Beam forming design method of metasurface enabling communication calculation integrated system

Abstract

The invention relates to the technical field of wireless communication and air computing, in particular to a beamforming design method of a metasurface enabling communication computing integrated system, which comprises the following steps of: constructing an air computing mutual information measurement index of a target integrated system so as to evaluate the similarity measurement of actual aggregated data of a central node and an ideal result; establishing a mutual information optimization problem with similarity measurement maximization as a target according to constraint conditions of different center and edge nodes; and iteratively solving the mutual information optimization problem by adopting a multi-convex relaxation and alternating optimization strategy to obtain a beam forming deployment result. Therefore, the problems of high communication computing resource overhead, signaling redundancy caused by the influence of channel fading on the performance of a traditional system, low computing efficiency, insufficient aggregation precision, high joint optimization difficulty and the like existing in a traditional air computing and beam forming scheme under the scene that massive terminal devices are accessed can be effectively solved.

Description

Technical Field

[0001] This invention relates to the fields of wireless communication and over-the-air computing, and in particular to a beamforming design method for a metasurface-enabled integrated communication and computing system. Background Technology

[0002] With the development of the Internet of Things and edge computing, the explosive growth of massive amounts of terminal data has made efficient aggregation and real-time processing a core challenge for wireless communication. The traditional "communication first, computing later" model suffers from high transmission latency and high resource consumption, and is prone to channel congestion in densely populated device scenarios, making it difficult to meet real-time requirements.

[0003] In-flight computing leverages the superposition properties of electromagnetic waves to aggregate data during transmission, significantly reducing latency. However, bottlenecks remain: wireless channel fading leads to unstable transmission quality, affecting aggregation accuracy; and the mean square error (MSE) metric used in traditional beamforming has poor adaptability in complex scenarios, making it difficult to coordinate and optimize communication and computing performance. Intelligent metasurfaces can construct controllable multipath environments, overcoming traditional channel limitations. Theoretically, they can enhance link coverage and quality in densely populated scenarios, making them a key carrier for achieving convergence of communication, computing, and information.

[0004] However, the practical application of intelligent metasurface-enabled aerial computing faces key technical challenges: First, the joint optimization of beamforming and metasurface reflection coefficients is a non-convex problem, making the solution process complex and accuracy difficult to guarantee. Second, in scenarios with massive device access, the distributed control mode generates massive redundant signaling—users independently perceive channels, design parameters, and provide feedback, requiring repeated calibration of the link coupling effect between devices and metasurfaces. This consumes valuable communication resources and significantly increases the server's parsing and computational burden, resulting in a double loss of efficiency. Signaling delays also exacerbate channel time-varying errors, creating a vicious cycle of "high signaling overhead—low efficiency—performance degradation." Therefore, developing an integrated computing system based on intelligent metasurfaces and corresponding beamforming design methods to overcome these bottlenecks is of great significance. Summary of the Invention

[0005] This invention provides a beamforming design method for a metasurface-enabled integrated communication and computing system, which solves the problems of high communication and computing resource overhead, excessive signaling redundancy due to channel fading affecting the performance of traditional in-flight computing and beamforming schemes, low computational efficiency, insufficient aggregation accuracy, and difficulty in joint optimization.

[0006] A first aspect of this invention provides a beamforming design method for a metasurface-enabled integrated communication and computing system, comprising the following steps: Construct an aerial computing mutual information metric for the target integration system to evaluate the similarity between the actual aggregated data of the central node and the ideal result; Based on the constraints of different center and edge nodes, a mutual information optimization problem is established with the goal of maximizing the similarity metric. The mutual information optimization problem is solved iteratively using a multi-convex relaxation and alternating optimization strategy to obtain the beamforming deployment results.

[0007] Optionally, the constraints include server-side power constraints, edge device-side power constraints, and constant mode constraints on the reflectivity of the smart metasurface.

[0008] Optionally, the step of iteratively solving the mutual information optimization problem using a multiple convex relaxation and alternating optimization strategy to obtain the beamforming deployment result includes: The mutual information optimization problem is designed with multiple convex relaxation and matrix decomposition to obtain the relaxed convex optimization problem; An alternating optimization strategy is used to iteratively optimize the edge device precoding matrix, metasurface reflection coefficient, and center node combiner in the relaxed convex optimization problem to obtain the beamforming deployment result, wherein the beamforming deployment result includes the optimal edge device side power and the optimal smart metasurface reflection coefficient.

[0009] Optionally, the step of employing an alternating optimization strategy to iteratively optimize the edge device precoding matrix, metasurface reflection coefficient, and center node combiner in the relaxed convex optimization problem to obtain the beamforming deployment result includes: Based on the relaxed convex optimization problem, the metasurface reflection coefficient is fixed, and the pre-encoder of each edge device and the central node is iteratively optimized by weighted minimum mean square error to obtain the optimal edge device side power. The pre-encoder structure of each node is fixed based on the relaxed convex optimization problem, so as to perform Riemannian manifold gradient iterative optimization design on the metasurface reflection coefficient to obtain the optimal smart metasurface reflection coefficient.

[0010] A second aspect of the present invention provides a beamforming design apparatus for a metasurface-enabled communication and computing integrated system, comprising: The evaluation module is used to construct an aerial computational mutual information metric for the target integrated system, in order to evaluate the similarity between the actual aggregated data of the central node and the ideal result. A module is established to establish a mutual information optimization problem with the goal of maximizing the similarity metric, based on the constraints of different center and edge nodes. The iterative solution module is used to iteratively solve the mutual information optimization problem using a multi-convex relaxation and alternating optimization strategy to obtain the beamforming deployment results.

[0011] Optionally, the constraints include server-side power constraints, edge device-side power constraints, and constant mode constraints on the reflectivity of the smart metasurface.

[0012] Optionally, the iterative solution module includes: The design unit is used to perform multiple convex relaxation and matrix decomposition design on the mutual information optimization problem to obtain the relaxed convex optimization problem. The iterative optimization unit is used to perform sequential iterative optimization of the edge device precoding matrix, metasurface reflection coefficient, and center node combiner in the relaxed convex optimization problem using an alternating optimization strategy, so as to obtain the beamforming deployment result, wherein the beamforming deployment result includes the optimal edge device side power and the optimal smart metasurface reflection coefficient.

[0013] Optionally, the iterative optimization unit includes: The first iterative optimization unit is used to fix the metasurface reflection coefficient according to the relaxed convex optimization problem, and to perform iterative optimization design on the pre-encoder of each edge device and the center node by weighted minimum mean square error, so as to obtain the optimal edge device side power. The second iterative optimization unit is used to fix the pre-encoder structure of each node according to the relaxed convex optimization problem, so as to perform Riemannian manifold gradient iterative optimization design on the metasurface reflection coefficient to obtain the optimal smart metasurface reflection coefficient.

[0014] A third aspect of the present invention provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the beamforming design method for a metasurface-enabled communication and computing integrated system as described in the above embodiments.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the beamforming design method of the metasurface-enabled communication-computing integrated system described above.

[0016] The beamforming design method for a metasurface-enabled integrated communication and computing system proposed in this invention constructs a system architecture consisting of an edge server, multiple edge devices, and multiple intelligent metasurfaces. It enhances link quality by introducing a reflection transmission path through intelligent metasurfaces and leverages the natural superposition properties of electromagnetic waves for data aggregation computation through over-the-air computing. To characterize the convergence rate, an over-the-air computing mutual information metric is designed, which is more universally applicable than the mean square error metric in low signal-to-noise ratio, complex channel, and power-constrained scenarios. Based on the alternating optimization approach, a multi-convex relaxation beamforming optimization algorithm is proposed, iteratively optimizing the server beamforming matrix and the edge device precoding matrix sequentially. It also features an intelligent metasurface reflection coefficient matrix; enabling joint optimization of multiple parameters between the server, RIS, and user, significantly reducing computational overhead and control signaling at terminal nodes, further improving the efficiency of the integrated communication and computing system; while ensuring the integrity of channel information, it minimizes signaling and pilot overhead in actual data computation through a bidirectional interaction mechanism of downlink broadcasting from the central node and uplink over-the-air computation from edge devices, thereby ensuring high-capacity robust transmission and accurate and efficient computation of data information; it can also effectively improve network throughput, exhibiting strong robustness under different device numbers and data volumes, and can be efficiently applied to IoT data aggregation, edge intelligence, and other scenarios. Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0017] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart illustrating a beamforming design method for a metasurface-enabled integrated communication and computing system provided in an embodiment of the present invention; Figure 2 A diagram of an airborne computing system empowered by a smart metasurface, provided in an embodiment of the present invention; Figure 3 This is a comparative schematic diagram of various aerial computational beamforming schemes under different signal-to-noise ratios provided in an embodiment of the present invention; Figure 4 This is a comparative diagram of various aerial computational beamforming schemes for different numbers of edge devices provided in an embodiment of the present invention; Figure 5 This is a block diagram of a beamforming design device for a metasurface-enabled communication and computing integrated system provided in an embodiment of the present invention. Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0018] Explanation of reference numerals in the attached figures: 50-Beamforming design device for a metasurface-enabled communication and computing integrated system; 501-Evaluation module; 502-Establishment module; 503-Iterative solution module; 601-Memory; 602-Processor; 603-Communication interface. Detailed Implementation

[0019] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0020] The beamforming design method for a metasurface-enabled integrated communication and computing system according to an embodiment of the present invention is described below with reference to the accompanying drawings. Addressing the issues mentioned in the background section regarding the challenges of traditional over-the-air computing systems in scenarios with massive terminal device access, such as redundant signaling interactions, high computational overhead, susceptibility to channel fading affecting aggregation accuracy, and the difficulty of jointly optimizing beamforming and metasurface reflection coefficients, the present invention provides a beamforming design method for a metasurface-enabled integrated communication and computing system. This method, under the premise of strictly satisfying server-side power constraints, edge device-side power constraints, and constant modulus constraints of intelligent metasurface reflection coefficients, constructs an over-the-air computing mutual information metric adapted to both homogeneous and heterogeneous differentiated data sources. It employs a convex relaxation transformation technique to transform the non-convex optimization problem into a convex optimization subproblem. Combined with a three-parameter alternating optimization strategy involving the edge device precoding matrix, intelligent metasurface reflection coefficient, and server beamforming matrix, it achieves accurate solutions for each parameter, effectively reducing terminal node control signaling overhead and system computational overhead. It is particularly suitable for global performance balancing and control in scenarios with massive device access and complex channel environments, and can adaptively adjust the optimization strategy for different data types and channel conditions, exhibiting excellent robustness. It can effectively solve the technical problems of signaling redundancy, computational inefficiency, insufficient aggregation accuracy, and difficulty in joint optimization in scenarios with massive terminal device access.

[0021] Specifically, Figure 1 This is a flowchart illustrating a beamforming design method for a metasurface-enabled integrated communication and computing system provided in an embodiment of the present invention.

[0022] like Figure 1 As shown, the beamforming design method for this metasurface-enabled integrated communication and computing system includes the following steps: In step S101, an aerial computational mutual information metric for the target integrated system is constructed to evaluate the similarity between the actual aggregated data of the central node and the ideal result.

[0023] In step S102, a mutual information optimization problem with the goal of maximizing similarity metric is established based on the constraints of different center and edge nodes.

[0024] In some embodiments, the constraints include server-side power constraints, edge device-side power constraints, and constant mode constraints on the reflectivity of the smart metasurface.

[0025] It should be noted that, as Figure 2 As shown, those skilled in the art need to pre-construct a target integrated system, which includes an edge server, edge devices, a smart metasurface, and a construction module. The edge server is configured with a multi-antenna array to receive and merge aggregated signals through a beamforming matrix; the edge devices transmit source data through independent precoding matrices; the smart metasurface is used to adjust the phase of reflected signals; and the construction module is used to configure and complete the beamforming matrix design of the over-the-air computing system according to channel and transceiver parameters.

[0026] In actual implementation, the target integrated system consists of an edge server and It consists of several edge devices, with the edge device serial number being... The number of intelligent metasurfaces is denoted as The corresponding serial number is The number of RF links in the device is The number of edge device antennas is The number of antennas on the server side is denoted as Initial data for edge devices Each device has an independent pre-encoder From the transmitting array via the channel Transmitted to the edge server and beamformed by the beamforming matrix The merging process, where data is received in a specific time slot, is illustrated in the following example:

[0027] in, The final aggregation obtained by the edge servers Dimensional data, Zero mean and variance The received thermal noise. The beamforming matrix... and , All must satisfy the maximum power constraint, that is:

[0028]

[0029] in, and Each has its own peak power constraint.

[0030] The uplink channel can be further modeled as the sum of the direct channel and the reflection channels of each metasurface, with the specific formula as follows:

[0031] in, To the equipment Direct channel matrix to the server, Metasurface The diagonal reflection matrix, and the reflection coefficient vector of each reflection unit are... ,at the same time and Representing intelligent metasurfaces Serving edge devices The reflection channel matrix on both sides of the time.

[0032] The received signal can be calculated. Combined calculation results with ideal The mutual information, or similarity measure, is shown in the following formula:

[0033] Among them, auxiliary matrix and The definition is as follows:

[0034]

[0035] It should be noted that, in this metasurface-enabled aerial computing system, considering a series of hardware and software constraints on the central node, edge devices, and the metasurface side, the current problem can be specifically modeled as follows:

[0036] In step S103, a multi-convex relaxation and alternating optimization strategy is used to iteratively solve the mutual information optimization problem to obtain the beamforming deployment result.

[0037] In some embodiments, a multi-convex relaxation and alternating optimization strategy is used to iteratively solve the mutual information optimization problem to obtain beamforming deployment results, including: The mutual information optimization problem is designed with multiple convex relaxation and matrix decomposition to obtain the relaxed convex optimization problem; An alternating optimization strategy is used to iteratively optimize the edge device precoding matrix, metasurface reflection coefficient, and center node combiner in the relaxed convex optimization problem to obtain the beamforming deployment result, which includes the optimal edge device side power and the optimal smart metasurface reflection coefficient.

[0038] In some embodiments, an alternating optimization strategy is used to iteratively optimize the edge device precoding matrix, metasurface reflection coefficient, and center node combiner in the relaxed convex optimization problem to obtain the beamforming deployment result, including: Based on the relaxed convex optimization problem, the metasurface reflection coefficient is fixed, and the pre-encoders of each edge device and the central node are iteratively optimized by weighted minimum mean square error to obtain the optimal edge device side power. The pre-encoder structure of each node is fixed based on the relaxed convex optimization problem, so as to perform Riemannian manifold gradient iterative optimization design on the metasurface reflection coefficient to obtain the optimal smart metasurface reflection coefficient.

[0039] In actual implementation, the central node aggregates a large amount of data from numerous edge nodes. Based on the independence of the data source distribution, a customized representation scheme for the mutual information index is developed. Then, using convex relaxation and matrix factorization, the mutual information optimization problem with non-convex objective functions and non-convex constraints is transformed into a convex optimization problem, while ensuring a strict lower bound property for the relaxation result. The specific expression for the relaxed convex optimization problem is as follows:

[0040] Among them, auxiliary variables , Therefore, auxiliary variables can be used. get Among them, it is necessary to meet the following conditions. .

[0041] According to the function For variables and The convexity of the matrix can be relaxed by Taylor expansion and matrix decomposition, resulting in the following form:

[0042] Among them, the updated auxiliary variable is , .

[0043] As can be seen, the optimization objective after relaxation has now become a convex quadratic optimization problem, with auxiliary variables... and Includes the optimization objective to be estimated Therefore, by substituting the specific definition, we can obtain the final optimization problem-solving objective.

[0044] Furthermore, for the beamforming matrix on the edge server side The energy constraint does not affect the final mutual information value; therefore, subsequent processing only requires design optimization for the power on the edge device side. The optimization objective and constraints can be transformed into a Lagrangian function for further analysis, as shown in the following expression:

[0045] Among them, parameters For the equipment Lagrange factor First, the beamforming matrix on the server side... Optimization is then performed. Based on the Lagrange function described above, the optimization objective is now about... This is a quadratic programming problem, and this convex problem can be solved by obtaining its current i-th... The theoretically optimal closed-form solution under round-recursion iteration is as follows:

[0046] Simultaneously, pre-encode the edge device. Similar to the expression above, the optimization objective here is also about... This is a quadratic programming problem, but unlike the process described above, the optimization problem is subject to the Lagrange factor due to the introduction of energy constraints on the terminal equipment. The impact requires flexible adjustments at this point. Based on this, its theoretical optimal closed-form solution is calculated, specifically as follows:

[0047] Simultaneously, a one-dimensional binary search scheme is used to control the growth rate of the Lagrange factor. When the dual relaxation condition of the power constraint is satisfied, the search and determination of the Lagrange factor is completed.

[0048] Furthermore, since this involves the design of the reflection phase within the channel, it is necessary to... Specifically, regarding The function is then further optimized. During this iteration, the reflection coefficients of the remaining metasurfaces are fixed, and the function is optimized for the th metasurface. Metasurface coefficient By performing alternating optimization, the optimization objective can be transformed into... The form is as follows, where the variable expressions are as follows:

[0049]

[0050] Meanwhile, considering that the reflection coefficient needs to satisfy the constant modulus constraint This is the applicable condition for Riemannian manifolds. Therefore, the gradient descent scheme of Riemannian manifolds is called to further optimize the phase-modulated reflection coefficient to ensure that the reflection coefficient satisfies the constant modulus constraint.

[0051] In addition, in this embodiment of the invention, the parameter configurations that do not meet the constraints are dynamically weighted to meet the constraint requirements.

[0052] Those skilled in the art will understand that this optimization design problem is a non-convex multivariate optimization problem, making it difficult to find a closed-form solution that maximizes the system's energy efficiency. This invention uses fractional programming, first-order approximate relaxation, and a convex optimization tool to jointly design the precoding vectors for public and private information. It should be noted that this invention has no requirements regarding the antenna deployment of the central node or the number of terminal devices. As the number of connected users increases, the constraints of the optimization problem become more numerous, but the precoding design can always be achieved.

[0053] Finally, after iteratively optimizing the edge device precoding matrix, metasurface reflection coefficient, and central node combiner respectively, the calculation results are deployed to the edge device and the central node.

[0054] The beamforming design method of the metasurface-enabled communication and computing integrated system proposed in this invention will be described in detail below through a specific embodiment.

[0055] The simulation parameters are set as follows: Number of edge devices K =10, Number of data streams N RF =4. Number of edge device antennas N D =5. Number of server-side antennas N S =32, Number of intelligent metasurfaces I =2. Number of intelligent metasurface reflection units N I =8. Energy threshold on the edge device side P k =5. Server-side energy threshold P S =32.

[0056] like Figure 3 As shown, under different data stream configurations, the proposed metasurface-enabled over-the-air computing optimization scheme achieves the best overall computing speed compared to the non-RIS-enabled over-the-air computing method and the zero-forcing mean square error optimization method. Specifically, it achieves a significant performance improvement of approximately 4 nats / s / Hz compared to the non-metasurface-enabled beamforming scheme, and approximately 3 times the performance improvement compared to the classic zero-forcing mean square error metric.

[0057] At the same time, such as Figure 4 As shown, under the same transmission signal-to-noise ratio constraint, this scheme achieves the best over-the-air computing performance to date, regardless of the number of edge devices. Specifically, on the one hand, the system capacity before and after deploying RIS-enabled systems can be significantly improved by at least 1 nats / s / Hz; on the other hand, in actual systems where RIS-enabled systems are deployed, the overall optimization scheme proposed in this invention can significantly improve system throughput by 2 nats / s / Hz compared to the classic zero-forcing precoding algorithm. Therefore, it can be concluded that, based on the relevant methods and apparatus of this invention, better computing performance can be achieved than related technologies under the same simulation conditions, base stations can save more resources, realize a more efficient integrated computing architecture, and ensure green and reliable communication.

[0058] In summary, the beamforming design method for the metasurface-enabled integrated communication and computing system proposed in this embodiment of the invention, under the premise of strictly meeting the power constraints of servers and edge devices and the constant mode constraints of intelligent metasurfaces, maximizes the improvement of system aggregation accuracy and transmission efficiency by constructing an airborne computing mutual information metric adapted to differentiated data sources and combining multiple convex relaxation and three-parameter alternating optimization strategies. It ensures efficient aggregation and processing of massive data while significantly reducing terminal control signaling overhead and system computing overhead. In particular, it can achieve global performance balance control in environments with massive device access and complex channels, which is of great significance for promoting the engineering implementation of integrated communication and computing technology and the development of green and energy-saving communication.

[0059] Next, with reference to the accompanying drawings, a beamforming design device for a metasurface-enabled integrated communication and computing system according to an embodiment of the present invention is described.

[0060] Figure 5 This is a block diagram of a beamforming design device for a metasurface-enabled integrated communication and computing system provided in an embodiment of the present invention.

[0061] like Figure 5 As shown, the beamforming design device 50 of the metasurface-enabled communication and computing integrated system includes: an evaluation module 501, a setup module 502, and an iterative solution module 503.

[0062] The evaluation module 501 is used to construct an airborne computational mutual information metric for the integrated target system, to evaluate the similarity between the actual aggregated data and the ideal result of the central node. The establishment module 502 is used to establish a mutual information optimization problem with the goal of maximizing the similarity metric, based on the constraints of different central and edge nodes. The iterative solution module 503 is used to iteratively solve the mutual information optimization problem using multiple convex relaxation and alternating optimization strategies to obtain the beamforming deployment results.

[0063] In some embodiments, the constraints include server-side power constraints, edge device-side power constraints, and constant mode constraints on the reflectivity of the smart metasurface.

[0064] In some embodiments, the iterative solution module includes: Design unit, used to design multiple convex relaxation and matrix decomposition for mutual information optimization problem, so as to obtain the relaxed convex optimization problem; The iterative optimization unit is used to perform sequential iterative optimization of the edge device precoding matrix, metasurface reflection coefficient, and center node combiner in the relaxed convex optimization problem using an alternating optimization strategy, so as to obtain the beamforming deployment result, which includes the optimal edge device side power and the optimal smart metasurface reflection coefficient.

[0065] In some embodiments, the iterative optimization unit includes: The first iterative optimization unit is used to fix the metasurface reflection coefficient according to the relaxed convex optimization problem, and to perform iterative optimization design on the pre-encoder of each edge device and the center node by weighted minimum mean square error, so as to obtain the optimal edge device side power. The second iterative optimization unit is used to fix the pre-encoder structure of each node according to the relaxed convex optimization problem, so as to perform Riemannian manifold gradient iterative optimization design on the metasurface reflection coefficient to obtain the optimal smart metasurface reflection coefficient.

[0066] It should be noted that the foregoing explanation of the beamforming design method embodiment for the metasurface-enabled integrated communication and computing system also applies to the beamforming design device of the metasurface-enabled integrated communication and computing system in this embodiment, and will not be repeated here.

[0067] The beamforming design device for the metasurface-enabled integrated communication and computing system proposed in this embodiment of the invention, under the premise of strictly meeting the power constraints of servers and edge devices and the constant mode constraints of intelligent metasurfaces, maximizes the improvement of system aggregation accuracy and transmission efficiency by constructing an airborne computing mutual information metric adapted to differentiated data sources and combining multiple convex relaxation and three-parameter alternating optimization strategies. It ensures efficient aggregation and processing of massive data while significantly reducing terminal control signaling overhead and system computing overhead. In particular, it can achieve global performance balance control in environments with massive device access and complex channels, which is of great significance for promoting the engineering implementation of integrated communication and computing technology and the development of green and energy-saving communication.

[0068] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0069] The electronic device may include: a memory 601, a processor 602, and a computer program stored on the memory 601 and capable of running on the processor 602.

[0070] When the processor 602 executes the program, it implements the beamforming design method of the metasurface-enabled communication and computing integrated system provided in the above embodiments.

[0071] Furthermore, electronic devices also include: Communication interface 603 is used for communication between memory 601 and processor 602.

[0072] The memory 601 is used to store computer programs that can run on the processor 602.

[0073] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0074] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0075] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0076] Processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0077] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the beamforming design method of the metasurface-enabled communication-computing integrated system described above.

[0078] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0079] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0080] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0081] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0082] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0083] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0084] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0085] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A beamforming design method for a metasurface-enabled integrated communication and computing system, characterized in that, Includes the following steps: Construct an aerial computing mutual information metric for the target integration system to evaluate the similarity between the actual aggregated data of the central node and the ideal result; Based on the constraints of different center and edge nodes, a mutual information optimization problem is established with the goal of maximizing the similarity metric. The mutual information optimization problem is solved iteratively using a multi-convex relaxation and alternating optimization strategy to obtain the beamforming deployment results.

2. The beamforming design method for the metasurface-enabled communication and computing integrated system according to claim 1, characterized in that, The constraints include server-side power constraints, edge device-side power constraints, and constant mode constraints on the reflectivity coefficient of the smart metasurface.

3. The beamforming design method for the metasurface-enabled communication and computing integrated system according to claim 1, characterized in that, The step of iteratively solving the mutual information optimization problem using a multiple convex relaxation and alternating optimization strategy to obtain the beamforming deployment result includes: The mutual information optimization problem is designed with multiple convex relaxation and matrix decomposition to obtain the relaxed convex optimization problem; An alternating optimization strategy is used to iteratively optimize the edge device precoding matrix, metasurface reflection coefficient, and center node combiner in the relaxed convex optimization problem to obtain the beamforming deployment result, wherein the beamforming deployment result includes the optimal edge device side power and the optimal smart metasurface reflection coefficient.

4. The beamforming design method for the metasurface-enabled communication and computing integrated system according to claim 3, characterized in that, The method employs an alternating optimization strategy to iteratively optimize the edge device precoding matrix, metasurface reflection coefficient, and center node combiner in the relaxed convex optimization problem to obtain the beamforming deployment result, including: Based on the relaxed convex optimization problem, the metasurface reflection coefficient is fixed, and the pre-encoder of each edge device and the central node is iteratively optimized by weighted minimum mean square error to obtain the optimal edge device side power. The pre-encoder structure of each node is fixed based on the relaxed convex optimization problem, so as to perform Riemannian manifold gradient iterative optimization design on the metasurface reflection coefficient to obtain the optimal smart metasurface reflection coefficient.

5. A beamforming design device for a metasurface-enabled integrated communication and computing system, characterized in that, include: The evaluation module is used to construct an aerial computational mutual information metric for the target integrated system, in order to evaluate the similarity between the actual aggregated data of the central node and the ideal result. A module is established to establish a mutual information optimization problem with the goal of maximizing the similarity metric, based on the constraints of different center and edge nodes. The iterative solution module is used to iteratively solve the mutual information optimization problem using a multi-convex relaxation and alternating optimization strategy to obtain the beamforming deployment results.

6. The beamforming design device for the metasurface-enabled communication and computing integrated system according to claim 5, characterized in that, The constraints include server-side power constraints, edge device-side power constraints, and constant mode constraints on the reflectivity coefficient of the smart metasurface.

7. The beamforming design device for the metasurface-enabled communication and computing integrated system according to claim 5, characterized in that, The iterative solution module includes: The design unit is used to perform multiple convex relaxation and matrix decomposition design on the mutual information optimization problem to obtain the relaxed convex optimization problem. The iterative optimization unit is used to perform sequential iterative optimization of the edge device precoding matrix, metasurface reflection coefficient, and center node combiner in the relaxed convex optimization problem using an alternating optimization strategy, so as to obtain the beamforming deployment result, wherein the beamforming deployment result includes the optimal edge device side power and the optimal smart metasurface reflection coefficient.

8. The beamforming design device for the metasurface-enabled communication and computing integrated system according to claim 7, characterized in that, The iterative optimization unit includes: The first iterative optimization unit is used to fix the metasurface reflection coefficient according to the relaxed convex optimization problem, and to perform iterative optimization design on the pre-encoder of each edge device and the center node by weighted minimum mean square error, so as to obtain the optimal edge device side power. The second iterative optimization unit is used to fix the pre-encoder structure of each node according to the relaxed convex optimization problem, so as to perform Riemannian manifold gradient iterative optimization design on the metasurface reflection coefficient to obtain the optimal smart metasurface reflection coefficient.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the beamforming design method for a metasurface-enabled communication and computing integrated system as described in any one of claims 1-4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the beamforming design method for the metasurface-enabled communication and computing integrated system as described in any one of claims 1-4.

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