A method and system for collaborative transmission and reception of multi-cluster air computing networks

Abstract

This invention discloses a method and system for collaborative transmission and reception in a multi-cluster airborne computing network, belonging to the field of airborne computing in wireless networks. First, a multi-cluster airborne computing scenario is constructed, and the expression of the airborne computing rate and its key influencing factors are analyzed. Next, the problem is decomposed into a receive beamforming subproblem and a transmission factor control subproblem based on physical meaning. A closed-form solution expression is derived for the receive beamforming subproblem, while a continuous convex approximation is used to solve the transmission factor control subproblem. An alternating optimization algorithm is then used to jointly solve the two subproblems to obtain the optimal weighted sum rate. Finally, system parameters are varied to study the relationship between different parameters and the weighted sum computing rate. The invention's solution outperforms existing methods in terms of computing rate and reveals the impact of key parameters on airborne computing performance.

Description

Technical Field

[0001] The present invention belongs to the technical field of air computing (AirComp) in wireless networks, and in particular relates to a method and system for collaborative transmission and reception in a multi-cluster air computing network. Background Art

[0002] The advent of sixth-generation (6G) wireless technology has enabled seamless connectivity for millions, or even billions, of wireless devices, driving advances in environmental perception and massive data collection. In such massive networks, we are interested not only in the data captured by individual wireless devices but also in the computational results of raw data, such as the average of multiple temperature sensor readings, maximum vehicle speed, or the average of distributed models in federated edge learning. In these scenarios, traditional approaches may not utilize spectrum efficiently and may introduce additional computational latency. Furthermore, this disadvantage becomes more pronounced as the number of devices increases. To address these issues, an innovative approach called over-the-air computing (AirComp) has emerged. This technology allows wireless devices to transmit signals simultaneously, leveraging the superposition properties of the channel to perform computations during transmission, significantly reducing the latency required by traditional approaches during access, transmission, and processing.

[0003] A primary goal of over-the-air computing systems is signal alignment (SA). Due to the varying distances between wireless devices and the aggregation center (FC), and the potential for some devices to be obscured, the channel conditions from wireless devices to the FC are uneven, posing a significant challenge to signal alignment. Numerous researchers have conducted extensive research on signal alignment. By deploying multiple antennas at the receiver, the freedom of receive beamforming can be enhanced, facilitating signal alignment at the receiver. At the transmitter, varying degrees of signal amplification using transmission factors can partially offset differences in channel fading between wireless devices, facilitating signal alignment at the receiver. Some research has improved the performance of over-the-air computing by varying the channel conditions from wireless devices to the FC.

[0004] Numerous computing tasks are required within a service area. Various wireless devices are strategically deployed to perform specific aerial computing tasks, including temperature sensing, light intensity calculation, and fire detection. These wireless devices and aggregation centers together form an aerial computing cluster. However, in this network, inter-cluster interference is inevitable.

[0005] Currently, the focus is on single-cluster over-the-air computing, without in-depth consideration of multi-cluster over-the-air computing. In multi-cluster over-the-air computing, strong interference exists between clusters, intra-cluster signal alignment is difficult, and multi-cluster over-the-air computing performance is difficult to improve. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to address the deficiencies in the above-mentioned existing technologies and provide a method and system for transmitting and receiving collaboration in a multi-cluster aerial computing network. By jointly designing transmission factor control and receiving beamforming strategies, the weighted sum computing rate is maximized, 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:

[0008] A method for transmitting and receiving coordination in a multi-cluster air computing network includes the following steps:

[0009] S1. Deploy multiple aggregation centers within the service area. Each aggregation center is equipped with an array antenna to provide air computing services to its wireless device cluster, forming a multi-cluster air computing model.

[0010] S2. Determine a multi-cluster air computing weighted sum rate performance index in the multi-cluster air computing model formed in step S1;

[0011] S3. Decomposing the coupled optimization variables in the multi-cluster air-computation weighted sum rate performance index obtained in step S2 into a receive beamforming subproblem and a transmission factor control subproblem based on actual physical meanings;

[0012] S4. Analyze the receive beamforming subproblem obtained in step S3, split the overall optimization objective into optimization objectives for individual clusters, and obtain a closed-form expression for the optimal beam for each cluster based on the gradient condition of the optimal solution.

[0013] S5. Analyze the transfer factor control subproblem obtained in step S3, relax the optimization objective, introduce additional auxiliary variables, perform convex approximation on the non-convex constraints, and solve the problem using convex optimization tools;

[0014] 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;

[0015] 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.

[0016] 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 .

[0017] 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.

[0018] Preferably, the weighted sum of the in-flight computation rates of each cluster is expressed as:

[0019]

[0020] in, represents the weight of the k-th rate, Indicates wireless devices The transfer factor, Indicates wireless devices The maximum transmission power, represents the set of all clusters, represents the air computation rate of aggregation center k.

[0021] Preferably, in step S3, after fixing the transmission factor, the receive beamforming subproblem is obtained as follows:

[0022]

[0023] Determine the optimal receive beamforming vector , optimize the transmission factor separately and obtain the transmission factor control subproblem:

[0024]

[0025] in, represents the weight of the k-th rate, Indicates wireless devices, Indicates wireless devices The maximum transmission power, represents the set of all clusters, represents the air computing rate of the aggregation center k, The optimal quantization bit number 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.

[0026] Preferably, in step S4, the closed-form expression of the optimal beam of each cluster is:

[0027]

[0028] 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.

[0029] Preferably, in step S5, additional auxiliary variables are introduced to perform convex approximation processing on the non-convex constraint, specifically:

[0030]

[0031] 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.

[0032] 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 based on the optimal receiving beam, and the auxiliary variable is calculated. The initial index of

[0033] 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;

[0034] Calculate the weighted sum calculation rate of the current network based on 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.

[0035] Preferably, in step S7, the number of clusters in the network is gradually increased, and the weighted sum of the calculation rate change trends under different numbers of clusters is calculated;

[0036] Then, we change the number of antennas at each aggregation center and calculate the trend of the weighted sum calculation rate under different numbers of antennas.

[0037] Finally, the number of wireless devices in each cluster is changed, and the trend of weighted and calculated rates under different numbers of wireless devices is calculated.

[0038] In a second aspect, an embodiment of the present invention provides a transceiver coordination system for a multi-cluster air computing network, including:

[0039] The deployment module deploys multiple aggregation centers within the service area. Each aggregation center is equipped with an array antenna to provide air computing services to its wireless device cluster, forming a multi-cluster air computing model. The weighted sum rate performance index of the multi-cluster air computing in the multi-cluster air computing model is determined.

[0040] The problem module decomposes the coupled optimization variables in the multi-cluster air-based weighted sum rate performance index into a receive beamforming subproblem and a transmission factor control subproblem based on the actual physical meaning;

[0041] The analysis module analyzes the receive beamforming subproblem, splits the overall optimization objective into optimization objectives for individual clusters, and derives a closed-form expression for the optimal beam for each cluster based on the gradient conditions of the optimal solution. The module also analyzes the transmission factor control subproblem, relaxes the optimization objective, introduces additional auxiliary variables, performs convex approximation on non-convex constraints, and solves the problem using convex optimization tools.

[0042] The optimization module designs alternating optimization and continuous convex approximation algorithms, repeats until the computation rate converges, obtains the optimal performance indicators, and analyzes the interference between clusters.

[0043] The output module changes the network cluster size, number of antennas, and number of devices in each cluster to reanalyze the impact of different parameters on rate and interference to achieve rate optimization.

[0044] 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 collaboration in a multi-cluster air computing network when executing the computer program.

[0045] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, comprising 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.

[0046] In a fifth aspect, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for transmitting and receiving collaboration for a multi-cluster air computing network are implemented.

[0047] In a sixth aspect, an embodiment of the present invention provides an electronic device, comprising a computer program, which, when executed by the electronic device, implements the steps of the above-mentioned method for coordinated transmission and reception in a multi-cluster air computing network.

[0048] Compared with the prior art, the present invention has at least the following beneficial effects:

[0049] A transmit-receive coordination method for multi-cluster airborne computing networks employs a continuous convex approximation scheme to effectively control the transmit power of each cluster's wireless devices after receive beamforming, minimizing inter-cluster interference and significantly improving overall system performance. The proposed scheme demonstrates significant computational rate advantages across various cluster numbers, and its computational rate continues to increase and eventually stabilizes as the number of clusters increases. In contrast, the rates of the maximum power transmission and adaptive power transmission schemes initially increase and then decrease with the number of clusters, demonstrating that the proposed schemes can better balance computational rate and interference in complex network environments. The proposed scheme achieves optimal rate growth when the number of receive antennas increases. Adding more receive antennas improves beamforming accuracy, focuses signal reception, and reduces interference, resulting in better signal alignment and improved computational rate. Furthermore, when the number of antennas exceeds a certain threshold, the maximum power transmission scheme outperforms the adaptive power transmission scheme. When the number of wireless devices in each cluster is large, overall network interference increases, making signal alignment more difficult. 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.

[0050] Furthermore, a region with numerous computing needs requires the deployment of multiple aggregation centers to complete specific wireless device computing tasks. Wireless devices and their corresponding aggregation centers form a computing cluster. In multi-cluster scenarios, coordinating resource scheduling within each cluster is crucial for reducing inter-cluster interference and improving the overall network computing rate.

[0051] Furthermore, the commonly used metric for over-the-air computing is mean squared error (MSE), which is not intuitive. However, through mathematical derivation, we can derive the over-the-air computing rate metric, which has good physical significance. Key indicators influencing over-the-air computing are the transmission factor of the transmitter and the beamforming of the receiver. Coordinating the transmission factor of each device can reduce interference between clusters, while precise receive beamforming can align the signals of each wireless device within a cluster, improving the computing rate within the cluster.

[0052] Furthermore, the transmission factor and the receive beam variables are coupled together and difficult to optimize. Decomposing the original problem into two corresponding sub-problems allows the problem to be solved, and is consistent with the idea of ​​mutual feedback adjustment between the transmitter and the receiver in reality, making it a realistic and feasible solution.

[0053] 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 proves that this subproblem is convex, and the closed-form solution of the subproblem can be obtained using the properties of the gradient zero point. Using the closed-form solution to calculate the optimal solution of this subproblem has low complexity and high speed.

[0054] Furthermore, given the fixed receive beams, the transmission factors of inter-cluster wireless devices are coupled, 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 convex. The introduction of auxiliary functions further complicates the power constraints. Using continuous convex approximation to address non-convex constraints allows the problem to be solved using convex optimization tools.

[0055] Furthermore, the two subproblems 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 and the current optimal rate is calculated. When the difference is less than a set threshold, the iteration stops and a suboptimal solution is determined for the current optimization variable multi-cluster air computing problem.

[0056] Furthermore, different network parameters have different effects on multi-cluster over-the-air computation. We explored the impact of the number of clusters, the number of wireless devices per cluster, and the number of antennas at the aggregation center on the rate. This helps us set a reasonable set of network parameters and improve the network computation rate.

[0057] 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.

[0058] In summary, the solution of the present invention outperforms the existing methods in terms of computing rate and reveals the impact of key parameters on the performance of in-flight computing.

[0059] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A diagram of a multi-cluster control computing system model constructed for the present invention;

[0061] Figure 2 Flowchart of the present invention;

[0062] Figure 3 This is a schematic diagram of the relationship between the weighted sum calculation rate and the number of clusters in the network;

[0063] Figure 4 A schematic diagram of a computer device provided in accordance with an embodiment of the present invention;

[0064] Figure 5 The block diagram of a chip provided according to one embodiment of the present invention is shown. DETAILED DESCRIPTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0066] In the description of the present invention, it is to be understood that the terms “include” and “comprise” indicate the presence of the 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.

[0067] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0068] It should be further understood that the term "and / or" as used in the present specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0069] It should be understood that although the terms "first," "second," and "third" may be used to describe preset ranges in embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are merely used to distinguish one preset range from another. For example, without departing from the scope of 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.

[0070] 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 the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0071] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0072] The present invention provides a method for transmitting and receiving collaboration in a multi-cluster airborne computing network. A multi-cluster airborne computing scenario is constructed, and the expression of the airborne computing rate and its key influencing factors are analyzed. Then, the problem is decomposed into a receive beamforming subproblem and a transmission factor control subproblem according to the physical meaning. For the receive beamforming subproblem, its closed-form solution expression is derived. 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 solution of the present invention outperforms existing methods in terms of computing rate and reveals the influence of key parameters on airborne computing performance.

[0073] See also Figure 2 The present invention provides a collaborative method for transmitting and receiving data in a multi-cluster air computing network, comprising the following steps:

[0074] S1. Deploy multiple aggregation centers within a service area. Each aggregation center is equipped with an array antenna to provide air computing services to its wireless device cluster, forming a multi-cluster air computing model.

[0075] See also Figure 1The multi-cluster air computing model constructed by the present invention is specifically as follows:

[0076] 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);

[0077] For a given cluster i, it contains Wireless devices, using represents the nth wireless device in the cluster, ={1, 2, ..., } represents the set of all wireless devices;

[0078] Wireless devices The maximum transmission power is expressed as ; The kth FC is equipped with Antenna, with Indicates wireless devices The channel to the kth FC.

[0079] When i equals k, Indicates a communication channel; otherwise, it indicates an interference channel;

[0080] Wireless devices The transfer factor is expressed as , the receive beamforming of FC is expressed as .

[0081] Each aggregation center (FC) not only receives the signals transmitted by the wireless devices in its cluster, but is also affected by the interference signals of 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:

[0082]

[0083] The aggregated signals from all clusters are transferred to the kth aggregation center (FC) and accumulated together with the noise. This yields:

[0084]

[0085] The recovered signal after receiving beamforming is expressed as:

[0086]

[0087] Taking the sum function as a monotonic function, the signal that the k-th aggregation center (FC) expects to receive is defined as:

[0088]

[0089] S2. Determine the performance indicators that need to be optimized in the multi-cluster air computing scenario formed in step S1, as well as the key factors that affect the performance of the multi-cluster air computing;

[0090] 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:

[0091]

[0092] in, is the noise power, and the mean square error is further expressed in the form of calculation rate:

[0093]

[0094] in, () function is a piecewise function. When the independent variable is in the interval (0, 1), the value is 0. When the independent variable is in the interval [1, +∞], it degenerates to ()function.

[0095] The power consumption of wireless devices is limited, so the transmission power must meet the power constraints:

[0096]

[0097] What needs to be solved is the weighted sum of the computational rates of each cluster in the air, which can be expressed as:

[0098]

[0099] in, represents the weight of the k-th rate.

[0100] S3. The rate function of a single cluster obtained in step S2 is segmented and does not have simple concavity and convexity and monotonicity. The transmission factor of each cluster device will affect the air computing rate of the entire network, and the weighted sum computing rate cannot be solved directly. The receiving beamforming vector and transfer factor In the mean square error (MSE) calculation, the coupled optimization variables have different physical meanings and are decomposed into the receive beamforming subproblem and the transmission factor control subproblem according to their actual physical meanings.

[0101] Alternating Optimization (AO) method is used to optimize the receive beamforming separately and transfer factor .

[0102] After fixing the transmission factor, we get the receive beamforming subproblem, which is denoted as subproblem 1:

[0103]

[0104] Once the optimal receive beamforming vector is determined , we optimize the transmission factor separately, which leads to sub-problem 2:

[0105]

[0106] S4. Analyze the receive beamforming subproblem and split the overall optimization objective into the optimization objectives of individual clusters. Based on the gradient conditions of the optimal solution, obtain the closed-form expression for the optimal beam of each cluster.

[0107] 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:

[0108]

[0109] 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 shows:

[0110]

[0111] in, is a size of x The identity matrix, function () indicates the selection of the real part, express For the convenience of expression, let

[0112]

[0113] For any given , both .therefore, is a positive definite Hermitian matrix; when hour, reaches a minimum. In this case, the optimal Expressed as:

[0114]

[0115] S5. Solve and analyze the transmission factor control subproblem decomposed in step S3, relax the optimization objective, introduce additional auxiliary variables, and finally perform convex approximation on the non-convex constraints so that the problem can be solved using convex optimization tools;

[0116] The transmission factor is controlled to achieve the coordination of interference and signal between clusters. () function pair () function performs relaxation processing and introduces an auxiliary variable , the transmission factor control subproblem is written as

[0117]

[0118] The target of this problem is a convex function. The first two constraints are also convex constraints. Relaxing the third constraint yields

[0119]

[0120] This constraint is a convex constraint, and only continuous convex approximation (SCA) is needed to solve subproblem 2 before relaxation.

[0121] 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;

[0122] Design a joint optimization algorithm for receiving beamforming and transmission factor control. After a random initial beam and initial transmission factor are given, the optimal receiving beam is solved for each cluster according to step S4. The mean square error of each cluster is calculated based on the optimal receiving beam, and the auxiliary variable is further calculated. 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 changed until the change of the auxiliary variable is less than a threshold ε. The weighted sum calculation rate of the current network is calculated based on the optimal beam and transmission factor. If the weighted sum is greater than a threshold δ, continue the above steps. Otherwise, exit the loop and output the optimal weighted sum calculation rate.

[0123] 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.

[0124] 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.

[0125] First, gradually increase the number of clusters in the network and calculate the trend of weighted sum calculation rate under different numbers of clusters;

[0126] Then, we change the number of antennas at each aggregation center and calculate the trend of the weighted sum calculation rate under different numbers of antennas.

[0127] Finally, the number of wireless devices in each cluster is changed, and the trend of weighted and calculated rates under different numbers of wireless devices is calculated.

[0128] Those skilled in the art will appreciate that various aspects of the present invention may be implemented as systems, methods, or program products. Accordingly, various aspects of the present invention may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, microcode, etc.), or in a combination of hardware and software, collectively referred to herein as "circuits," "modules," or "platforms."

[0129] 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.

[0130] The deployment module deploys multiple aggregation centers within the service range. Each aggregation center is equipped with an array antenna to provide air computing services to its wireless device cluster, forming a multi-cluster air computing model. The weighted sum rate performance index of the multi-cluster air computing in the multi-cluster air computing model is determined.

[0131] The problem module decomposes the coupled optimization variables in the multi-cluster air-based weighted sum rate performance index into a receive beamforming subproblem and a transmission factor control subproblem based on the actual physical meaning;

[0132] The analysis module analyzes the receive beamforming subproblem, splits the overall optimization objective into optimization objectives for individual clusters, and derives a closed-form expression for the optimal beam for each cluster based on the gradient conditions of the optimal solution. The module also analyzes the transmission factor control subproblem, relaxes the optimization objective, introduces additional auxiliary variables, performs convex approximation on non-convex constraints, and solves the problem using convex optimization tools.

[0133] The optimization module designs alternating optimization and continuous convex approximation algorithms, repeats until the computation rate converges, obtains the optimal performance indicators, and analyzes the interference between clusters.

[0134] The output module changes the network cluster size, number of antennas, and number of devices in each cluster to re-analyze the impact of different parameters on rate and interference, thereby achieving rate optimization.

[0135] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein 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 may be other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; 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:

[0136] Multiple aggregation centers are deployed 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; the weighted sum rate performance index of multi-cluster air computing in the multi-cluster air computing model is determined; the coupled optimization variables in the weighted sum rate performance index of multi-cluster air computing are decomposed into a receiving beamforming subproblem and a transmission factor control subproblem according to the actual physical meaning; the receiving beamforming subproblem is analyzed, and the overall optimization goal is split into the optimization goal of a single cluster. According to the gradient condition of the optimal solution, the closed-form expression of the optimal beam of each cluster is obtained; the transmission factor control subproblem is analyzed, the optimization goal is relaxed, additional auxiliary variables are introduced, non-convex constraints are convexly approximated, and the problem is solved using convex optimization tools; alternating optimization and continuous convex approximation algorithms are designed, and repeated until the computing rate converges to obtain the optimal performance index, and the interference between clusters is analyzed; the network cluster size, the number of antennas, and the number of devices in each cluster are changed to re-analyze the impact of different parameters on rate and interference to achieve rate optimization.

[0137] See also Figure 4The terminal device is a computer device. 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, it implements the method for transmitting and receiving coordination for a multi-cluster air computing network in the embodiment. To avoid repetition, it is not described in detail here. Alternatively, when the computer program 63 is executed by the processor 61, it implements the functions of each model / unit in the transmitting and receiving coordination system for a multi-cluster air computing network in the embodiment. To avoid repetition, it is not described in detail here.

[0138] The computer device 60 may be a desktop computer, a notebook computer, a PDA, a cloud server, or other computing devices. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. It will be understood by those skilled in the art that Figure 4 This is merely an example of the computer device 60 and does not constitute a limitation of the computer device 60 . The computer device 60 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.

[0139] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, a graphics processing unit (GPU), a tensor processing unit (TPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0140] 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.

[0141] 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 about to be output.

[0142] See also Figure 5 The terminal device is an electronic device 600, which is implemented as 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), and a display unit 640.

[0143] 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 .

[0144] 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 .

[0145] 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.

[0146] Bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0147] The electronic device 600 can also communicate with one or more external devices 700 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and / or any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication can occur via an input / output (I / O) interface 650. Furthermore, the electronic device 600 can 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 can communicate with other modules of the electronic device 600 via the bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can 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.

[0148] In another embodiment of the present invention, the present invention further 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, of course, 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. 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-Volatile Memory), such as at least one disk memory.

[0149] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the transmission and reception coordination method for a multi-cluster air computing network in the above embodiment. The processor may load and execute the following steps:

[0150] Multiple aggregation centers are deployed 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; the weighted sum rate performance index of multi-cluster air computing in the multi-cluster air computing model is determined; the coupled optimization variables in the weighted sum rate performance index of multi-cluster air computing are decomposed into a receiving beamforming subproblem and a transmission factor control subproblem according to the actual physical meaning; the receiving beamforming subproblem is analyzed, and the overall optimization goal is split into the optimization goal of a single cluster. According to the gradient condition of the optimal solution, the closed-form expression of the optimal beam of each cluster is obtained; the transmission factor control subproblem is analyzed, the optimization goal is relaxed, additional auxiliary variables are introduced, non-convex constraints are convexly approximated, and the problem is solved using convex optimization tools; alternating optimization and continuous convex approximation algorithms are designed, and repeated until the computing rate converges to obtain the optimal performance index, and the interference between clusters is analyzed; the network cluster size, the number of antennas, and the number of devices in each cluster are changed to re-analyze the impact of different parameters on rate and interference to achieve rate optimization.

[0151] 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, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can 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 of 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.

[0152] See also Figure 3 , a schematic diagram of the relationship between the system weighted sum computing rate and the number of clusters in the network is given. The computing 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 inter-cluster interference is significant, and the rate increase brought by the addition of 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 computing rate tends to be stable.

[0153] Both the maximum power transmission scheme and the adaptive power transmission scheme show a trend in which the rate initially increases with the number of clusters, followed by a decrease. This is due to the trade-off 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, inter-cluster interference is relatively low, and the maximum power transmission scheme effectively improves the signal-to-noise ratio, thereby enhancing performance. However, when the number of clusters reaches or exceeds five, inter-cluster interference becomes the primary factor limiting the rate, making the adaptive transmission scheme more advantageous in this situation.

[0154] In summary, the present invention provides a method and system for transmitting and receiving coordination in a multi-cluster airborne computing network, which uses an alternating optimization method to decompose it into two sub-problems. By deriving the receive beamforming problem, a closed-form solution expression is obtained, and a continuous convex approximation method is proposed 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 receive beamforming, but an increase in the number of devices in each cluster will hinder signal alignment. When the cluster's carrying capacity limit is exceeded, the emergence of strong interference will hinder the improvement of the airborne computing rate.

[0155] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and 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 into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into 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, and will not be repeated here.

[0156] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0157] 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. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0158] 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 merely illustrative. For example, the division of the modules or units is merely a logical functional division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, and can be electrical, mechanical, or other forms.

[0159] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0160] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0161] 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 process 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. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. 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, USB flash drive, mobile hard disk, magnetic 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.

[0162] 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 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 produce 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.

[0163] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0164] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0165] The above content is 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 collaborative transmission and reception in a multi-cluster air computing network, characterized in that: The following steps are involved: S1. Deploy multiple aggregation centers within the service area. Each aggregation center is equipped with an array antenna to provide air computing services to its wireless device cluster, forming a multi-cluster air computing model. S2. Determine 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 receive beamforming subproblem and a transmission factor control subproblem based on actual physical meanings; S4. Analyze the receive beamforming subproblem obtained in step S3, split the overall optimization objective into optimization objectives for individual clusters, and obtain a closed-form expression for the optimal beam for each cluster based on the gradient condition of the optimal solution. S5. Analyze the transfer factor control subproblem obtained in step S3, relax the optimization objective, introduce additional auxiliary variables, perform convex approximation on the non-convex constraints, and solve 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 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 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. 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 a multi-cluster air computing network according to claim 3, characterized in that: The weighted sum of the air computing rates of each cluster is expressed as: in, represents the weight of the k-th rate, Indicates wireless devices The transfer factor, Indicates wireless devices 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 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 obtain the transmission factor control subproblem: in, represents the weight of the k-th rate, Indicates wireless devices, Indicates wireless devices The maximum transmission power, represents the set of all clusters, represents the air computing rate of the aggregation center k, The optimal quantization bit number 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 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 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 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 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 initial transmission factor are given, the optimal receiving beam is solved for each cluster according to step S4. The mean square error of each cluster is calculated based on the optimal receiving beam, and the auxiliary variable is calculated. 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; Calculate the weighted sum calculation rate of the current network based on 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 a multi-cluster air computing network according to claim 1, characterized in that: In step S7, 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, we change the number of antennas at each aggregation center and calculate the trend of the weighted sum calculation rate under different numbers of antennas. Finally, the number of wireless devices in each cluster is changed, and the trend of weighted and calculated rates under different numbers of wireless devices is calculated.

10. A transceiver coordination system for multi-cluster air computing networks, characterized in that: include: The deployment module deploys multiple aggregation centers within the service area. Each aggregation center is equipped with an array antenna to provide air computing services for its wireless device cluster, 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; The problem module decomposes the coupled optimization variables in the multi-cluster air-based weighted sum rate performance index into a receive beamforming subproblem and a transmission factor control subproblem based on the actual physical meaning; The analysis module analyzes the receive beamforming subproblem, splits the overall optimization objective into optimization objectives for individual clusters, and derives a closed-form expression for the optimal beam for each cluster based on the gradient conditions of the optimal solution. The module also analyzes the transmission factor control subproblem, relaxes the optimization objective, introduces additional auxiliary variables, performs convex approximation on non-convex constraints, and solves the problem using convex optimization tools. The optimization module designs alternating optimization and continuous convex approximation algorithms, repeats until the computation rate converges, obtains the optimal performance indicators, and analyzes the interference between clusters. The output module changes the network cluster size, number of antennas, and number of devices in each cluster to reanalyze the impact of different parameters on rate and interference to achieve rate optimization.

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