using wireless communication use fixed base stations and traditional communication protocols, resulting in large computing loads on base stations and poor communication control stability.

[0004] In addition, in large-scale industrial Internet of Things scenarios, multiple base stations need to work together, and there are also prone to situations of unbalanced resource allocation and high control latency. Therefore, how to effectively allocate communication and computing resources to ensure the control stability and low latency of the system has also become an urgent problem to be solved. Summary of the Invention

[0005] Based on this, in view of the problems of large computing loads on base stations and poor communication control stability in existing wireless network control systems using wireless communication, a wireless network control system based on MEC and URLLC and its resource allocation method are provided.

[0006] The present invention is implemented by the following technical solutions:

[0007] In a first aspect, the present invention discloses a wireless network control system based on MEC and URLLC, which is set in a target control area.

[0008] The wireless network control system based on MEC and URLLC includes: M base stations and K subsystems.

[0009] The M base stations serve as mobile communication switching centers; each base station adopts large-scale MIMO technology and is equipped with 1 edge server.

[0010] The K subsystems serve as wireless communication control objects; any one subsystem is only associated with one base station and conducts data interaction through wireless communication; any one base station supports association with multiple subsystems.

[0011] Among them, the k-th subsystem includes: the k-th device, the k-th sensor, and the k-th actuator;

[0012] The k-th sensor is used to upload the sensed data to the base station associated with the k-th subsystem;

[0013] The base station associated with the k-th subsystem is used to take the received sensed data as a computing task, perform computing through the edge server it is equipped with to generate a control signal, and then downlink the control signal to the k-th actuator;

[0014] The k-th actuator is used to control the k-th device according to the control signal.

[0015] Among them, M base stations adopt the time division multiple access protocol so that different base stations perform uplink transmission and downlink transmission in orthogonal time slots.

[0016] Among them, one working cycle of the wireless network control system is represented as T;

[0017]

[0018] In the formula, is the m-th uplink transmission time slot, which represents the time taken for the m-th base station and its associated subsystem to complete uplink transmission; is the common computing time slot, which represents the overlapping segment of the computing time of M edge servers; t m is the m-th downlink transmission time slot, which represents the time taken for the m-th base station and its associated subsystem to complete downlink transmission.

[0019] This wireless network control system based on MEC and URLLC implements the method or process according to the embodiments of the present disclosure.

[0020] In a second aspect, the present invention discloses a resource allocation method, which is applied to the wireless network control system based on MEC and URLLC disclosed in the first aspect.

[0021] A resource allocation method includes:

[0022] S1, taking the association configuration, transmission power, and time slot allocation as consideration factors, taking the stability constraint as a constraint condition, and taking minimizing T as the objective function minT, to construct an optimization model;

[0023] S2, solving the optimization model to obtain an optimal resource allocation scheme;

[0024] Among them, the association configuration is the association α between the m-th base station and the k-th subsystem m,k ;

[0025] The transmission power includes: the uplink transmission power between the m-th base station and the k-th subsystem The downlink transmission power p m,k ;

[0026] The time slot allocation includes: the m-th uplink transmission time slot The common computing time slot The m-th downlink transmission time slot t m ;

[0027] Among them, the stability constraints include: communication reliability constraints, computing resource limit constraints, and control stability constraints.

[0028] Among them, the optimization model solving method includes: converting the optimization model into a convex difference problem and using the convex-concave optimization algorithm to solve it.

[0029] This resource allocation method implements the method or process according to the embodiments of the present disclosure.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. The present invention proposes a wireless network control system. On the one hand, the MEC technology is used to distribute computing tasks to multiple base stations, reducing the computing burden on the central server and improving the computing efficiency and scalability of the system. On the other hand, large-scale MIMO (multiple input multiple output) is adopted at the base station, and the time division multiple access protocol is adopted for the interaction between the base station and the subsystem, effectively improving the utilization rate of communication resources, reducing interference between users, and ensuring the realization of URLLC. The present invention supports the cooperative control of multiple subsystems, is applicable to large-scale industrial Internet of Things scenarios, and has broad application prospects.

[0032] 2. The present invention proposes a resource allocation method for a wireless network control system, aiming to minimize the working cycle of the wireless network control system, jointly optimizing the association configuration between the base station and the subsystem, communication resource allocation, and computing resource allocation, significantly reducing the closed-loop control delay, and improving the control response speed and stability of the system. Description of the Drawings

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0034] Figure 1 It is the structural diagram of the wireless network control system based on MEC and URLLC provided in Embodiment 1 of the present invention;

[0035] Figure 2 For Figure 1 a schematic diagram of the composition of a working cycle T of a wireless network control system;

[0036] Figure 3 is a flowchart of the resource allocation method provided in Embodiment 1 of the present invention. Detailed implementation manners

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] It should be noted that when a component is referred to as "installed on" another component, it can be directly on the other component or there may also be an intermediate component. When a component is considered to be "set on" another component, it can be directly set on the other component or there may be an intermediate component at the same time. When a component is considered to be "fixed to" another component, it can be directly fixed to the other component or there may be an intermediate component at the same time.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The term "or / and" used herein includes any and all combinations of one or more of the related listed items.

[0040] Embodiment 1

[0041] Referring to Figure 1 , a structural diagram of a wireless network control system based on MEC and URLLC provided in Embodiment 1 of the present invention is shown.

[0042] First of all, it should be noted that this wireless network control system is set in the target control area and can cover all subsystems (a total of K) in the target control area.

[0043] As Figure 1 shown, this wireless network control system includes: M base stations (i.e., Figure 1 Base Station 1 to Base Station M in Figure 1 ), and K subsystems (i.e.,

[0044] Subsystem 1 to Subsystem K in

[0045] The connection between the base station and the subsystem is defined as follows:

[0046] 1. Any one subsystem is only associated with one base station and conducts data interaction through wireless communication;

[0047] 2. Any one base station supports association with multiple subsystems.

[0048] Then, for the base station, each base station adopts large-scale MIMO technology to meet the above connection definition. Each base station uses the matched-filter receive beamforming technology to provide services for its associated subsystems through spatial multiplexing on the same time-frequency resources. Generally, the Mth base station is equipped with N antennas, and N > K is satisfied. Among them, to ensure the smooth progress of communication, it is recommended to make N much larger than K to provide sufficient redundant channels.

[0049] Each base station is equipped with 1 edge server, which alleviates the computing load of the central server (located in the base station) through mobile edge computing.

[0050] The structures of the K subsystems are the same, and each includes: 1 device, 1 sensor, and 1 actuator. Then, taking the kth (k ∈ [1, K]) subsystem (i.e., subsystem k) as an example - it includes: the kth device (i.e., device k), the kth sensor (i.e., sensor k), and the kth actuator (i.e., actuator k).

[0051] The kth sensor is used to upload the sensed data to the base station associated with the kth subsystem;

[0052] The base station associated with the kth subsystem is used to regard the received sensed data as a computing task, and through the edge server it is equipped with, perform calculations to generate a control signal, and then downlink the control signal to the kth actuator;

[0053] The kth actuator is used to control the kth device according to the control signal.

[0054] Then, as Figure 1 shown, there is "upload" and "download" between any one subsystem and its associated base station, and it is recommended to adopt the short-packet communication transmission method to achieve wireless communication data interaction.

[0055] The M base stations adopt the time-division multiple access protocol to enable different base stations to perform uplink transmission and downlink transmission in orthogonal time slots. As Figure 2As shown, it is stipulated that the first base station to the Mth base station work in sequence: after the current base station completes "uploading", the next base station starts "uploading"; after the current base station completes "downloading", the next base station starts "downloading"; then, for a single base station, the time between "uploading" and "downloading" is reserved for computing, which is borne by the edge server equipped with the base station; for different base stations, there will be overlapping public computing durations.

[0056] Of course, for a single base station, there will be "blank" periods before "uploading" or / and after "downloading"; but for the entire wireless network control system, these "blank" periods can be covered by "uploading" and "downloading".

[0057] In this way, by adopting large-scale MIMO at the base station and using the time division multiple access protocol for the interaction between the base station and the subsystem, URLLC is achieved and communication interference is avoided.

[0058] Then, in this wireless network control system, one working cycle is represented as T;

[0059] See Figure 2 , the mathematical expression of T is:

[0060]

[0061] In the formula, is the mth uplink transmission time slot, which represents the time taken for the mth base station and its associated subsystem to complete uplink transmission; is the common computing time slot, which represents the overlapping segment of the computing time of M edge servers; t m is the mth downlink transmission time slot, which represents the time taken for the mth base station and its associated subsystem to complete downlink transmission; m ∈ [1, M].

[0062] That is to say, T is divided into 2M + 1 time slots - among which the first M time slots are used for K subsystems to "upload" data, the (M + 1)th time slot is the overlapping time for M edge servers to compute, and the last M time slots are used for K base stations to "download" control signals.

[0063] Among them, taking the mth base station as an example, it performs "uploading" in the mth time slot, computes from the (m + 1)th time slot to the (M + m)th time slot, and performs "downloading" in the (M + m + 1)th time slot.

[0064] Embodiment 2

[0065] Based on the wireless network control system provided in Embodiment 1, this Embodiment 2 provides a resource allocation method, which is used in the wireless network control system of Embodiment 1 and aims to effectively allocate communication and computing resources to ensure the control stability and low latency of the system.

[0066] Specifically, referring to Figure 3 , the resource allocation method includes:

[0067] S1. Taking the association configuration, transmission power, and time slot allocation as consideration factors, taking the stability constraint as a constraint condition, and taking minimizing T as the objective function minT, an optimization model is constructed;

[0068] First, it should be determined that the optimization model takes minimizing T as the objective function minT.

[0069] Then, considering several factors that affect T, there are:

[0070] 1. Association configuration, which represents the association between the subsystem and the base station - can be represented by α m,k to represent the association between the m-th base station and the k-th subsystem.

[0071] Referring to Embodiment 1, α m,k initially satisfies the following conditions:

[0072]

[0073] 2. Transmission power, which characterizes the uplink transmission power and downlink transmission power between the subsystem and the base station - can be represented by to represent the uplink transmission power between the m-th base station and the k-th subsystem, and p m,k to represent the downlink transmission power between the m-th base station and the k-th subsystem.

[0074] Since the connection between the base station and the subsystem is as described in Embodiment 1 - a single subsystem is only associated with 1 base station, and a single base station supports associating multiple subsystems. Then, initially satisfies:

[0075]

[0076] In the formula, represents the uplink transmission power threshold of the k-th subsystem;

[0077] p m,k satisfies:

[0078]

[0079] In the formula, P m represents the total downlink transmission power threshold of the m-th base station.

[0080] 3. Time slot allocation, which characterizes the specific duration allocation of a working cycle T of the wireless network control system - that is, the m-th uplink transmission time slot Common computing time slot The m-th downlink transmission time slot t m 。

[0081] Referring to Embodiment 1, t m satisfies the following conditions:

[0082]

[0083] Then, considering the calculation constraints to ensure the reliability of model calculation.

[0084] Among them, the constraints include: control stability constraint, computing resource limitation constraint, communication reliability constraint.

[0085] 1. The mathematical expression of the control stability constraint is:

[0086]

[0087] In the formula, Φ k 、Υ k 、Q k are all real symmetric matrices related to the k-th subsystem; η k represents the convergence rate of the k-th subsystem.

[0088] It should be noted that the control stability constraint is constructed through the Lyapunov function and obtained through a series of conversions, aiming to ensure that the control state of the wireless network control system decreases at a given convergence rate η k decreases.

[0089] 2. The mathematical expression of the computing resource limitation constraint is:

[0090]

[0091] In the formula, S k represents the task calculation period of the edge server corresponding to the k-th subsystem; represents the number of bits of the computing task corresponding to the sensed data sent by the k-th subsystem; F m represents the maximum computing frequency of the m-th edge server.

[0092] That is to say, the computing resource constraint is expressed by the computing frequency of the edge server and the computing amount of the task to ensure that the computing task is completed within the allocated time slot.

[0093] 3. The mathematical expression of the communication reliability constraint is:

[0094]

[0095] In the formula, Denote the uplink transmission outage probability between the \(m\)-th base station and the \(k\)-th subsystem;

[0096] Denote the downlink transmission outage probability between the \(m\)-th base station and the \(k\)-th subsystem;

[0097] ε th Denote the reliability threshold.

[0098] That is to say, for "uploading" and "downloading", it is necessary to ensure the reliability of their transmissions, and data transmission interruptions or unavailable transmitted data should not occur.

[0099] Then, the optimization model is thus constructed.

[0100] S2. Solve the optimization model to obtain the optimal resource allocation scheme.

[0101] It should be noted that the optimal resource allocation scheme includes the specific solutions of the above-mentioned associated configuration, transmission power, and time slot allocation.

[0102] The solution method of the optimization model can adopt the following two methods:

[0103] ①. Method 1: Combine the alternating optimization method and the successive convex approximation algorithm to solve the optimization model and obtain the optimal resource allocation scheme.

[0104] Specifically, S2 can be designed as:

[0105] S201. First, keep the transmission power unchanged, and use the successive convex approximation algorithm to optimize the associated configuration and time slot allocation;

[0106] Then, keep the associated configuration unchanged, and use the successive convex approximation algorithm to optimize the transmission power and time slot allocation;

[0107] S202. Repeat S201 until the time slot allocation result converges.

[0108] For S201, first keep the transmission power unchanged, take the associated configuration and time slot allocation as the optimization objects. At this time, it is a non-convex problem - convert it into a convex problem through the successive convex approximation algorithm, and then solve the associated configuration and time slot allocation through the convex problem solver;

[0109] Then, keep the associated configuration unchanged, take the transmission power and time slot allocation as the optimization objects. At this time, it is a non-convex problem - convert it into a convex problem through the successive convex approximation algorithm, and then solve the transmission power and time slot allocation through the convex problem solver.

[0110] In this way, the alternating optimization is realized; each optimization will obtain two time slot allocation results - then it can be determined whether the final time slot allocation result is obtained by examining whether it converges.

[0111] Of course, in S201, the order can also be reversed - first keep the association configuration unchanged and use the successive convex approximation algorithm to optimize the transmission power and time slot allocation; then keep the transmission power unchanged and use the successive convex approximation algorithm to optimize the association configuration and time slot allocation.

[0112] ②. Method 2: Convert the optimization model into a difference-of-convex problem and use the concave-convex optimization algorithm to solve it.

[0113] Specifically, S2 can be designed as:

[0114] For α m,k , perform relaxation processing, and correspondingly introduce the sparsity penalty term R1 and the sparsity penalty term R2 to minT to obtain the objective function minT';

[0115] where α m,k satisfies after relaxation processing:

[0116]

[0117] satisfies after relaxation processing:

[0118]

[0119] In the formula, z m,k is the auxiliary variable introduced when performing relaxation processing on .

[0120] Then, the expressions of R1, R2, and T' are:

[0121] In the formula, λ1 and λ2 are both penalty coefficients.

[0122] S202, randomly initialize α m,k , p m,k , t m , z m,k as the initial solution;

[0123] Use the concave-convex optimization algorithm to perform multiple rounds of iterative solution based on the initial solution until α m,k , p m,k converges.

[0124] Among them, in each round of iterative solution of the concave-convex optimization algorithm, R1 and R2 will be linearized first to construct a convex sub-problem, and then the convex sub-problem solver will be used to solve it to obtain a new solution (still including α m,k , p m,k , tm , z m,k ).

[0125] Since α m,k , p m,k has been relaxed, when they all converge, the remaining solutions must also converge. Then the new solution obtained at this time can be used as the optimal resource allocation scheme.

[0126] It should be noted that in Method 2, α m,k , p m,k , t m , z m,k These variables are updated together in each round, rather than only optimizing two types of variables each time as in Method 1 (only optimizing the association configuration and time slot allocation, or only optimizing the transmission power and time slot allocation). This can retain the global coupling structure between variables and avoid the "information loss" caused by block fixation in alternating optimization.

[0127] Embodiment 3

[0128] This Embodiment 3 discloses a computer device, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the steps of the resource allocation method disclosed in Embodiment 1 are implemented.

[0129] This Embodiment 3 also discloses a readable storage medium. Computer program instructions are stored in the readable storage medium. When the computer program instructions are read and run by a processor, the steps of the resource allocation method disclosed in Embodiment 1 are executed.

[0130] This Embodiment 3 also discloses a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the resource allocation method disclosed in Embodiment 1 are implemented.

[0131] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0132] The above-described embodiments only represent several implementation manners of the present invention. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent should be subject to the appended claims.

Claims

1. A wireless network control system based on MEC and URLLC, which is set in a target control area, is characterized in that, It includes: M base stations, which serve as mobile communication switching centers; each base station adopts massive MIMO technology and is equipped with 1 edge server; and K subsystems, which serve as wireless communication control objects; Any one subsystem is only associated with one base station and conducts data interaction through wireless communication; any one base station supports association with multiple subsystems; Among them, the k-th subsystem includes: the k-th device, the k-th sensor, and the k-th actuator; the k-th sensor is used to upload sensed data to the base station associated with the k-th subsystem; the base station associated with the k-th subsystem is used to take the received sensed data as a computing task, perform calculations through its equipped edge server to generate a control signal, and then download the control signal to the k-th actuator; the k-th actuator is used to control the k-th device according to the control signal; k ∈ [1, K]; Among them, the M base stations adopt a time division multiple access protocol to enable different base stations to perform uplink transmission and downlink transmission in orthogonal time slots; Among them, a working cycle of the wireless network control system is represented as T; In the formula, is the m-th uplink transmission time slot, representing the time taken for the m-th base station and its associated subsystem to complete uplink transmission; is the common computing time slot, representing the overlapping segment of the computing time of M edge servers; t m is the m-th downlink transmission time slot, representing the time taken for the m-th base station and its associated subsystem to complete downlink transmission; m ∈ [1, M].

2. The wireless network control system based on MEC and URLLC according to claim 1, wherein The M-th base station is equipped with N antennas, N > K.

3. A resource allocation method, characterized in that, It is applied to the wireless network control system based on MEC and URLLC as described in claim 1 or 2; The resource allocation method includes: S1, taking the association configuration, transmission power, and time slot allocation as consideration factors, taking the stability constraint as a constraint condition, and taking minimizing T as the objective function minT, to construct an optimization model; S2, solving the optimization model to obtain an optimal resource allocation scheme; Among them, the association configuration includes: the association α between the m-th base station and the k-th subsystem m,k ; The transmission power includes: the uplink transmission power between the m-th base station and the k-th subsystem Downlink transmission power p m ,k ; Time slot allocation includes: the m-th uplink transmission time slot Common computing time slot The m-th downlink transmission time slot t m ; Among them, the stability constraint includes: control stability constraint, computing resource limit constraint, and communication reliability constraint; Among them, the optimization model solving method includes: converting the optimization model into a difference-of-convex problem and using a convex-concave optimization algorithm to solve it.

4. The resource allocation method according to claim 3, wherein α m,k Initially satisfied: Initially satisfied: wherein, represents the uplink transmission power threshold of the k-th subsystem; p m,k Satisfy: In the formula, P m represents the total threshold of the downlink transmission power of the m-th base station.

5. The resource allocation method according to claim 3, wherein The mathematical expression of the control stability constraint is: where Φ k , Υ k , Q k are all real symmetric matrices related to the k-th subsystem; η k represents the convergence rate of the k-th subsystem.

6. The resource allocation method according to claim 3, wherein The mathematical expression of the computing resource limit constraint is: where S k represents the task computing period of the edge server corresponding to the k-th subsystem; represents the number of bits of the computing task corresponding to the sensed data sent by the k-th subsystem; F m represents the maximum computing frequency of the m-th edge server.

7. The resource allocation method according to claim 3, wherein The mathematical expression of the communication reliability constraint is: wherein, represents the uplink transmission interruption probability between the m-th base station and the k-th subsystem; Denote the downlink transmission outage probability between the m-th base station and the k-th subsystem; ε th represents a reliability threshold.

8. The resource allocation method according to claim 4, wherein S2 It includes: S201, perform relaxation processing on α m,k , , and introduce sparsity penalty term one R1 and sparsity penalty term two R2 to minT correspondingly to obtain the objective function minT'; Among them, the expressions of R1, R2, and T' are as follows: where λ1 and λ2 are both penalty coefficients; z m,k is the auxiliary variable introduced when performing relaxation processing on ; S202, randomly initialize α m,k , p m,k , t m , z m,k as the initial solution; Use the convex-concave optimization algorithm to perform multiple rounds of iterative solutions based on the initial solution until α m,k and p m,k converges.

9. The resource allocation method according to claim 8, wherein α m,k After relaxation treatment, it satisfies: After relaxation treatment, it satisfies:

10. A computer program product, characterized in that, It includes a computer program; when the computer program is executed by a processor, it implements the steps of the resource allocation method as described in any one of claims 3-9.

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