Intelligent metasurface rapid optimization method for mobile communication

Abstract

The invention relates to an intelligent metasurface rapid optimization method for mobile communication. The method comprises the following steps of: setting RIS rapid optimization parameters including an optimization ending standard, a codebook size and a unit state set, roughly grouping RIS units, enabling parameters of sub-array planes in the same group to be the same, generating an initial state generation probability matrix for each group, and optimizing by using a CE method. And after multiple iterations, if the performance reaches the standard, terminating, and taking the current parameter as a result, otherwise, re-grouping the roughly grouped sub-array planes, taking a rough grouping optimization final value as an initial value, generating a new grouping initial probability matrix, and then continuing to optimize by using the CE method until an end standard is met. According to the method, through coarse-to-fine progressive optimization, parameter multiplexing and a dynamic termination mechanism, the efficiency, precision and adaptability of RIS optimization in a mobile communication scene are considered, and different communication requirements can be rapidly and stably met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication based on intelligent metasurfaces, in particular to an intelligent metasurface fast optimization method for mobile communication. BACKGROUND

[0002] Intelligent reflecting surfaces (IRS, or Reconfigurable Intelligent Surfaces, RIS) are a kind of artificial electromagnetic surface structure with programmable electromagnetic properties, usually composed of a large number of carefully designed electromagnetic units. By applying control signals to the adjustable elements on the electromagnetic units, the electromagnetic properties of these electromagnetic units can be dynamically controlled, and then the intelligent regulation of spatial electromagnetic waves can be realized in a programmable way without actively emitting electromagnetic waves, forming an electromagnetic field with controllable amplitude, phase, polarization, and frequency parameters. In wireless communication applications, RIS can be used for coverage enhancement, hotspot flow enhancement, interference suppression, secure communication, and energy and information co-transmission, etc. The optimization and regulation of RIS is one of the key technologies for the application of intelligent metasurfaces in wireless communication, mainly optimizing the amplitude and phase parameters of electromagnetic units according to the electromagnetic environment of the communication nodes to make the target function optimal. Generally, there are two methods for the optimization and regulation of RIS: one is the model-based method, which assumes that the sub-channels from the transmitting end to the RIS and from the RIS to the receiving end satisfy a certain model, and then the estimation results are used for the optimization of the intelligent metasurface; the other is the model-free method, which does not require channel estimation in the optimization process of the intelligent metasurface. When the sub-channels can be accurately estimated, the model-based method can achieve better performance; but when the sub-channels cannot be accurately estimated, the model-free method may be the only choice. In mobile communication, due to the mobility of communication nodes, the electromagnetic environment they are in has significant time-varying characteristics, making it difficult to model their channels with a specific model. Therefore, the model-free method has important significance in the optimization and regulation of intelligent metasurfaces for mobile communication.

[0003] The basic idea of the model-free method is to configure different parameters for the RIS, and simultaneously detect the corresponding performance measures at the receiving end, and select the RIS parameters corresponding to the optimal performance measure as the optimization result. In theory, the optimal result can be obtained by traversing all RIS parameters. However, when the number of RIS units is large, the search time complexity is huge. For example, for an RIS array composed of 100 RIS units, if each unit has 2 states and the search time for each state is 1 millisecond, then it takes 1*2 100 milliseconds ≈ 0.3*10 24hours. For this purpose, researchers have proposed a variety of fast search algorithms, but the existing fast search algorithms all have defects including difficulty in convergence and poor performance of optimal code words. SUMMARY

[0004] Therefore, it is necessary to provide a smart surface fast optimization method for mobile communication capable of quickly retrieving optimized RIS parameters in view of the above technical problems.

[0005] A smart surface fast optimization method for mobile communication, the method is applied to two mobile users who cannot predict the position of the other party, each of the mobile users is provided with an omnidirectional antenna and a smart surface for modulating the antenna transmission signal, the method is applied to each mobile user and includes: setting parameters in the RIS fast optimization process, the parameters include optimization end criteria, specified codebook size and RIS unit state set; coarsely grouping the RIS units of the smart surface, and the same grouping of RIS subarrays uses the same parameters, and an initial RIS state generation probability matrix is generated for each group; based on the initial RIS state generation probability matrix, the cross-entropy method is used to optimize the parameters of the coarsely grouped RIS, after multiple iterations, if the performance measure meets the communication requirements, the iteration is stopped, and the parameters obtained in the current iteration are taken as the RIS fast optimization result; If the performance measure does not meet the communication requirements, the RIS subarrays after coarse grouping are regrouped, and the final parameters obtained by coarse grouping optimization are taken as the initial values, the corresponding initial probability matrix is generated for the new grouping, and the CE method is used for parameter optimization until the optimization end criteria are met, and the RIS fast optimization result is obtained.

[0006] In one embodiment, when the initial RIS state generation probability matrix is used to optimize the parameters of the coarsely grouped RIS using the CE method: a plurality of code words consistent with the specified codebook size are generated based on the initial RIS state generation probability matrix; According to the generated code words, the corresponding unit states of the RIS units in the corresponding group are configured, and the performance measure corresponding to each code word is detected at the receiving end, and the iteration optimization process is entered; In each iteration optimization process, the performance measures are sorted from large to small, and the code words corresponding to the top preset number of performance measures are selected to update the initial RIS state generation probability matrix, and new code words are generated based on the updated RIS state generation probability matrix, and the performance measure of the new code words is detected.

[0007] In one embodiment, after each iteration: determining whether the current iteration number reaches the preset number, if the current iteration number reaches the preset number, randomly selecting one from the plurality of code words generated in the current iteration as an optimal code word output, and obtaining a RIS fast optimization result; if the current iteration number does not reach the preset number, determining whether the current RIS state generation probability matrix of each group has converged, if the current RIS state generation probability matrix has not converged, entering the next iteration; if the current RIS state generation probability matrix has converged, determining whether the current performance measure meets the communication requirement, if yes, ending the iteration and obtaining the RIS fast optimization result; if not, re-grouping the RIS sub-arrays after coarse grouping, and optimizing the parameters of the grouped RIS units.

[0008] In one embodiment, when determining whether the current RIS state generation probability matrix of each group has converged, the Frobenius norm of the difference between the current iteration and the previous state generation probability matrix is used for determination.

[0009] In one embodiment, when detecting the performance measure corresponding to each code word at the receiving end of another mobile user, the received signal-to-noise ratio, the received signal strength and the correct reception rate are included.

[0010] In one embodiment, when re-grouping the RIS sub-arrays after coarse grouping, the state generation probability matrix corresponding to coarse grouping is expanded according to a preset expansion formula to obtain an initial probability matrix corresponding to new grouping, and the preset expansion formula is represented as:

[0011] In the above formula, represents the state generation probability matrix obtained in the tthiteration.

[0012] In one embodiment, the RIS units are grouped by using the structural information of the intelligent metasurface.

[0013] In one embodiment, when the RIS units of the intelligent metasurface are coarsely grouped, they are divided into two groups.

[0014] In one embodiment, in the parameter optimization process after the final parameters obtained by coarse grouping optimization are used as initial values and the CE method is used for parameter optimization after an initial probability matrix corresponding to new grouping is generated, the parameter optimization is realized by progressive grouping in each iteration process. The application also provides a motorized communication system, the system comprising two motorized users who cannot predict the position of the other party, each of the motorized users being provided with an omnidirectional antenna and an intelligent metasurface for modulating the antenna transmission signal, and a motorized communication-oriented intelligent metasurface fast optimization method according to any one of claims 1-8 being implemented in each of the motorized users to optimize the RIS unit parameters of the intelligent metasurface, so that the communication signals between the two motorized users form a reflection link and a direct link to the receiving antenna of the other party, and the communication signals are in phase superposition.

[0015] A computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the motorized communication-oriented intelligent metasurface fast optimization method when executing the computer program.

[0016] A computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the steps of the motorized communication-oriented intelligent metasurface fast optimization method.

[0017] The motorized communication-oriented intelligent metasurface fast optimization method comprises the following steps: setting a parameter in the RIS fast optimization process, the parameter comprising an optimization end criterion, a codebook size, and an RIS unit state set, coarsely grouping the RIS units of the intelligent metasurface, using the same parameter for the RIS subarrays in the same group, generating a corresponding initial RIS state generation probability matrix for each group, performing parameter optimization on the coarsely grouped RIS units based on the initial RIS state generation probability matrix and using the CE method, stopping the iteration when the performance measure meets the communication requirement after multiple iterations, and taking the parameter obtained in the current iteration as the RIS fast optimization result, re-grouping the RIS subarrays after the coarse grouping, using the final parameter obtained by the coarse grouping optimization as the initial value, generating a corresponding initial probability matrix for the new grouping, and performing parameter optimization using the CE method until the optimization end criterion is met, and then obtaining the RIS fast optimization result. The method uses the progressive optimization from coarse grouping to fine grouping, parameter reuse, and dynamic termination mechanism, and takes into account the efficiency, accuracy, and adaptability of RIS optimization in the motorized communication scenario, and can quickly and stably meet different communication requirements. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 An application environment diagram of the motorized communication-oriented intelligent metasurface fast optimization method in one embodiment; Figure 2 A schematic diagram of the RIS grouping method in one embodiment; Figure 3 A schematic diagram of the RIS grouping method in one embodiment; Figure 4 A schematic diagram of the structure of a mobile communication system in an embodiment; Figure 5 A specific step flowchart of a smart metasurface fast optimization method for mobile communication in an embodiment; Figure 6 An internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0020] In the prior art, the model-based RIS optimization method is not applicable to the mobile communication scenario. Such a method relies on the sub-channel model assumption, while in mobile communication, the electromagnetic environment is significantly time-varying due to node movement, making it difficult to accurately model the channel. However, the model-free method does not require channel estimation, but has an efficiency bottleneck. The exhaustive method has a very high search time complexity when there are many RIS units (e.g., about 0.3x10 24 hours for 100 units and 2 states), which is completely impractical. Although the existing fast search algorithm (such as the CE (Cross-Entropy, CE) method) has improved efficiency compared to the exhaustive method, it is still insufficient in scenarios such as mobile communication that require high real-time performance. For example, for 100 units and 2 states, the training time of the CE method converges after 10 iterations is about 16.7 minutes, which is difficult to meet the real-time optimization requirements under fast channel changes.

[0021] In view of the problem that the existing method cannot balance the time-varying characteristics of the mobile communication scenario and the real-time requirement of RIS optimization, as shown in Figure 1 the present application, a smart metasurface fast optimization method for mobile communication is provided. The method is applied to two mobile users who cannot predict the position of the other party. Each mobile user is provided with an omnidirectional antenna and a smart metasurface that modulates the antenna transmit signal or receive signal. The method proposed in the present application is applied to each mobile user and specifically includes the following steps: Step S100, setting parameters in the RIS fast optimization process, which includes optimization end criteria, specifies codebook size and RIS unit state set.

[0022] Step S110, coarsely grouping the RIS units of the smart metasurface, and the same grouping of RIS subarrays uses the same parameters, and generates a corresponding initial RIS state generation probability matrix for each group.

[0023] Step S120: Generate a probability matrix based on the initial RIS state, and use the CE method to optimize the parameters of the coarsely grouped RIS. After multiple iterations, if the performance metric meets the communication requirements, stop the iteration and use the parameters obtained in the current iteration as the RIS fast optimization result.

[0024] Step S130: If the performance metric does not meet the communication requirements, the RIS subarrays after coarse grouping are regrouped, and the final parameters obtained from the coarse grouping optimization are used as initial values ​​to generate corresponding initial probability matrices for the new groups. Then, the CE method is used to optimize the parameters until the optimization end criteria are met, and the RIS fast optimization result is obtained.

[0025] In this method, to further accelerate the RIS parameter optimization process, the search space needs to be further compressed while maintaining communication performance. In fact, RIS has significant structural features that can be used to remove many invalid codewords, thus reducing the search space. For example, when the distance between RIS units is less than half a wavelength, adjacent RIS units can share the same parameters. Figure 2 The following is from The RIS, composed of 16 cells, is initially coarsely grouped into four subarrays of 16 cells each, based on proximity. Each subarray shares a single parameter. Therefore, the search space for parameter optimization during the first coarse grouping is 2^36. 4 If the coarse grouping meets the requirements, the search stops; otherwise, a second grouping is performed, dividing the RIS into 16 subarrays, each containing 4 units. When searching this group, the search space is 2^n. 16 If the grouping meets the requirements, the search stops; otherwise, a third grouping is performed. Each unit is an independent subarray with a search space of 2^n. 32 It should be noted that although the space size is the same when refining to one subarray per unit as when searching the entire array directly, this method uses the RIS state probability matrix obtained from the coarse search as the initial value of the RIS state probability matrix for the fine search, which greatly accelerates the convergence rate of the fine search and saves the overall search time of the algorithm.

[0026] Furthermore, when the transmitter and receiver only move in the horizontal plane, the RIS array only needs to perform a horizontal scan. Therefore, the same parameters can be used for the same column of the RIS array, and only the parameters of different columns of the RIS array need to be optimized. For example... Figure 3 The following is from The RIS is divided into two 8-row 4-column subarrays by the first rough grouping, divided into four 8-row 2-column subarrays by the second grouping, and divided into eight 8-row 1-column subarrays by the third grouping. In the search process, the units in each subarray use the same parameters, greatly reducing the search space. Therefore, in the method, the structure characteristics of the RIS are used to effectively reduce the search space of the RIS code word, thereby greatly saving the search time, and a new intelligent metasurface optimization and regulation method with fast convergence is obtained.

[0027] In the embodiment, the method is implemented in two mobile users as shown in Figure 4 Each mobile user is equipped with an RIS capable of regulating electromagnetic wave parameters on demand. In mobile communication, since the positions of the two parties cannot be predicted, both parties use omnidirectional antennas. When mobile user 1 sends information to mobile user 2, a direct link is formed between the antenna of mobile user 1 and the antenna of mobile user 2, and at the same time, a reflected link is formed between the transmitting antenna of mobile user 1 and the RIS of mobile user 2, and between the RIS of mobile user 2 and the receiving antenna. If the communication signals arriving at the receiving antenna through the reflected link and the direct link are in phase and superimposed, the receiving signal strength can be enhanced, thereby improving the communication performance; on the contrary, if the communication signals of the two links are superimposed in opposite phases, the communication performance may be deteriorated. Therefore, optimizing and regulating the phase of the RIS is of great significance to the RIS-aided wireless communication system.

[0028] In step S100, the parameters related to the optimization of the intelligent metasurface are first set, including the optimization end criterion, the specified codebook size, and the RIS unit state set.

[0029] Specifically, the factor for judging whether the algorithm converges is set , which is usually taken as . The quantile for updating the proportion of the code word set is set , where . The maximum number of iterations is set , where . The number of RIS units is set , and the number of RIS unit states is set . The maximum codebook size is set , which is usually taken as . The RIS unit state set is also set , when the RIS is 1-bit quantization, there is ; when the RIS is 2-bit quantization, there is .

[0030] It should be noted here that the optimization termination criteria actually include multiple sequential judgment criteria, which will be mentioned again in the following explanation, and will not be elaborated here.

[0031] In step S110, when generating the probability matrix based on the initial RIS state and using the CE method to optimize the parameters of the coarsely grouped RIS, each group is actually treated as a whole, and the parameters of the RIS units in different groups are optimized.

[0032] Specifically, during initialization, multiple codewords of the same size as the specified codebook are first generated based on the initial RIS state generation probability matrix. Then, according to the generated codewords, the corresponding unit states are configured for the RIS units in the corresponding groups. At the receiving end, the performance metrics corresponding to each codeword are detected, and the iterative optimization process begins. In each iteration, the performance metrics are sorted from largest to smallest, and the codewords corresponding to the top-ranked preset number of performance metrics are selected to iteratively update the initial RIS state generation probability matrix. New codewords are then generated based on the updated RIS state generation probability matrix, and the performance metrics of the new codewords are detected.

[0033] In one embodiment, the RIS units of the smart metasurface are divided into two groups, that is, the RIS are divided into... There are groups, each group contains There are RIS units, and all units in the same group use the same parameters. The initial generation size is... RIS state probability matrix That is, each element of the matrix is ​​initialized to 0. RIS state generation probability matrix The superscript indicates the current iteration number, which is the nth iteration of the matrix. Line number Column elements Representing the first RIS The unit takes the first The probabilities of states, any column of which satisfies .

[0034] Furthermore, according to generate individual code characters The codebook is composed of, for The vector, , For the first k The first of the code characters n The state of all units in each RIS group.

[0035] Next, configure the codewords for each RIS chip. ( ), and the performance measure corresponding to each code word is detected at the receiving end The performance measure directly reflects the communication performance of the configuration code word .

[0036] Further, in the iteration process, the performance measure ( ) is sorted from large to small, and the corresponding index is ( ), and the first code words in are selected, and the RIS state generation probability matrix is updated according to the following formula , wherein is an indicator function: when the random variable is equal to , the function takes the value 1; when the random variable is not equal to , the function takes the value 0. Then, according to , a new code word is generated: At this time, one iteration is completed, and the iteration number is updated.

[0037] In this embodiment, after each iteration: it is judged whether the current iteration number reaches the preset number, if the current iteration number reaches the preset number, a code word is randomly selected from the plurality of code words generated in the current iteration as the optimal code word output, and the RIS fast optimization result is obtained. If the current iteration number does not reach the preset number, it is judged whether the current RIS state generation probability matrix of each group has converged, if the current RIS state generation probability matrix has not converged, the next iteration is entered, if the current RIS state generation probability matrix converges, it is judged whether the current performance measure meets the communication requirement, if it meets, the iteration is ended, and the RIS fast optimization result is obtained, if it does not meet, the RIS subarray after coarse grouping is grouped again, and the parameters of the grouped RIS unit are optimized.

[0038] Specifically, when it is judged whether the current RIS state generation probability matrix of each group has converged, the Frobenius norm of the difference between the current iteration and the previous state generation probability matrix is used for judgment, and the process is represented as:

[0039] Specifically, when the performance measure corresponding to each code word is detected at the receiving end of another mobile user, it includes the received signal-to-noise ratio, the received signal strength and the correct reception rate.

[0040] Considering that the coarse grouping combines a large number of RIS units into a small number of subarrays, all units in the same group are forced to use the same parameters. This simplification can improve optimization efficiency, but may not accurately match complex electromagnetic environments (such as multipath interference, dynamic fading, etc.). For example, if there are incident waves in different directions within the area covered by a coarse group, it is difficult to optimize the reflection performance of each direction with uniform parameters, resulting in an overall performance measure that cannot meet the standard. In addition, the coarse grouping generates an initial probability matrix based on a simplified model, and the optimization process is constrained within a relatively narrow parameter space. When the communication demand is high (such as high signal-to-noise ratio, low interference), this space may not contain the optimal solution. At this time, the number of subarrays needs to be increased by fine grouping to allow more units to independently adjust parameters, thereby expanding the optimization space and improving the likelihood of finding a better solution. Therefore, in the present method, the fine grouping is proposed to compensate for the accuracy of the coarse grouping. When the simplification of the coarse grouping cannot meet the performance requirements, the parameter degrees of freedom are increased by splitting the group, and the optimal solution is searched in a more detailed dimension to balance efficiency and accuracy.

[0041] In step S130, each current group is split into two subgroups, and the state generation probability matrix of the coarse group is updated Each subgroup contains RIS units, and all units in the same group use the same parameters. When the RIS subarrays after coarse grouping are grouped again, the state generation probability matrix corresponding to the coarse grouping is expanded according to a preset expansion formula to obtain an initial probability matrix corresponding to the new group generation, and the preset expansion formula is as follows:

[0042] In the above formula, is a matrix, and represents the state generation probability matrix obtained in the i-th iteration; the expansion matrix is a t matrix, which expands each column in the original to two columns to obtain a probability matrix.

[0043] ​Specifically, after re-grouping the RIS sub-arrays after coarse grouping, the CE method is still used for parameter updating, and it is judged again whether the current iteration number reaches the preset number. If the current iteration number reaches the preset number, a random one of the multiple code words generated in the current iteration is selected as the optimal code word output, and the RIS fast optimization result is obtained. If the current iteration number does not reach the preset number, it is judged whether the current RIS state generation probability matrix of each group has converged. If the current RIS state generation probability matrix has not converged, the next iteration is entered. If the current RIS state generation probability matrix has converged, it is judged whether the current grouping number meets the maximum grouping number. If it meets, a random one of the multiple code words generated in the current iteration is selected as the optimal code word output, and the RIS fast optimization result is obtained. If it does not meet, the RIS sub-arrays after current grouping are re-grouped, and the parameters of the grouped RIS units are optimized.

[0044] In fact, after re-grouping the RIS sub-arrays after coarse grouping, if the optimized parameters still cannot meet the requirements, re-grouping is performed again. This multiple progressive grouping method dynamically adjusts the precision of parameter optimization to achieve adaptive balance between efficiency and accuracy. Each re-grouping further splits the original sub-arrays, allowing more RIS units to independently adjust parameters and expanding the optimization space. This means that it can more accurately match complex electromagnetic environments (such as multipath fading and dynamic interference) and gradually approach the theoretical optimal solution, avoiding performance bottlenecks caused by overly coarse initial grouping. If the finest granularity grouping (each unit is optimized independently) is used directly, the computational complexity will increase dramatically due to the explosion of parameter dimensions, making it difficult to meet the real-time requirements of mobile communication. Progressive grouping gradually refines from coarse to fine, quickly filters out the optimal parameter range in the early stage, and only optimizes within this range in subsequent fine grouping, reducing invalid searches and balancing efficiency and final accuracy. This also enhances the adaptability of the method to dynamic scenarios. In mobile communication, the electromagnetic environment changes dramatically over time, such as rapid changes in the channel caused by node movement. Progressive grouping can flexibly adjust the optimization granularity based on real-time performance feedback: if the current grouping precision is sufficient to meet the requirements, iteration can be stopped; if it is not sufficient, the granularity can be dynamically increased to ensure that adaptive parameter configurations can always be found in changing environments, improving system stability.

[0045] As shown in FIG. 1, it is a specific flowchart of the method. Figure 5

[0046] ​In the method, first, the RIS is coarsely grouped, the RIS subarrays in the same group adopt the same parameters, the coarsely grouped RIS is optimized by using the CE method, if the performance measure meets the communication requirement, the training is stopped, otherwise, the RIS is finely grouped, the final parameters obtained by the coarse grouping are used as the initial values, the finely grouped RIS is optimized by using the CE method, until the algorithm meets the communication performance requirement, or the upper limit of the iteration number is reached, and the method is stopped. Compared with the existing model-based intelligent metasurface optimization technology, the method does not need to assume that the channel satisfies a model, and does not need to estimate the channel parameters. Meanwhile, the method utilizes the structural information of the RIS, further accelerates the RIS parameter optimization rate under the premise of ensuring the communication effect, is suitable for fast-changing channel conditions such as mobile communication, and has good usability.

[0047] It should be understood that, although Figure 1 The steps in the flowchart of the method are displayed in sequence according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps in the method can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the sub-steps or stages is not necessarily sequential, but can be alternately executed with other steps or at least part of the sub-steps or stages of other steps.

[0048] In one embodiment, a mobile communication system is provided, and the system can refer to Figure 4 The system includes two mobile users who cannot predict the position of the other party, each mobile user is provided with an omnidirectional antenna and an intelligent metasurface for modulating the antenna transmission signal, and the RIS unit parameter optimization of the intelligent metasurface is performed by using the method for fast optimization of the intelligent metasurface for mobile communication, so that the communication signals between the two mobile users form a reflection link and a direct link to the receiving antenna of the other party, and the signals are in phase superposition.

[0049] The specific limitations of the mobile communication system can refer to the limitations of the method for fast optimization of the intelligent metasurface for mobile communication described above, and will not be repeated here. Each module in the mobile communication system can be realized by software, hardware, and a combination thereof. Each module can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0050] In one embodiment, a computer device is provided, which can be a terminal, and an internal structure diagram thereof can be as shown in FIG. 1. Figure 6 The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is configured to communicate with external terminals through network connection. The computer program is executed by the processor to implement an intelligent metasurface fast optimization method for motor communication. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0051] Those skilled in the art can understand that Figure 6 the structure shown in FIG. 1 is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0052] In one embodiment, a computer device is provided, which includes a memory and a processor, and the memory stores a computer program. The processor implements the following steps when executing the computer program: parameters in the RIS fast optimization process, including optimization end criteria, specified codebook size and RIS element state set; coarsely grouping the RIS elements of the intelligent metasurface, and using the same parameters for the RIS subarrays in the same group, and generating a corresponding initial RIS state generation probability matrix for each group; based on the initial RIS state generation probability matrix, using the CE method to optimize the parameters of the coarsely grouped RIS, and after multiple iterations, if the performance measure meets the communication requirements, stopping the iteration, and taking the parameters obtained in the current iteration as the RIS fast optimization result; If the performance measure does not meet the communication requirements, the RIS subarrays after coarse grouping are re-grouped, and the final parameters obtained by coarse grouping optimization are taken as the initial values. The corresponding initial probability matrix is generated for the new grouping, and the CE method is used for parameter optimization until the optimization end criteria are met, and the RIS fast optimization result is obtained.

[0053] In one embodiment, a computer readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, implements the following steps: Parameters in the RIS fast optimization process are set, including optimization end criteria, specified codebook size, and RIS unit state set; The RIS units of the intelligent metasurface are coarsely grouped, and the RIS subarrays of the same group use the same parameters, and a corresponding initial RIS state generation probability matrix is generated for each group; Based on the initial RIS state generation probability matrix, the parameters of the coarsely grouped RIS are optimized using the CE method, and after multiple iterations, if the performance measure meets the communication requirements, the iteration is stopped, and the parameters obtained in the current iteration are taken as the RIS fast optimization result; If the performance measure does not meet the communication requirements, the RIS subarrays after coarse grouping are re-grouped, and the final parameters obtained by coarse grouping optimization are taken as the initial values, a corresponding initial probability matrix is generated for the new group, and the CE method is used for parameter optimization until the optimization end criteria are met, and the RIS fast optimization result is obtained.

[0054] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0055] Any combination of the technical features in the above embodiments can be made, and for the sake of brevity, not all possible combinations are described above, however, as long as the combination of the technical features does not exist in contradiction, it shall be considered within the scope of the present disclosure.

[0056] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it shall not be understood as a limitation on the patent scope of the present application. It shall be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these shall be within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A method for intelligent metasurface fast optimization for mobile communication, characterized in that, The method is applied to two mobile users who cannot predict the position of the other party, each of which is provided with an omnidirectional antenna and an intelligent metasurface for modulating the antenna transmission signal, and the method is applied to each mobile user, comprising: setting parameters in the RIS fast optimization process, including optimization end criteria, specified codebook size, and RIS unit state set; coarsely grouping the RIS units of the intelligent metasurface, and using the same parameters for the RIS subarrays in the same group, and generating a corresponding initial RIS state generation probability matrix for each group; based on the initial RIS state generation probability matrix, using the CE method to optimize the parameters of the coarsely grouped RIS, after multiple iterations, if the performance measure meets the communication requirements, stop iteration, and the parameters obtained in the current iteration are taken as the RIS fast optimization result; if the performance measure does not meet the communication requirements, the RIS subarrays after coarse grouping are regrouped, and the final parameters obtained by coarse grouping optimization are taken as the initial value, a corresponding initial probability matrix is generated for the new grouping, and the CE method is used for parameter optimization until the optimization end criteria are met, and the RIS fast optimization result is obtained.

2. The method of claim 1, wherein, In the process of optimizing the parameters of the coarsely grouped RIS based on the initial RIS state generation probability matrix using the CE method: generate a plurality of code words consistent with the specified codebook size based on the initial RIS state generation probability matrix; according to the generated code words, configure the corresponding unit states for the RIS units in the corresponding group, and detect the performance measure corresponding to each code word at the receiving end, and enter the iterative optimization process; in each iteration optimization process, sort the performance measures from large to small, and select a preset number of performance measures corresponding to the code words in the front of the sorting to update the initial RIS state generation probability matrix, and generate new code words based on the updated RIS state generation probability matrix, and detect the performance measure of the new code words.

3. The method of claim 2, wherein, After each iteration: determine whether the current iteration number reaches the preset number, if the current iteration number reaches the preset number, randomly select one of the plurality of code words generated in the current iteration as the optimal code word output, and obtain the RIS fast optimization result; if the current iteration number does not reach the preset number, determine whether the current RIS state generation probability matrix of each group has converged, if the current RIS state generation probability matrix has not converged, enter the next iteration; if the current RIS state generation probability matrix converges, determine whether the current performance measure meets the communication requirements, if it meets, the iteration ends, and the RIS fast optimization result is obtained; if not, regroup the RIS subarrays after coarse grouping, and optimize the parameters of the grouped RIS units.

4. The method of claim 3, wherein, In the process of determining whether the current RIS state generation probability matrix of each group has converged, the Frobenius norm of the difference between the current iteration and the previous state generation probability matrix is used for judgment.

5. The method of claim 2, wherein, In the process of detecting the performance measure corresponding to each code word at the receiving end of the other mobile user, the received signal-to-noise ratio, the received signal strength, and the correct reception rate are included.

6. The method of claim 1, wherein, When the RIS sub-arrays after rough grouping are grouped again, a state generation probability matrix corresponding to rough grouping is generated, and is expanded according to a preset expansion formula to obtain an initial probability matrix corresponding to new grouping generation, and the preset expansion formula is represented as: In the above formula, denotes the state generation probability matrix obtained at the tth iteration.

7. The method of claim 1, wherein, The RIS units are grouped by using the structural information of the intelligent metasurface.

8. The method of claim 7, wherein, When the RIS units of the intelligent metasurface are roughly grouped, they are divided into two groups.

9. The method of claim 1-8, wherein, In the parameter optimization process by using the CE method after taking the final parameters obtained by rough grouping optimization as initial values and generating an initial probability matrix corresponding to new grouping, the parameter optimization is realized by progressive grouping in each iteration process.

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