Receiving and transmitting cooperation method and system for multi-cluster air computing network

Abstract

The invention discloses a multi-cluster air computing network-oriented transmitting-receiving cooperation method and system, and belongs to the field of air computing in a wireless network, and the method comprises the steps: firstly constructing a multi-cluster air computing scene, and analyzing an expression of an air computing rate and key influence factors of the expression; secondly, decomposing the problem into a receiving beamforming sub-problem and a transmission factor control sub-problem according to physical significance; deriving a closed-form solution expression of the receiving beamforming sub-problem according to the receiving beamforming sub-problem; for the transmission factor control sub-problem, continuous convex approximation is used for solving; then, jointly solving the two sub-problems by adopting an alternating optimization algorithm to obtain an optimal weighting sum rate; finally, system parameters are changed, and the change relation between different parameters and the weighting and calculation rate is studied. The scheme provided by the invention is superior to the existing method in the aspect of calculation rate, and reveals the influence of key parameters on the air calculation performance.

Description

Background Art

[0002] The advent of the sixth generation (6G) of wireless technology has enabled the seamless connection of millions or even billions of wireless devices, driving advances in environmental perception and massive data collection. In such a large network scale, we are not only concerned with the data obtained by each wireless device, but also the results of calculations on the raw data, such as the average of multiple temperature sensor readings, the maximum vehicle speed, or the average of distributed models in federated edge learning. In these scenarios, traditional solutions may not be able to efficiently utilize the spectrum and may introduce additional computing delays. In addition, this disadvantage will become more obvious as the number of devices increases. To solve these problems, an innovative solution called air computing (AirComp) has emerged. This technology allows wireless devices to transmit signals simultaneously, using the superposition characteristics of the channel to perform calculations during transmission, greatly reducing the delays required by traditional solutions during access, transmission, and processing.

[0003] The main goal of air computing systems is signal alignment (SA). Due to the different distances between wireless devices and the aggregation center (FC) and the possibility that some devices may be blocked, the channel conditions from wireless devices to FC are uneven, which poses a major challenge to signal alignment. Many researchers have conducted extensive research on signal alignment. By setting up multiple antennas at the receiving end, the freedom of receiving beamforming can be enhanced, which promotes signal alignment at the receiving end. At the transmitting end, the signal is amplified to different degrees by the transmission factor, which can partially offset the channel fading differences between other wireless devices, which is beneficial to the signal alignment at the receiving end. Some studies have improved the performance of air computing by changing the channel conditions from wireless devices to FC.

[0004] A large number of computing tasks need to be performed within a service area. Various wireless devices are strategically deployed to perform specific aerial computing tasks, including temperature sensing, light intensity calculation, and fire monitoring. Wireless devices and aggregation centers together form an aerial computing cluster, but in this network, interference between clusters is inevitable.

[0005] Currently, the focus is on the problem of single-cluster air computing, and no in-depth consideration is given to the problem of multi-cluster air computing. In multi-cluster air computing, there is strong interference between clusters, it is difficult to align the signals within a cluster, and it is difficult to improve the performance of multi-cluster air computing. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a transceiver coordination method and system for a multi-cluster aerial computing network in response to the above-mentioned deficiencies in the prior art, which maximizes the weighted sum computing rate by jointly designing the transmission factor control and receiving beamforming strategy, thereby solving the technical problem of decreased computing efficiency due to interference in multi-cluster networks.

[0007] The present invention adopts the following technical solutions: A method for transmitting and receiving coordination in a multi-cluster air computing network includes the following steps: S1. Deploy multiple aggregation centers within the service range. Each aggregation center is equipped with an array antenna to provide air computing services for its wireless device group, forming a multi-cluster air computing model. S2, determining a multi-cluster air computing weighted sum rate performance index in the multi-cluster air computing model formed in step S1; S3, decomposing the coupled optimization variables in the multi-cluster air-computation weighted sum rate performance index obtained in step S2 into a receiving beamforming sub-problem and a transmission factor control sub-problem according to the actual physical meaning; S4, analyzing the receive beamforming subproblem obtained in step S3, splitting the overall optimization goal into optimization goals of individual clusters, and obtaining a closed-form expression for the optimal beam of each cluster based on the gradient condition of the optimal solution; S5, analyzing the transfer factor control subproblem obtained in step S3, relaxing the optimization objective, introducing additional auxiliary variables, performing convex approximation on the non-convex constraints, and solving the problem using convex optimization tools; S6. Design an alternating optimization and continuous convex approximation algorithm, repeat steps S4 and S5 until the calculation rate converges, obtain the optimal performance index, and analyze the interference between clusters; S7. Change the network cluster size, the number of antennas, and the number of devices in each cluster, and repeat steps S4, S5, and S6 to analyze the impact of different parameters on rate and interference to achieve rate optimization.

[0008] Preferably, in step S1, the service range includes N clusters, which perform different air computing tasks, and the set of all clusters is ={1, 2, ..., N}; the center of each cluster is equipped with an aggregation center; the signals sent by all wireless devices in cluster i to the kth aggregation center are aggregated The aggregated signals from all clusters are transmitted to the kth aggregation center and accumulated with the noise. After receiving beamforming, the recovered signal is obtained. , taking the sum function as a monotonic function, we can get the signal that the k-th aggregation center expects to receive .

[0009] Preferably, in step S2, the mean square error of the difference between the target calculation function and the actual calculation function is used as part of the performance evaluation index, the mean square error is expressed in the form of calculation rate, the transmission power needs to meet the power constraint condition, and the weighted sum of the calculation rates of each cluster in the air is solved.

[0010] Preferably, the weighted sum of the in-flight calculation rates of each cluster is expressed as:

[0011] in, represents the weight of the k-th rate, Indicates wireless device The transfer factor Indicates wireless device The maximum transmission power, represents the set of all clusters, represents the air computation rate of aggregation center k.

[0012] Preferably, in step S3, after fixing the transmission factor, the receive beamforming subproblem is obtained as follows:

[0013] Determine the optimal receive beamforming vector , optimize the transmission factor separately and get the transmission factor control subproblem:

[0014] in, represents the weight of the k-th rate, Indicates wireless devices, Indicates wireless device The maximum transmission power, represents the set of all clusters, represents the aerial computation rate of the aggregation center k, The optimal number of quantization bits for calculating the function for the aggregation center k, is the number of wireless devices corresponding to the aggregation center k, () function is a piecewise function, (0,1] is 0, (1,+∞) is ()function.

[0015] Preferably, in step S4, the closed-form expression of the optimal beam of each cluster is:

[0016] in, For wireless devices The transfer factor For wireless devices and the channel between aggregation center k, For wireless devices and the conjugate transpose of the channel between the aggregation centers k, is the noise power of the aggregation center k, for x The identity matrix of dimension , is the number of wireless devices corresponding to aggregation center i.

[0017] Preferably, in step S5, additional auxiliary variables are introduced to perform convex approximation processing on the non-convex constraints, specifically:

[0018] in, is the conjugate transpose of the receive beam at aggregation center k, For wireless devices The channel between the aggregation center k, that is, the effective communication channel, For wireless devices The transfer factor is the aggregation center and the number of clusters. is the auxiliary variable corresponding to the aggregation center k, is the inverse of the mean square error, Auxiliary variables The value of the last solution, is the noise power of the aggregation center k, is the receiving beam of the aggregation center k.

[0019] Preferably, in step S6, a joint optimization algorithm for receiving beamforming and transmission factor control is designed; after a random initial beam and an initial transmission factor are given, the optimal receiving beam is solved for each cluster according to step S4, and the mean square error of each cluster is calculated according to the optimal receiving beam, and the auxiliary variable The initial index of According to step S5, the optimal transmission factor is solved, the current minimum mean square error is calculated, and the auxiliary variable is updated. until the change of the auxiliary variable is less than a threshold ε; The weighted sum calculation rate of the current network is calculated according to the optimal beam and transmission factor. If the weighted sum is greater than the threshold δ, continue; otherwise, exit the loop and output the optimal weighted sum calculation rate.

[0020] Preferably, in step S7, firstly, the number of clusters in the network is gradually increased, and the weighted sum under different numbers of clusters is calculated to calculate the rate change trend; Then, the number of antennas equipped at each aggregation center is changed, and the changing trend of the weighted sum calculation rate under different numbers of antennas is calculated; Finally, the number of wireless devices in each cluster is changed, and the changing trend of the weighted and calculated rate under different numbers of wireless devices is calculated.

[0021] In a second aspect, an embodiment of the present invention provides a transceiver coordination system for a multi-cluster air computing network, including: The deployment module deploys multiple aggregation centers in the service range, each aggregation center is equipped with an array antenna to provide air computing services for the wireless device group to which it belongs, forming a multi-cluster air computing model; and determines the weighted and rate performance indicators of the multi-cluster air computing in the multi-cluster air computing model; The problem module decomposes the coupled optimization variables in the multi-cluster air-computation weighted sum rate performance index into the receive beamforming sub-problem and the transmission factor control sub-problem according to the actual physical meaning; The analysis module analyzes the receive beamforming subproblem, splits the overall optimization goal into the optimization goals of a single cluster, and obtains the closed-form expression of the optimal beam of each cluster based on the gradient condition of the optimal solution; analyzes the transmission factor control subproblem, relaxes the optimization goal, introduces additional auxiliary variables, performs convex approximation on non-convex constraints, and uses convex optimization tools to solve the problem; Optimization module, design alternating optimization and continuous convex approximation algorithms, repeat until the calculation rate converges, obtain the optimal performance index, and analyze the interference between clusters; The output module changes the network cluster size, the number of antennas, and the number of devices in each cluster to re-analyze the impact of different parameters on rate and interference to achieve rate optimization.

[0022] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for transmitting and receiving coordination in a multi-cluster air computing network when executing the computer program.

[0023] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for transmitting and receiving coordination in a multi-cluster air computing network.

[0024] In a fifth aspect, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for transmitting and receiving coordination for a multi-cluster air computing network when executing the computer program.

[0025] In a sixth aspect, an embodiment of the present invention provides an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-mentioned method for transmitting and receiving coordination in a multi-cluster air computing network.

[0026] Compared with the prior art, the present invention has at least the following beneficial effects: A method for transmitting and receiving coordination in a multi-cluster aerial computing network is proposed. By adopting a continuous convex approximation scheme, after receiving beamforming, the transmission power of each cluster wireless device is effectively controlled, thereby minimizing the interference between clusters and significantly improving the overall performance of the system. Under different numbers of clusters, the proposed scheme shows a significant computing rate advantage, and as the number of clusters increases, its computing rate continues to increase and eventually stabilizes. In contrast, the rates of the maximum power transmission and adaptive power transmission schemes increase first and then decrease with the increase in the number of clusters, indicating that the proposed scheme can better balance the relationship between computing rate and interference in a complex network environment. When the number of receiving antennas increases, the proposed scheme shows the best rate growth effect. Increasing the number of receiving antennas can improve the beamforming accuracy, focus signal reception, reduce interference, and thus achieve better signal alignment effect and improve the computing rate. In addition, when the number of antennas exceeds a certain threshold, the maximum power transmission scheme performs better than the adaptive power transmission scheme. When the number of wireless devices in each cluster is large, the overall interference of the network increases, and the difficulty of signal alignment increases. At this time, the adaptive power transmission scheme has more significant advantages than the maximum power transmission scheme. It can more effectively reduce interference between clusters and ensure the computing rate in a high-density equipment environment.

[0027] Furthermore, if there are many computing needs in a region, multiple aggregation centers need to be deployed to complete specific wireless device computing tasks. Wireless devices and corresponding aggregation centers form a computing cluster. For multi-cluster scenarios, coordinating the resource scheduling of each cluster is of great significance to reducing cluster-to-cluster interference and improving the overall computing rate of the network.

[0028] Furthermore, the common measurement indicator of air computing is mean square error, which is not intuitive enough. Through mathematical deduction, we can get the rate indicator of air computing, which has good physical meaning. The key indicators that affect air computing are the transmission factor of the transmitter and the beamforming of the receiver. Coordinating the transmission factor of each device can reduce the interference between clusters. Accurate receiving beamforming can align the signals of each wireless device in the cluster and improve the computing rate within the cluster.

[0029] Furthermore, the transmission factor and the receiving beam variable are coupled together and difficult to optimize. Decomposing the original problem into two corresponding sub-problems allows the problem to be solved, and it is in line with the idea of ​​mutual feedback adjustment between the transmitter and the receiver in reality, which is a realistic and feasible solution.

[0030] Furthermore, under the premise of determining the transmission factor, the beamforming of each aggregation center does not affect each other, and analysis can be performed on a single cluster. Mathematical derivation can prove that this subproblem is a convex problem. The closed-form solution of the subproblem can be obtained using the properties of the gradient zero point. The closed-form solution is used to calculate the optimal solution of this subproblem with lower complexity and faster speed.

[0031] Furthermore, under the premise of determining the receiving beam, the transmission factors of the wireless devices between clusters are coupled with each other and the rate function is a piecewise function. Therefore, the objective function is not a simple convex function. By introducing auxiliary variables and relaxing the piecewise function, the objective function becomes a convex function. The introduction of auxiliary functions makes the power constraint more complicated. The continuous convex approximation is used to deal with non-convex constraints, so that the problem can be solved using convex optimization tools.

[0032] Furthermore, the two sub-problems need to be optimized alternately so that the rate gradually approaches the optimal point. Each time the optimal rate is updated, the difference between the previous one and the optimal rate is calculated. When the difference is less than the set threshold, the iteration is stopped to determine a suboptimal solution to the current optimization variable multi-cluster air computing problem.

[0033] Furthermore, different network parameters have different effects on multi-cluster air computing, and the number of clusters, the number of wireless devices in each cluster, and the number of aggregation center antennas are explored to determine the impact on the rate. This helps to set a reasonable set of network parameters and improve the network computing rate.

[0034] It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0035] In summary, the scheme of the present invention outperforms the existing methods in terms of computing rate and reveals the influence of key parameters on the performance of in-flight computing.

[0036] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A diagram of a multi-cluster control computing system model constructed for the present invention; Figure 2 It is a flow chart of the present invention; Figure 3 A schematic diagram of the relationship between the weighted sum calculation rate and the number of clusters in the network according to the present invention; Figure 4 A schematic diagram of a computer device provided by an embodiment of the present invention; Figure 5 The block diagram of a chip provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0040] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0041] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0042] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0043] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0044] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0045] The present invention provides a method for transmitting and receiving coordination in a multi-cluster air computing network, constructs a multi-cluster air computing scenario, analyzes the expression of air computing rate and its key influencing factors; then, decomposes the problem into a receiving beamforming subproblem and a transmission factor control subproblem according to the physical meaning; for the receiving beamforming subproblem, its closed-form solution expression is derived; and for the transmission factor control subproblem, a continuous convex approximation is used to solve it; then, an alternating optimization algorithm is used to jointly solve the above two subproblems to obtain the optimal weighted sum rate; finally, the system parameters are changed to study the changing relationship between different parameters and the weighted sum computing rate; the scheme of the present invention is superior to the existing method in terms of computing rate, and reveals the influence of key parameters on air computing performance.

[0046] See also Figure 2 The present invention provides a method for transmitting and receiving cooperation in a multi-cluster air computing network, comprising the following steps: S1. Multiple aggregation centers are deployed in a service range. Each aggregation center is equipped with an array antenna to provide air computing services for its wireless device group, forming a multi-cluster air computing model. See also Figure 1 The multi-cluster air computing model constructed by the present invention is specifically: Consider an air computing service coverage area, the entire area consists of N clusters, performing different air computing tasks, and the set of all clusters is defined as ={1, 2, ..., N}. The center of each cluster is equipped with a convergence center (FC); For a given cluster i, it contains wireless device, represents the nth wireless device in the cluster, ={1, 2, ..., } represents the set of all wireless devices; Wireless Devices The maximum transmission power is expressed as ; The kth FC is equipped with Antenna, with Indicates wireless device The channel to the kth FC.

[0047] When i equals k, indicates a communication channel; otherwise, it indicates an interference channel; Wireless Devices The transfer factor is expressed as , the receive beamforming of FC is expressed as .

[0048] Each aggregation center (FC) not only receives the signals transmitted by the wireless devices in the cluster it is responsible for, but is also affected by the interference signals of the wireless devices in other clusters and the environmental noise; the signals sent by all wireless devices in cluster i to the kth FC are aggregated as follows:

[0049] The aggregated signals from all clusters are transferred to the kth aggregation center (FC) and accumulated together with the noise. This yields:

[0050] The recovered signal after receiving beamforming is expressed as:

[0051] Taking the sum function as a monotonic function, the signal that the k-th aggregation center (FC) expects to receive is defined as:

[0052] S2, determining the performance indicators that need to be optimized in the multi-cluster air computing scenario formed in step S1, and the key factors that affect the multi-cluster air computing performance; The mean square error (MSE) of the difference between the target calculation function and the actual calculation function is used as part of the performance evaluation index and is expressed as:

[0053] in, is the noise power, and the mean square error is further expressed in the form of calculation rate:

[0054] in, The () function is a piecewise function. When the independent variable is in the interval (0, 1], its value is 0. When the independent variable is in the interval [1, +∞], it degenerates to ()function.

[0055] The power consumption of wireless devices is limited, so the transmission power needs to meet the power constraint conditions:

[0056] What needs to be solved is the weighted sum of the computational rates of each cluster in the air, expressed as:

[0057] in, Represents the weight of the k-th rate.

[0058] S3. The rate function of a single cluster obtained in step S2 is segmented and does not have simple concavity and monotonicity. The transmission factor of each cluster device will affect the air computing rate of the entire network, and the weighted and computing rates cannot be solved directly. The receiving beamforming vector and transfer factor In the mean square error (MSE) calculation, they are coupled with each other and have different physical meanings. The coupled optimization variables are decomposed into the receiving beamforming subproblem and the transmission factor control subproblem according to the actual physical meaning. The receive beamforming is optimized separately using the alternating optimization (AO) method and transfer factor .

[0059] After fixing the transmission factor, we get the receive beamforming subproblem, which is denoted as subproblem 1:

[0060] Once the optimal receive beamforming vector is determined , the transmission factor is optimized separately, thus deriving sub-problem 2:

[0061] S4. Analyze the receive beamforming sub-problem, split the overall optimization goal into the optimization goals of a single cluster, and obtain the closed-form expression of the optimal beam for each cluster based on the gradient condition of the optimal solution; Solve and analyze the receive beamforming subproblem 1 decomposed in step S3. For this subproblem, the mean square error The receive beamforming vector of the cluster itself is determined by the receive beamforming vectors of other clusters. (where i≠k). Optimize the receive beamforming for each cluster separately. Subproblem 1 can be transformed into:

[0062] For the above problem, we only need to minimize the minimum mean square error of the aggregation center k to maximize the sum rate of the entire network. Further analysis yields:

[0063] in, is a size of x The identity matrix, function () indicates selecting the real part, express For the convenience of expression, let

[0064] For any given , both .therefore, is a positive definite Hermitian matrix; when hour, reaches a minimum value. In this case, the optimal It is expressed as:

[0065] S5, solving and analyzing the transmission factor control subproblem decomposed in step S3, relaxing the optimization objective, introducing additional auxiliary variables, and finally performing convex approximation processing on the non-convex constraints, so that the problem can be solved using convex optimization tools; The transmission factor is controlled to achieve the coordination of interference and signals between clusters. () Function pair () function to perform relaxation processing and introduce an auxiliary variable , the control subproblem of the transfer factor is written as

[0066] The target of this problem is a convex function. The first two constraints are also convex constraints. Relaxing the third constraint yields

[0067] This constraint is a convex constraint, and only continuous convex approximation (SCA) is needed to solve subproblem 2 before relaxation.

[0068] S6. Design an alternating optimization and continuous convex approximation algorithm, repeat steps S4 and S5 until the calculation rate converges, obtain the optimal performance index, and analyze the interference between clusters; Design a joint optimization algorithm for receiving beamforming and transmission factor control. Given a random initial beam and initial transmission factor, solve the optimal receiving beam for each cluster according to step S4, calculate the mean square error of each cluster based on the optimal receiving beam, and further calculate the auxiliary variable Then, according to step S5, the optimal transmission factor is solved, the current minimum mean square error is calculated, and the auxiliary variable is updated. The value of is adjusted until the change of the auxiliary variable is less than a threshold ε. The weighted sum calculation rate of the current network is calculated according to the optimal beam and transmission factor. If the weighted sum is greater than a threshold δ, continue the above steps. Otherwise, jump out of the loop and output the optimal weighted sum calculation rate.

[0069] S7. Change the network cluster size, the number of antennas, and the number of devices in each cluster, and repeat steps S4, S5, and S6 to analyze the impact of different parameters on rate and interference.

[0070] Change the system parameters, repeat steps S4, S5, and S6, and analyze the impact of different parameters on the performance indicators of the proposed solution.

[0071] First, the number of clusters in the network is gradually increased, and the weighted and computational rate trends under different numbers of clusters are calculated; Then, the number of antennas equipped at each aggregation center is changed, and the changing trend of the weighted sum calculation rate under different numbers of antennas is calculated; Finally, the number of wireless devices in each cluster is changed, and the changing trend of the weighted and calculated rate under different numbers of wireless devices is calculated.

[0072] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0073] In another embodiment of the present invention, a transceiver coordination system for multi-cluster aerial computing networks is provided. The system can be used to implement the above-mentioned transceiver coordination method for multi-cluster aerial computing networks. Specifically, the transceiver coordination system for multi-cluster aerial computing networks includes a layout module, a problem module, an analysis module, an optimization module and an output module.

[0074] Among them, the deployment module deploys multiple aggregation centers in the service range, each aggregation center is equipped with an array antenna to provide air computing services for the wireless device group to which it belongs, forming a multi-cluster air computing model; determining the multi-cluster air computing weighted sum rate performance index in the multi-cluster air computing model; The problem module decomposes the coupled optimization variables in the multi-cluster air-computation weighted sum rate performance index into the receive beamforming sub-problem and the transmission factor control sub-problem according to the actual physical meaning; The analysis module analyzes the receive beamforming subproblem, splits the overall optimization goal into the optimization goals of a single cluster, and obtains the closed-form expression of the optimal beam of each cluster based on the gradient condition of the optimal solution; analyzes the transmission factor control subproblem, relaxes the optimization goal, introduces additional auxiliary variables, performs convex approximation on non-convex constraints, and uses convex optimization tools to solve the problem; Optimization module, design alternating optimization and continuous convex approximation algorithms, repeat until the calculation rate converges, obtain the optimal performance index, and analyze the interference between clusters; The output module changes the network cluster size, the number of antennas, and the number of devices in each cluster to re-analyze the impact of different parameters on the rate and interference, and achieve rate optimization.

[0075] In another embodiment of the present invention, a terminal device is provided, the terminal device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, graphics processors (GPU), tensor processors (TPU), digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the transceiver coordination method for a multi-cluster air computing network, including: Deploy multiple aggregation centers within the service scope, and each aggregation center is equipped with an array antenna to provide air computing services for its wireless device group, forming a multi-cluster air computing model; determine the weighted sum rate performance index of multi-cluster air computing in the multi-cluster air computing model; decompose the coupled optimization variables in the weighted sum rate performance index of multi-cluster air computing into a receiving beamforming subproblem and a transmission factor control subproblem according to the actual physical meaning; analyze the receiving beamforming subproblem, split the overall optimization goal into the optimization goal of a single cluster, and obtain the closed-form expression of the optimal beam for each cluster based on the gradient condition of the optimal solution; analyze the transmission factor control subproblem, relax the optimization goal, introduce additional auxiliary variables, perform convex approximation on non-convex constraints, and use convex optimization tools to solve the problem; design alternating optimization and continuous convex approximation algorithms, repeat until the computing rate converges, obtain the optimal performance index, and analyze the interference between clusters; change the network cluster size, the number of antennas, and the number of devices in each cluster to re-analyze the impact of different parameters on rate and interference to achieve rate optimization.

[0076] See also Figure 4 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for transmitting and receiving cooperation for a multi-cluster air computing network in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the transmitting and receiving cooperation system for a multi-cluster air computing network in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0077] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 4 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0078] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, graphics processing units (GPU), tensor processing units (TPU), digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0079] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0080] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0081] See also Figure 5 The terminal device is an electronic device 600, which is in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0082] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 2 Follow the steps shown in .

[0083] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0084] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0085] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0086] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0087] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs. It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (Non-VolatileMemory), such as at least one disk memory.

[0088] The processor may load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the above-mentioned embodiment related to the method for transmitting and receiving cooperation for a multi-cluster air computing network; the processor may load and execute the following steps of one or more instructions in the computer-readable storage medium: Deploy multiple aggregation centers within the service scope, and each aggregation center is equipped with an array antenna to provide air computing services for its wireless device group, forming a multi-cluster air computing model; determine the weighted sum rate performance index of multi-cluster air computing in the multi-cluster air computing model; decompose the coupled optimization variables in the weighted sum rate performance index of multi-cluster air computing into a receiving beamforming subproblem and a transmission factor control subproblem according to the actual physical meaning; analyze the receiving beamforming subproblem, split the overall optimization goal into the optimization goal of a single cluster, and obtain the closed-form expression of the optimal beam for each cluster based on the gradient condition of the optimal solution; analyze the transmission factor control subproblem, relax the optimization goal, introduce additional auxiliary variables, perform convex approximation on non-convex constraints, and use convex optimization tools to solve the problem; design alternating optimization and continuous convex approximation algorithms, repeat until the computing rate converges, obtain the optimal performance index, and analyze the interference between clusters; change the network cluster size, the number of antennas, and the number of devices in each cluster to re-analyze the impact of different parameters on rate and interference to achieve rate optimization.

[0089] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0090] See also Figure 3 , a schematic diagram of the relationship between the system weighted sum calculation rate and the number of clusters in the network is given. The calculation rate of the proposed scheme is significantly higher than that of the maximum power transmission scheme and the adaptive power transmission scheme. As the number of clusters increases, the rate of the proposed scheme continues to rise, but the growth rate gradually slows down and eventually stabilizes. When the number of clusters is small, the overall network interference is low, and increasing the number of clusters can improve computing power. However, when the number of clusters exceeds five, the interference between clusters is significant, and the rate increase brought by the addition of new clusters is less than the loss caused by the increase in interference. Some clusters may have a rate of zero due to strong interference. In order to reduce network interference and increase the rate, it is necessary to shut down these clusters. Therefore, the number of clusters that remain open in the network is limited, and the calculation rate tends to be stable.

[0091] Both the maximum power transmission scheme and the adaptive power transmission scheme show a trend that the rate first increases with the increase in the number of clusters, and then decreases. This is due to the balance between the rate increase brought about by the increase in the number of clusters and the increase in interference. When the number of clusters is less than five, the inter-cluster interference is relatively low, and the maximum power transmission scheme can effectively improve the signal-to-noise ratio, thereby improving performance. When the number of clusters reaches or exceeds five, the inter-cluster interference becomes the main factor limiting the rate, so the adaptive transmission scheme is more advantageous in this case.

[0092] In summary, the present invention provides a method and system for transmitting and receiving coordination in a multi-cluster aerial computing network, which uses an alternating optimization method to decompose it into two sub-problems. By deriving the receiving beamforming problem, a closed-form solution expression is obtained, and it is proposed to use a continuous convex approximation method to iteratively solve the transmission factor. Numerical results show that the proposed scheme is significantly superior to the other two schemes in reducing inter-cluster interference and improving the weighted sum calculation rate. Different parameter settings have different effects on performance: increasing the number of antennas can improve the accuracy of receiving beamforming, but an increase in the number of devices in each cluster will hinder signal alignment. When the cluster's carrying limit is exceeded, the emergence of strong interference will hinder the improvement of the aerial computing rate.

[0093] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0094] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0095] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0096] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0097] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0098] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0099] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0100] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0101] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0102] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0103] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for transmitting and receiving coordination in a multi-cluster air computing network, characterized in that: The following steps are involved: S1. Deploy multiple aggregation centers within the service range. Each aggregation center is equipped with an array antenna to provide air computing services for its wireless device group, forming a multi-cluster air computing model. S2, determining a multi-cluster air computing weighted sum rate performance index in the multi-cluster air computing model formed in step S1; S3, decomposing the coupled optimization variables in the multi-cluster air-computation weighted sum rate performance index obtained in step S2 into a receiving beamforming sub-problem and a transmission factor control sub-problem according to the actual physical meaning; S4, analyzing the receive beamforming subproblem obtained in step S3, splitting the overall optimization goal into optimization goals of individual clusters, and obtaining a closed-form expression for the optimal beam of each cluster based on the gradient condition of the optimal solution; S5, analyzing the transfer factor control subproblem obtained in step S3, relaxing the optimization objective, introducing additional auxiliary variables, performing convex approximation on the non-convex constraints, and solving the problem using convex optimization tools; S6. Design an alternating optimization and continuous convex approximation algorithm, repeat steps S4 and S5 until the calculation rate converges, obtain the optimal performance index, and analyze the interference between clusters; S7. Change the network cluster size, the number of antennas, and the number of devices in each cluster, and repeat steps S4, S5, and S6 to analyze the impact of different parameters on rate and interference to achieve rate optimization.

2. The method for transmitting and receiving in coordination with a multi-cluster air computing network according to claim 1, characterized in that: In step S1, the service range includes N clusters, which perform different air computing tasks. The set of all clusters is ={1, 2, ..., N}; the center of each cluster is equipped with an aggregation center; the signals sent by all wireless devices in cluster i to the kth aggregation center are aggregated The aggregated signals from all clusters are transmitted to the kth aggregation center and accumulated with the noise. After receiving beamforming, the recovered signal is obtained. , taking the sum function as a monotonic function, we can get the signal that the k-th aggregation center expects to receive .

3. The method for transmitting and receiving in coordination with a multi-cluster air computing network according to claim 1, characterized in that: In step S2, the mean square error of the difference between the target calculation function and the actual calculation function is used as part of the performance evaluation index, and the mean square error is expressed in the form of calculation rate. The transmission power needs to meet the power constraint condition to solve the weighted sum of the calculation rates of each cluster in the air.

4. The method for transmitting and receiving in coordination with a multi-cluster air computing network according to claim 3, characterized in that: The weighted sum of the computational rates of each cluster in the air is expressed as: in, represents the weight of the k-th rate, Indicates wireless device The transfer factor Indicates wireless device The maximum transmission power, represents the set of all clusters, represents the air computation rate of aggregation center k.

5. The method for transmitting and receiving in coordination with a multi-cluster air computing network according to claim 1, characterized in that: In step S3, after fixing the transmission factor, the receive beamforming subproblem is as follows: Determine the optimal receive beamforming vector , optimize the transmission factor separately and get the transmission factor control subproblem: in, represents the weight of the k-th rate, Indicates wireless devices, Indicates wireless device The maximum transmission power, represents the set of all clusters, represents the air calculation rate of the aggregation center k, The optimal number of quantization bits for calculating the function for the aggregation center k, is the number of wireless devices corresponding to the aggregation center k, () function is a piecewise function, (0,1] is 0, (1,+∞) is ()function.

6. The method for transmitting and receiving in coordination with a multi-cluster air computing network according to claim 1, characterized in that: In step S4, the closed-form expression of the optimal beam of each cluster is: in, For wireless devices The transfer factor For wireless devices and the channel between aggregation center k, For wireless devices and the conjugate transpose of the channel between the aggregation centers k, is the noise power of the aggregation center k, for x The identity matrix of dimension , is the number of wireless devices corresponding to aggregation center i.

7. The method for transmitting and receiving in coordination with a multi-cluster air computing network according to claim 1, characterized in that: In step S5, additional auxiliary variables are introduced to perform convex approximation processing on the non-convex constraints, specifically: in, is the conjugate transpose of the receive beam at aggregation center k, For wireless devices The channel between the aggregation center k, that is, the effective communication channel, For wireless devices The transfer factor is the aggregation center and the number of clusters. is the auxiliary variable corresponding to the aggregation center k, is the inverse of the mean square error, Auxiliary variables The value of the last solution, is the noise power of the aggregation center k, is the receiving beam of the aggregation center k.

8. The method for transmitting and receiving in coordination with a multi-cluster air computing network according to claim 1, characterized in that: In step S6, a joint optimization algorithm for receiving beamforming and transmission factor control is designed; after a random initial beam and an initial transmission factor are given, the optimal receiving beam is solved for each cluster according to step S4, and the mean square error of each cluster is calculated according to the optimal receiving beam, and the auxiliary variable The initial index of According to step S5, the optimal transmission factor is solved, the current minimum mean square error is calculated, and the auxiliary variable is updated. until the change of the auxiliary variable is less than a threshold ε; The weighted sum calculation rate of the current network is calculated according to the optimal beam and transmission factor. If the weighted sum is greater than the threshold δ, continue; otherwise, exit the loop and output the optimal weighted sum calculation rate.

9. The method for transmitting and receiving in coordination with a multi-cluster air computing network according to claim 1, characterized in that: In step S7, first, the number of clusters in the network is gradually increased, and the weighted sum calculation rate change trend under different numbers of clusters is calculated; Then, the number of antennas equipped at each aggregation center is changed, and the changing trend of the weighted sum calculation rate under different numbers of antennas is calculated; Finally, the number of wireless devices in each cluster is changed, and the changing trend of the weighted and calculated rate under different numbers of wireless devices is calculated.

10. A transceiver coordination system for a multi-cluster air computing network, characterized in that: include: The deployment module deploys multiple aggregation centers in the service range. Each aggregation center is equipped with an array antenna to provide air computing services for the wireless device group to which it belongs, forming a multi-cluster air computing model. Determine a multi-cluster air computing weighted sum rate performance index in a multi-cluster air computing model; The problem module decomposes the coupled optimization variables in the multi-cluster air-computation weighted sum rate performance index into the receive beamforming sub-problem and the transmission factor control sub-problem according to the actual physical meaning; The analysis module analyzes the receive beamforming subproblem, splits the overall optimization goal into the optimization goals of a single cluster, and obtains the closed-form expression of the optimal beam of each cluster based on the gradient condition of the optimal solution; analyzes the transmission factor control subproblem, relaxes the optimization goal, introduces additional auxiliary variables, performs convex approximation on non-convex constraints, and uses convex optimization tools to solve the problem; Optimization module, design alternating optimization and continuous convex approximation algorithms, repeat until the calculation rate converges, obtain the optimal performance index, and analyze the interference between clusters; The output module changes the network cluster size, the number of antennas, and the number of devices in each cluster to re-analyze the impact of different parameters on rate and interference to achieve rate optimization.

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