Beamforming method, system and computer readable medium for metasurface antenna

Abstract

The present disclosure provides a beamforming method for a metasurface antenna, comprising: step S1, constructing a beamforming model for the metasurface antenna; the goal in the beamforming model is to maximize the communication quality index between the user-end cluster and the metasurface antenna, and the decision variable in the beamforming model is the state codeword corresponding to the N antenna units included in the metasurface antenna, and the state codeword is represented by b bits; step S2, solving the beamforming model based on a binary bat algorithm to obtain the corresponding current optimal solution; in the binary bat algorithm solving process, the individuals in the population are the overall codewords composed of the state codewords corresponding to the N antenna units respectively arranged in sequence, and the optimal solution includes the optimal value of the state codeword of each antenna unit; step S3, deploying the metasurface antenna according to the current optimal solution.

Description

Technical Field

[0001] The present disclosure relates to the field of wireless communication technology, and in particular to a beamforming method, system and computer-readable medium for a metasurface antenna. Background Art

[0002] In order to continuously improve the performance of wireless communications, it is necessary to study new information technologies with lower cost, lower power consumption and higher intelligence. In the current research on the frontier of communications, metasurface antenna technology (RIS / Large Intelligent Surface / Reconfigurable Intelligent Surface / Software Defined Surface / Metasurface / IRS / Intelligent Reflecting Surface / Reconfigurable Meta Surfaces / HolographicMIMO, etc.) meets the above requirements. The metasurface antenna unit is composed of low-cost, low-power devices, and the phase of the unit can be freely adjusted. The wireless propagation environment can be reconfigured by software control, making the wireless environment more intelligent.

[0003] Compared with other existing technologies, metasurface antennas have significant advantages in many aspects. Compared with traditional phased array antennas, metasurface antennas do not require any transmitting radio frequency (RF) chain, so they can greatly reduce hardware costs and energy consumption. In addition, programmable metasurface antennas can also quickly control the direction of the beam, and can control the electromagnetic waves through the electrical signals output by the control circuit to achieve reconfigurable radiation patterns, and can control the beam direction more quickly and conveniently according to needs. Not only that, the advantages of simple manufacturing process and low cost of metasurface antennas also make it have the potential to gradually replace traditional phased arrays, and can shine in future engineering and practical applications. Summary of the invention

[0004] In a first aspect, the present invention provides a beamforming method for a metasurface antenna, the beamforming comprising:

[0005] Step S1, constructing a beamforming model of the metasurface antenna; the goal in the beamforming model is to maximize the communication quality index between the user terminal cluster and the metasurface antenna, and the decision variable in the beamforming model is the state codeword corresponding to the N antenna units included in the metasurface antenna, and the state codeword is represented by b bits, where b is a positive integer, and the user terminal cluster includes at least one user terminal;

[0006] Step S2, solving the beamforming model based on the binary bat algorithm to obtain the corresponding current optimal solution; in the binary bat algorithm solving process, the individual in the population is an overall codeword composed of the state codewords corresponding to the N antenna units arranged in sequence, and the fitness of the individual in the population is the communication quality index between the user-end cluster and the super-surface antenna when the overall codeword corresponding to the individual is deployed on the super-surface antenna, and the optimal solution includes the optimal value of the state codeword of each antenna unit;

[0007] Step S3: deploy the metasurface antenna according to the current optimal solution.

[0008] Optionally, step S2 includes:

[0009] Step S201, initializing algorithm parameters of the binary bat algorithm, the algorithm parameters including: sound wave frequency, pulse emission rate and pulse loudness;

[0010] Step S202: Generate an initialization population including a plurality of individuals and use it as the original population corresponding to the first iteration process;

[0011] Step S203, determining whether the original population corresponding to the current iterative process meets the preset iteration termination condition;

[0012] If it is determined that the original population corresponding to the current iterative processing does not meet the iteration termination condition, step S204 is executed; if it is determined that the original population corresponding to the current iterative processing meets the iteration termination condition, step S206 is executed;

[0013] Step S204: calibrate the first The overall codeword of the individuals in the original population corresponding to the first iteration is updated to obtain The final updated population corresponding to the iterative processing is and is an integer, is the preset maximum number of iterations;

[0014] Step S205: The final updated population corresponding to the iterative processing is taken as the The original population corresponding to the iterative processing, and the current number of iterations Add 1 to update.

[0015] After step S205 is completed, step S203 is executed again;

[0016] Step S206: Output The best fitness individual in the original population of the iteration is used to obtain the current optimal solution.

[0017] Optionally, the initialization population includes 2 b groups, each group includes a individuals, a is a positive integer;

[0018] In the initialized population, the first state code words of a individuals in the same group are all the same, the first state code words of any two individuals in different groups are all different, and the last N-1 state code words of each individual are randomly generated;

[0019] In step S204, During the updating process of the overall codeword of the individuals in the original population corresponding to the iterative processing, the first state codeword of each individual in the original population corresponding to each iterative processing remains unchanged.

[0020] Optionally, step S204 specifically includes:

[0021] Step S2041: Determine the first The sound wave frequencies of each individual in the original population corresponding to the iterative processing are:

[0022] ;

[0023] Indicates The first iterative process corresponds to the first The sound frequency of an individual, and is an integer, and Respectively represent the preset minimum sound wave frequency and maximum sound wave frequency, Represents a random number in the range [0,1];

[0024] Step S2042: while keeping the first state codeword of each individual in the original population unchanged, The individuals in the original population corresponding to the first iteration are updated, and the The initial updated population corresponding to the iterative processing is:

[0025] ;

[0026] in, Indicates The first iteration corresponds to the first updated population The individual The value of bits, and is a positive integer, Indicates The first iterative process corresponds to the first The individual The value of bits, Express The result of the inversion process is Represents the first random number in the range [0,1], represents the transfer function for binary position updates, Indicates The first iteration corresponds to the first updated population The individual The speed of bit movement, Indicates The first iteration corresponds to the first update of the best fitness individual in the population. The value of bits;

[0027] Step S2043: while maintaining the first state codeword of each individual in the initial update population unchanged, For each group in the initial update population corresponding to the first iteration, the following formula is used to update the individuals in the group, and the first The population is further updated corresponding to the iterative processing:

[0028] ;

[0029] Indicates The corresponding iterative processing further updates the population in the first In the group The individual The value of bits, Indicates The first iteration process corresponds to the initial update population in the first In the group The individual The value of bits, Indicates The first iteration corresponds to the first updated population The best fitness individual in the group is The value of bits, Indicates that the value is within the range The second random number within Indicates that the The group in The pulse emission rate configured during the iteration process, and is a positive integer, and is a positive integer;

[0030] Step S2044: For each group, detect The group in The second best fitness corresponding to the iteration process Is it greater than the corresponding first optimal fitness? , and detection for The group in the group in the The values ​​generated during the iteration are in the range The third random number within Is it less than the pre-set The group in The configured impulse loudness during the iteration process , the first optimal fitness For the The first iteration process corresponds to the initial update population The fitness of the individual with the best fitness in the group, the second best fitness For the The next iteration process further updates the population The fitness of the individual with the best fitness in the group;

[0031] Step S2045: According to the detection results of each group in step S2044, determine the The pulse emission rate and pulse loudness configured during the iterative processing;

[0032] Among them, for the Group, if The second best fitness corresponding to the group Greater than the corresponding first optimal fitness , and the first The group in The third random number in the iteration Less than impulse loudness , then increase the pulse emission rate The processing result is taken as the The group in The pulse emission rate configured during the iteration process , and reduce pulse loudness The processing result is taken as the The group in The configured impulse loudness during the iteration process ;

[0033] For Group, if The second best fitness corresponding to the group Less than or equal to the corresponding first optimal fitness , or The group in The third random number in the iteration Greater than or equal to impulse loudness , then the pulse emission rate As the The group in The pulse emission rate configured during the iteration process , and the impulse loudness As the The group in The configured ;

[0034] Step S2046: The next iteration corresponds to further updating the population and determining the The final updated population corresponds to the iterative processing.

[0035] Optionally, in step S2045, the pulse emission rate is increased The processing result is taken as the The group in The pulse emission rate configured during the iteration process , and reduce pulse loudness The processing result is taken as the The group in The configured impulse loudness during the iteration process The steps specifically include:

[0036] The following formula is used to determine the The group in The pulse emission rate configured during the iteration process and impulse loudness :

[0037] ;

[0038] in, Indicates the initial value of the pulse emission rate configured in the initialization phase, and These are all preset constants. and .

[0039] Optionally, step S2046 specifically includes: Each group in the further updated population corresponding to the iterative processing is taken as the target group in turn, and the following steps are used to update the first group. The corresponding iterative processing further updates the target group in the population:

[0040] Step S20461: The corresponding iterative processing further updates the 2 in the population except the target group b -1 other groups are each used as the target other group, and the equivalent codeword corresponding to the best fitness individual in the target other group is obtained when it is transferred to the target group, and the equivalent codeword refers to: each state codeword in the best fitness individual in the target other group is superimposed with the same conversion codeword to obtain the updated overall codeword, wherein the conversion codeword is equal to the difference between the first state codeword of the individual in the target group and the first state codeword of the best fitness individual in the target other group;

[0041] Step S20462: The optimal fitness individual in the target group in the population corresponding to the iterative processing is further updated as the initial temporary optimal fitness individual in the target group. b -1 other groupings corresponding to 2 b Each of the -1 optimal fitness individuals is used as the target other optimal fitness individuals, and the following processes are performed in sequence:

[0042] Determine whether the fitness of other target individuals with the best fitness is greater than the fitness of the target group's temporary best fitness individual;

[0043] Wherein, if it is determined that the fitness of the target other optimal fitness individuals is greater than the fitness of the temporary optimal fitness individual of the target group, the overall codeword of the temporary optimal fitness individual in the target group is randomly covered with another overall codeword in the target group, and the equivalent codeword of the target other optimal fitness individual is covered with the overall codeword of the temporary optimal fitness individual in the target group, and the individual corresponding to the equivalent codeword of the target other optimal fitness individual in the target group is used as the new temporary optimal fitness individual of the target group;

[0044] If it is determined that the fitness of the target other best fitness individuals is less than or equal to the fitness of the temporary best fitness individual of the target group, the equivalent codeword of the target other best fitness individual is randomly overwritten with the whole codeword of another individual in the target group except the temporary best fitness individual, and the temporary best fitness individual in the target group remains unchanged;

[0045] The population obtained after step S2046 is completed is used as the first The final updated population corresponds to the iterative processing.

[0046] Optionally, the value range of a is a≥8.

[0047] Optionally, the communication quality indicator is:

[0048] The sum of the communication rates between each user terminal in the user terminal cluster and the metasurface antenna;

[0049] Alternatively, a weighted sum of the communication rates between each user terminal in the user terminal cluster and the metasurface antenna;

[0050] or, the minimum value of the communication rates between each user terminal in the user terminal cluster and the metasurface antenna;

[0051] Alternatively, a weighted sum of the signal-to-interference-and-noise ratios of communications between each user terminal in the user terminal cluster and the metasurface antenna;

[0052] Alternatively, a weighted sum of the received powers of the reference information communicated between each user terminal in the user terminal cluster and the metasurface antenna.

[0053] In a second aspect, an embodiment of the present disclosure provides a beamforming system for a metasurface antenna, wherein the beamforming system is configured to implement the beamforming method provided in the first aspect, and the beamforming system includes:

[0054] A construction module is configured to construct a beamforming model of the metasurface antenna; the goal in the beamforming model is to maximize the communication quality index between the user terminal cluster and the metasurface antenna, the decision variable in the beamforming model is the state codeword corresponding to the N antenna units included in the metasurface antenna, the state codeword is represented by b bits, where b is a positive integer, and the user terminal cluster includes at least one user terminal;

[0055] A solution module is configured to solve the beamforming model based on a binary bat algorithm to obtain a corresponding current optimal solution; in the binary bat algorithm solution process, the individual in the population is an overall codeword composed of state codewords corresponding to the N antenna units arranged in sequence, and the fitness of the individual in the population is a communication quality index between the user-end cluster and the super-surface antenna when the overall codeword corresponding to the individual is deployed on the super-surface antenna, and the optimal solution includes the optimal value of the state codeword of each antenna unit;

[0056] A deployment module is configured to deploy the metasurface antenna according to the current optimal solution.

[0057] In a third aspect, an embodiment of the present disclosure further provides a computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps in the beamforming method provided in the first aspect are implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1A schematic diagram of a structure of a metasurface antenna-assisted multi-user wireless communication system;

[0059] Figure 2 A schematic diagram for channel modeling of a metasurface antenna;

[0060] Figure 3 A flow chart of a beamforming method for a metasurface antenna provided in an embodiment of the present disclosure;

[0061] Figure 4 This is a flow chart of an optional implementation method of step S2 in the embodiment of the present disclosure;

[0062] Figure 5 This is a flowchart of an optional implementation scheme of step S204 in the embodiment of the present disclosure;

[0063] Figure 6 A schematic diagram of modeling during simulation in the present disclosure;

[0064] Figure 7 Schematic diagram for defining spatial angles in simulation;

[0065] Figure 8 It is a schematic diagram for comparing the simulation and rate change curves under scenario 1 in the present disclosure;

[0066] Fig. 9 It is a front view of the simulated 3D directional diagram under scenario 1 in the present disclosure;

[0067] Fig.10 A top view of a simulated 3D directional diagram under scenario 1 in the present disclosure;

[0068] Fig.11 and Fig.12 These are two simulated beam 2D ​​section patterns under scenario 1 in the present disclosure;

[0069] Fig.13 It is a schematic diagram for comparing the simulation and rate change curves under scenario 2 in the present disclosure;

[0070] Fig.14 It is a front view of the simulated 3D directional diagram under scenario 2 in the present disclosure;

[0071] Fig.15 It is a top view of the simulated 3D directional diagram under scenario 2 in the present disclosure;

[0072] Fig.16 and Fig.17 These are two simulated beam 2D ​​section patterns under scenario 2 in the present disclosure;

[0073] Fig.18 A structural block diagram of a beamforming system for a metasurface antenna provided in an embodiment of the present disclosure;

[0074] Fig.19 The figure is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0075] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, the present disclosure is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0076] Unless otherwise defined, the technical terms or scientific terms used in the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, similar words such as "one", "one" or "the" do not indicate quantitative restrictions, but indicate that there is at least one. "Including" or "comprising" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0077] In each of the accompanying drawings, identical elements are represented by similar reference numerals. For the sake of clarity, the various parts in the accompanying drawings are not all drawn to scale. In addition, some well-known parts may not be shown in the drawings.

[0078] Many specific details of the present disclosure are described below to provide a clearer understanding of the present disclosure. However, as those skilled in the art will appreciate, the present disclosure may be implemented without following these specific details.

[0079] Figure 1 Figure 1 is a schematic diagram of a structure of a metasurface antenna-assisted multi-user wireless communication system. Figure 1 As shown, in practical applications, the role of metasurface antennas in communication systems can be divided according to their uses, mainly into receiving-end interference suppression and transmitting-end beamforming. The present invention mainly focuses on the application of metasurface antennas as beamforming transmitting antennas.

[0080] In the beamforming research, the metasurface antenna assisted multi-user wireless communication system can be analyzed and modeled first, see Figure 1 As shown; the metasurface antenna units are evenly distributed in rows and columns, and the number of rows is , the number of columns is ,Depend on The antenna units form an array, and the vertical spacing between units is , the horizontal spacing is , located at Line The phase shift of the antenna elements in the column is .

[0081] Assuming that the number of phase quantization bits of the antenna unit is b (b is a positive integer), the metasurface antenna array The phase shift matrix can be expressed as follows:

[0082] ;

[0083] Among them, the phase shift value of each antenna unit is in the b-bit quantization interval, so the full search space size for the entire antenna array is To control the beam direction of the emitted electromagnetic wave, it is actually to control the phase shift of each antenna unit, that is, to determine the phase shift matrix of the metasurface antenna array. .

[0084] Figure 2 A schematic diagram of channel modeling for metasurface antennas. Figure 2 As shown, in terms of channel modeling, the communication far-field model is adopted, and the channel between the metasurface antenna and the kth user is described as , and only the Los direct path is considered here; for the convenience of description, the metasurface antenna communication model is analyzed in the spatial coordinate system.

[0085] The metasurface antenna array units are evenly distributed, N z Row N y The array of columns is placed on the yoz plane, and the coordinate origin is set at the lower left corner unit position of the array. is the distance between two adjacent units (the vertical distance and horizontal distance are equal), is the azimuth of the steering vector relative to the hypersurface (the angle between the projection of the steering vector on the xoy plane and the x-axis), is the pitch angle of the steering vector relative to the metasurface (the angle between the steering vector and the z-axis), is the wavelength of the electromagnetic wave. Then the guidance vector of the metasurface antenna at point A is It is expressed as:

[0086] ;

[0087] Channel Matrix It can be expressed as follows:

[0088] ;

[0089] in, represents the path loss coefficient.

[0090] Furthermore, the signal-to-interference-and-noise ratio (SINR) at the user end can be obtained. and the total communication rate :

[0091]

[0092] At this time, the problem P1 to be solved by the present invention can be described as follows:

[0093] ;

[0094] For problem P1, its objective function and constraints are non-convex, and the channel information is unknown, so it is very difficult to solve this problem. For this kind of problem with discrete constraints, some researchers have proposed to relax the constraints to the continuous space for solution, and quantize the final phase results to the discrete domain. Specifically, the fractional programming method can be used to solve this problem. Some researchers have proposed an iterative algorithm based on Lagrange dual transformation and quadratic transformation. By transforming the original problem, it is simplified into several convex problems and the closed-form solutions of each sub-problem are derived. The computational complexity of this method is high. , and the performance of the solved result will be greatly reduced after quantization.

[0095] In addition, the premise of using the above solution is that channel estimation is required. Only after obtaining the channel information can subsequent calculations be performed. At present, the channel estimation methods can be mainly divided into blind estimation and non-blind estimation. Blind estimation refers to obtaining the estimated value of the channel through some information processing technology without using pilot information. This method has a very slow convergence speed and is inaccurate in time-varying fast fading channels compared to traditional non-blind estimation methods. Therefore, the current mainstream channel estimation method is still the traditional pilot-based non-blind estimation method. This method requires baseband information to be obtained through demodulation for processing. However, since the metasurface antenna itself has no information processing capabilities and only has one RF chain, channel estimation is very difficult and difficult to implement, making it difficult to actually implement the technology.

[0096] In addition, some researchers have focused on the direct optimization of discrete phases and proposed some beamforming algorithms based on discrete phases that do not require channel information. The simplest method is to traverse the phase state combinations of all antenna units, calculate the user end received signal to interference noise ratio and sum rate corresponding to each phase matrix, and compare the antenna unit phase state matrix with the maximum sum rate. Although this method is simple, its time complexity is , the time cost is very large, and the complexity grows exponentially with the number of units, so it is not applicable to larger arrays. Some researchers have also proposed random maximum sampling method and sample mean method based on the idea of ​​mathematical statistics. Although this type of method does not require channel information, it requires a large number of samples to ensure a good result, and the results are relatively random and the performance is unstable.

[0097] In summary, it is found that in the research of beamforming of metasurface antennas, one solution is to estimate the channel first and perform mathematical operations based on the existing channel. Its calculation complexity is high and the system implementation is difficult. Another method is a blind beamforming method based on discrete phase, which has the problems of high time complexity and poor performance.

[0098] In order to effectively improve or even completely solve at least one of the technical problems existing in the related technologies described above, the present disclosure provides a beamforming method, system and computer-readable medium for a metasurface antenna, which will be described below with reference to specific examples.

[0099] Figure 3 The present invention provides a flow chart of a beamforming method for a metasurface antenna according to an embodiment of the present invention. Figure 3 As shown, it is characterized in that beamforming includes:

[0100] Step S1: construct a beamforming model of the metasurface antenna.

[0101] The goal in the beamforming model is to maximize the communication quality index between the user-end cluster and the metasurface antenna. The decision variable in the beamforming model is the state codeword corresponding to each of the N antenna units included in the metasurface antenna. The state codeword is represented by b bits, where b is a positive integer. The user-end cluster includes at least one user end, and the user end and the metasurface antenna can communicate wirelessly using Bluetooth, WIFI, etc.

[0102] It should be noted that the communication quality index in the present disclosure is an indicator used to reflect the communication quality between the user-end cluster as a whole and the metasurface antenna. The larger the value, the better the communication quality.

[0103] Step S2: Solve the beamforming model based on the binary bat algorithm to obtain the corresponding current optimal solution.

[0104] Among them, in the binary bat algorithm solution process, the individuals in the population are the overall codewords composed of the state codewords corresponding to N antenna units arranged in sequence, and the optimal solution includes the optimal value of the state codeword of each antenna unit.

[0105] Bat Algorithm (BA) is a heuristic algorithm for global optimization proposed in recent years, which is inspired by the echolocation behavior of bats. Compared with other heuristic algorithms (such as genetic algorithms, particle swarm algorithms, etc.), it has more superior performance. Bat algorithm has good convergence and global search capabilities, and has shown certain advantages in solving high-dimensional complex problems. Therefore, the solution proposed in the present invention is developed on the basis of the binary bat algorithm, and the original problem of maximum sum rate is transformed into an N-dimensional maximum value search problem.

[0106] In the disclosed embodiment, the fitness of an individual in the population is a communication quality indicator between the user-end cluster and the metasurface antenna when the overall codeword corresponding to the individual is deployed on the metasurface antenna. In other words, the corresponding fitness function can be pre-set based on the target in the beamforming model.

[0107] In some embodiments, the communication quality indicator between the user-end cluster and the metasurface antenna may adopt one of the following parameters:

[0108] 1) The communication quality index is the sum of the communication rates between each user terminal in the user terminal cluster and the metasurface antenna.

[0109] 2) The communication quality index is the weighted sum of the communication rates between each user terminal in the user terminal cluster and the metasurface antenna.

[0110] 3) The communication quality index is the minimum value of the communication rate between each user terminal in the user terminal cluster and the metasurface antenna.

[0111] 4) The communication quality indicator is the weighted sum of the signal to interference plus noise ratio (SINR) between each user terminal in the user terminal cluster and the metasurface antenna.

[0112] 5) The communication quality indicator is the weighted sum of the reference signal receiving power (RSRP) of the communication between each user terminal in the user terminal cluster and the metasurface antenna.

[0113] In some embodiments, the fitness of any individual in the population is determined based on the following steps: first, the overall codeword corresponding to the individual is deployed on the metasurface antenna (the overall codeword records the state code corresponding to each antenna unit, and the phase of the corresponding antenna unit can be determined based on the state code); then, feedback information sent by each user terminal is received, and the feedback information includes the signal-to-interference-noise ratio and / or reference information receiving power of the communication between the corresponding user terminal and the metasurface antenna; then, the fitness corresponding to the individual is determined based on the feedback information fed back by all user terminals.

[0114] It should be noted that when the communication quality index between the user-end cluster and the metasurface antenna is sampled as shown in any of the above-listed (1) to (3), the corresponding communication rate can be determined based on the signal-to-interference-noise ratio between the user end and the metasurface antenna, and then the sum of the communication rates is calculated, or the weighted sum of the communication rates is calculated, or the minimum value of the communication rate is selected.

[0115] Among them, The communication rate between the user end and the metasurface antenna :

[0116] ;

[0117] and is a positive integer, Indicates the number of all clients. Indicates The signal-to-interference-noise ratio of the communication between the user end and the metasurface antenna.

[0118] Take the communication quality indicator as the sum of the communication rates between each user terminal in the user terminal cluster and the metasurface antenna as an example. At this time, the sum of the communication rates is :

[0119] ;

[0120] Of course, the five communication quality indicators exemplified above are only some optional implementation schemes in the present disclosure, which will not limit the technical solutions of the present disclosure. In practical applications, corresponding parameters can be pre-selected or designed according to actual needs to be used as communication quality indicators; for example, communication quality indicators can also be designed based on the wireless signal strength between the user terminal and the metasurface antenna. Examples are not given one by one here.

[0121] Step S3: deploy the metasurface antenna according to the current optimal solution.

[0122] The solution proposed in this invention is mainly based on the heuristic algorithm of the bat algorithm, which solves the problem of how to determine the phase matrix of the antenna unit when the metasurface antenna assists multi-user communication, and can be well applied to the beamforming of multi-users of the metasurface antenna. Compared with the traditional discrete array exhaustive search algorithm, the time complexity is greatly reduced, from the original Reduced to are the population size and the maximum number of iterations respectively). In addition, since this scheme does not require channel estimation, the process is simple and has high universality, it has good practical value.

[0123] Figure 4 FIG. 4 is a flow chart of an optional implementation method of step S2 in the embodiment of the present disclosure. Figure 4 As shown, in some embodiments, step S2 includes:

[0124] Step S201, initializing algorithm parameters of the binary bat algorithm.

[0125] Among them, the algorithm parameters include: sound wave frequency, pulse emission rate and pulse loudness.

[0126] In step S201, the range of sound wave frequency, the initial value of pulse emission rate and the initial value of pulse loudness can be set accordingly for use in subsequent iterative processing. Of course, in step S201, the maximum number of iterations and the number of individuals included in the population in the subsequent iterative processing can also be set.

[0127] Step S202: Generate an initialization population including a plurality of individuals and use it as the original population corresponding to the first iteration process.

[0128] Step S203: determine whether the original population corresponding to the current iterative process meets the preset iteration termination condition.

[0129] If it is determined that the original population corresponding to the current iterative processing does not meet the iteration termination condition, step S204 is executed; if it is determined that the original population corresponding to the current iterative processing meets the iteration termination condition, step S206 is executed.

[0130] In practical applications, the iteration termination condition can be preset according to actual needs; for example, the iteration process ends when the original population processed by the current iteration meets at least one of the following iteration termination conditions:

[0131] 1) Current number of iterations Greater than the preset maximum number of iterations .

[0132] 2) The fitness value of the individual with the best fitness in the original population of the current iteration is greater than or equal to the preset fitness threshold.

[0133] In the present disclosure, the best fitness individual in a population refers to an individual with the largest fitness value in the entire population, and the best fitness individual in a group (see below) refers to an individual with the largest fitness value in the entire group.

[0134] Step S204: calibrate the first The overall codeword of the individuals in the original population corresponding to the first iteration is updated to obtain The final updated population corresponding to the iterative processing is and is an integer, The preset maximum number of iterations.

[0135] Step S205: The final updated population corresponding to the iterative processing is taken as the The original population corresponding to the iterative processing, and the current number of iterations Add 1 to update.

[0136] After step S205 is completed, step S203 is executed.

[0137] Step S206: Output The best fitness individual in the original population of the iteration is used to obtain the current optimal solution.

[0138] The overall codeword corresponding to the individual with the best fitness records the state codeword corresponding to each antenna unit, which can be used as the optimal solution for the subsequent step S3 to deploy the metasurface antenna.

[0139] Optionally, the initial population consists of 2 b groups, each group includes a individuals, a is a positive integer. In the initialization population, the first state codewords of a individuals in the same group are the same, the first state codewords of any two individuals in different groups are different, and the last N-1 state codewords of each individual are randomly generated.

[0140] In step S204, During the updating process of the overall codeword of the individuals in the original population corresponding to the iteration processing, the first state codeword of each individual in the original population corresponding to each iteration processing remains unchanged.

[0141] In some embodiments, the value range of a is a≥8. For example, if a is 8, the number of individuals in the population is 8*2. b The larger the value of a is, the better the final result is, but the longer the algorithm runs. Therefore, in the present disclosure, the value of a can be set and adjusted in advance according to actual needs.

[0142] The value of b is set according to the phase adjustment accuracy of the antenna unit. Generally, the larger the value of b is, the higher the phase adjustment accuracy of the antenna unit is.

[0143] Figure 5 FIG. 4 is a flow chart of an optional implementation scheme of step S204 in the embodiment of the present disclosure. Figure 5 As shown, in some embodiments, step S204 specifically includes:

[0144] Step S2041: Determine the first The sound wave frequencies of each individual in the original population corresponding to the iterative processing are:

[0145] ;

[0146] Indicates The first iterative process corresponds to the first The sound frequency of an individual, and is an integer, and Respectively represent the preset minimum sound wave frequency and maximum sound wave frequency, Represents a random number in the range [0,1].

[0147] Step S2042: While keeping the first state codeword of each individual in the original population unchanged, The individuals in the original population corresponding to the first iteration are updated, and the The initial updated population corresponding to the iterative processing is:

[0148] ;

[0149] in, Indicates The first iteration corresponds to the first updated population The individual The value of bits, and is a positive integer, Indicates The first iterative process corresponds to the first The individual The value of bits, Express The result of the inversion process is Represents the first random number in the range [0,1], represents the transfer function for binary position updates, Indicates The first iteration corresponds to the first updated population The individual The speed of bit movement, Indicates The first iteration corresponds to the first update of the best fitness individual in the population. The value of bits.

[0150] Step S2043: While maintaining the first state codeword of each individual in the initial update population unchanged, For each group in the initial update population corresponding to the first iteration, the following formula is used to update the individuals in the group, and the first The population is further updated corresponding to each iterative process.

[0151] ;

[0152] Indicates The corresponding iterative processing further updates the population in the first In the group The individual The value of bits, Indicates The first iteration process corresponds to the initial update population in the first In the group The individual The value of bits, Indicates The first iteration corresponds to the first updated population The best fitness individual in the group is The value of bits, Indicates that the value is within the range The second random number within Indicates that the The group in The pulse emission rate configured during the iteration process, and is a positive integer, And it is a positive integer.

[0153] Step S2044: For each group, detect The group in The second best fitness corresponding to the iteration process Is it greater than the corresponding first optimal fitness? , and detection for The group in The values ​​generated during the iteration are in the range The third random number within Is it less than the pre-set The group in The configured impulse loudness during the iteration process .

[0154] Among them, the first optimal fitness For the The first iteration process corresponds to the initial update population The fitness of the individual with the best fitness in the group, the second best fitness For the The next iteration process further updates the population The fitness of the individual with the best fitness in the group.

[0155] Step S2045: According to the detection results of each group in step S2044, determine the The configured pulse emission rate and pulse loudness during the iteration process.

[0156] Among them, for the Group, if The second best fitness corresponding to the group Greater than the corresponding first optimal fitness , and the first The group in The third random number in the iteration Less than impulse loudness , then increase the pulse emission rate The processing result is taken as the The group in The pulse emission rate configured during the iteration process , and reduce pulse loudness The processing result is taken as the The group in The configured impulse loudness during the iteration process .

[0157] In some embodiments, the pulse emission rate and pulse loudness can be adjusted based on the following formula: The group in The pulse emission rate configured during the iteration process and impulse loudness :

[0158] ;

[0159] in, Indicates the initial value of the pulse emission rate configured in the initialization phase, and These are all preset constants. and .

[0160] For Group, if The second best fitness corresponding to the group Less than or equal to the corresponding first optimal fitness , or The group in The third random number in the iteration Greater than or equal to impulse loudness , then the pulse emission rate As the The group in The pulse emission rate configured during the iteration process , and the impulse loudness As the The group in The configured .

[0161] That is to say, when And the corresponding third random number generated When , the pulse transmission rate of the corresponding group will be increased and the pulse loudness will be reduced. or When , the pulse transmission rate of the corresponding group is maintained and the pulse loudness is reduced.

[0162] Step S2046: The next iteration corresponds to further updating the population and determining the The final updated population corresponds to the iterative processing.

[0163] In some embodiments, the The further updated population corresponding to the iterative processing is directly used as the The final updated population corresponds to the iterative processing.

[0164] In other embodiments, the The further updated population corresponding to the iterative processing is further updated (for example, using crossover processing, mutation processing, selection processing, etc.), and the processing result is used as the final updated population.

[0165] As an optional example, step S2046 specifically includes: Each group in the further updated population corresponding to the iterative processing is taken as the target group in turn, and the following steps S20461 to S20462 are used to update the first group. The corresponding iterative processing further updates the target group in the population:

[0166] Step S20461: The corresponding iterative processing further updates the 2 in the population except the target group b Each of the -1 other groups is used as the target other group, and the equivalent codeword corresponding to the transfer of the best fitness individual in the target other group to the target group is obtained. The equivalent codeword refers to: the updated overall codeword obtained by superimposing the same conversion codeword on each state codeword in the best fitness individual in the target other group, wherein the conversion codeword is equal to the difference between the first state codeword of the individual in the target group and the first state codeword of the best fitness individual in the target other group.

[0167] It should be noted that, for an antenna element array, if the state codewords corresponding to the same antenna element in two overall codewords have the same difference (i.e., the conversion codeword in the present disclosure), it means that when the two overall codewords are deployed separately in the antenna array, the phase of each antenna element differs by the same phase, and at this time, the sum rate calculated according to the fitness function (i.e., the sum of the communication rates, also called the "sum rate") is the same. In other words, the individual state codewords and their corresponding equivalent codes have the same fitness.

[0168] Step S20462: The optimal fitness individual in the target group in the population is further updated corresponding to the iterative processing, and is used as the initial temporary optimal fitness individual in the target group. b -1 other groupings corresponding to 2 b Each of the -1 optimal fitness individuals is used as the target other optimal fitness individuals, and the following processes are performed in sequence:

[0169] Determine whether the fitness of other optimal fitness individuals of the target group is greater than the fitness of the temporary optimal fitness individual of the target group.

[0170] Among them, if it is judged that the fitness of the target other optimal fitness individuals is greater than the fitness of the temporary optimal fitness individual of the target group, then the overall codeword of the temporary optimal fitness individual in the target group is randomly covered with another overall codeword in the target group, and the equivalent codeword of the target other optimal fitness individual is covered with the overall codeword of the temporary optimal fitness individual in the target group, and the individual corresponding to the equivalent codeword of the target other optimal fitness individual in the target group is used as the new temporary optimal fitness individual of the target group;

[0171] If it is determined that the fitness of the target's other best fitness individuals is less than or equal to the fitness of the target group's temporary best fitness individual, the equivalent codeword of the target's other best fitness individuals will randomly cover the overall codeword of another individual in the target group except the temporary best fitness individual, and the temporary best fitness individual in the target group remains unchanged.

[0172] The population obtained after step S2046 is completed is used as the first The final updated population corresponds to the iterative processing.

[0173] The technical solution of the present invention is improved on the basis of the binary bat algorithm: the first is the adjustment of the algorithm parameters, which are optimized according to the problems raised by the current metasurface antenna specific scenario, so that the algorithm is more in line with the problem solving; the second is to modify the algorithm itself in combination with the characteristics of the metasurface antenna. Since each unit of the metasurface antenna array superimposes the same phase without affecting the final result, in the solution of the present invention, the population is divided into two b Each group is iteratively updated independently, and then the optimal codewords in different groups are added to other groups through the concept of equivalent codewords. This enhances the diversity of individuals, prevents the algorithm from converging prematurely, and shares the iterative results between different populations, improving the final performance.

[0174] In order to verify the effect of the technical solution of the present invention, a simulation of the technical solution of the present invention was carried out. Figure 6 This is a schematic diagram of modeling during simulation in the present disclosure. Figure 7 A schematic diagram for defining spatial angles in simulation. Figure 6 and Figure 7 As shown, the simulation parameters are as follows:

[0175] Metasurface Antenna Parameters: Number of Rows ,Number of columns , the total number of antenna units , the antenna units in the array are evenly distributed in rows and columns, and the row spacing of the antenna units is , the column spacing of the antenna elements .

[0176] Spatial position: In the spatial coordinate system, the antenna is placed at (0, 0, 0) (with the lower left corner unit of the antenna unit as the reference point), the unit length is 1m, the metasurface antenna array is placed on the yoz plane, and the projection of each user on the horizontal plane is distributed on a circle with a radius of 4m located at (5, 1, 0) on the horizontal plane. The height of the user is 0 to 4m.

[0177] Transmitted signal: frequency .

[0178] Channel model: far-field model.

[0179] Noise level: Noise power is -20dBm.

[0180] Antenna unit parameters: 2-bit quantization.

[0181] Number of users: 2.

[0182] Algorithm related parameters: population size is 32, maximum number of iterations is 50, the range of sound wave frequency F is [0,2], the initial value of pulse emission rate is 0.1, the initial value of pulse loudness is 1.5, the parameters involved in the pulse emission rate and pulse loudness update process The value is 0.1. The value is 0.8.

[0183] In the simulation, in order to verify the performance of the algorithm proposed in the present invention, a beamforming algorithm based on fractional programming (FP) was designed as a comparative scheme. At present, this scheme has been widely studied and discussed in the problem of multi-user beamforming on intelligent metasurfaces, but it needs to obtain channel information before performing calculation iterations (the simulation assumes that the channel is known and directly uses the accurate channel), and when applied to antenna units with discrete phases, the process of quantizing the nearest point projection (NPP) has a greater impact on its performance. In contrast, the algorithm proposed in the present invention does not require channel estimation and has better performance on metasurface antenna arrays with discrete phases.

[0184] Due to the algorithm limitations in the electromagnetic simulation software, the azimuth angle specified in the software is the angle between the beam and the x-axis (the normal to the surface where the antenna is located), the elevation angle is the angle between the projection of the beam on the yoz plane (the plane where the antenna is located) and the z-axis. Figure 7 As shown, the angle definition is inconsistent with that in the previous channel calculation. Therefore, for the convenience of comparison, the azimuth angle and pitch angle in the following simulation data are calculated according to the angle defined in the simulation software.

[0185] Figure 8 It is a schematic diagram comparing the simulation and rate change curves under scenario 1 in the present disclosure. Fig. 9 This is a front view of the simulated 3D directional diagram under scenario 1 in the present disclosure. Fig.10 This is a top view of the simulated 3D directional map under scenario 1 in the present disclosure. Fig.11 and Fig.12 These are two simulated beam 2D ​​cross-sectional patterns in scenario 1 of the present disclosure. Fig.13 It is a schematic diagram for comparing the simulation and rate change curves under scenario 2 in the present disclosure. Fig.14 This is a front view of the simulated 3D directional diagram under scenario 2 in the present disclosure. Fig.15 This is a top view of the simulated 3D directional map under scenario 2 in the present disclosure. Fig.16 and Fig.17 These are two simulated beam 2D ​​section patterns under scenario 2 in this disclosure. Figures 8 to 17As shown, during the simulation, two groups of scenarios were set for simulation (parameters are shown in Tables 1 and 2 below). Since the algorithm proposed in the present invention has randomness in the selection of initial values, for the convenience of comparison, the average value of the results of 10 runs was taken to plot the sum rate (the communication quality indicator is the sum of the communication rates between each user terminal in the user terminal cluster and the metasurface antenna) change curve, and the directional pattern of the antenna array was simulated in the electromagnetic simulation software.

[0186]

[0187] Table 1. Scenario 1 simulation parameters

[0188]

[0189] Table 2. Scenario 2 simulation parameters

[0190] See also Figure 8 As shown, in terms of the performance of multi-user and rate optimization, compared with the beamforming algorithm based on fractional programming, the algorithm proposed in the present invention improves the rate by about 2bps / Hz after 50 iterations, which is about 15.7% higher than the comparison scheme.

[0191] See also Fig. 9 and Fig.10 As shown, from the 3D directional diagram of the scenario 1 simulation experiment, it can be clearly seen that there are two strong beam main lobes, namely the two beam directions marked by arrows in the figure.

[0192] See also Fig.11 and Fig.12 As shown in the figure, in order to verify the direction of the main lobe of the beam, the peak of the 3D directional diagram was sectioned in the simulation. The direction of the main lobes of the two beams (according to indicates) ,Compared with the preset angles in Table 1, the deviation is small and the direction is consistent.

[0193] See also Fig.13 As shown in the figure, in terms of the performance of multi-user and rate optimization, compared with the beamforming algorithm based on fractional planning, in scenario 2, after 50 iterations, the rate increased by about 5bps / Hz, which is about 35% higher than the comparison solution.

[0194] See also Fig.14 and Fig.15 As shown, from the 3D directional diagram of the scenario 1 simulation experiment, it can be clearly seen that there are two strong beam main lobes, namely the two beam directions marked by arrows in the figure.

[0195] See also Fig.16 and Fig.17As shown in the figure, in order to verify the direction of the main lobe of the beam, the peak of the 3D directional diagram is sectioned in the simulation. The direction of the main lobes of the two beams (according to indicates) ,Compared with the preset angle in Table 1, the deviation is within 10°, and the direction is consistent.

[0196] Fig.18 This is a structural block diagram of a beamforming system for a metasurface antenna provided in an embodiment of the present disclosure. Fig.18 As shown, the beamforming system is configured to implement the beamforming method provided in the previous embodiment, and the beamforming system includes: a construction module, a solution module and a deployment module.

[0197] Among them, the construction module is configured to construct a beamforming model of the metasurface antenna; the goal in the beamforming model is to maximize the communication quality index between the user-end cluster and the metasurface antenna, and the decision variables in the beamforming model are the state codewords corresponding to the N antenna units included in the metasurface antenna, and the state codeword is represented by b bits, where b is a positive integer, and the user-end cluster includes at least one user end.

[0198] The solution module is configured to solve the beamforming model based on the binary bat algorithm to obtain the corresponding current optimal solution; in the binary bat algorithm solution process, the individuals in the population are the overall codewords composed of the state codewords corresponding to N antenna units arranged in sequence, and the fitness of the individuals in the population is the communication quality index between the user-end cluster and the super-surface antenna when the overall codeword corresponding to the individual is deployed on the super-surface antenna, and the optimal solution includes the optimal value of the state codeword of each antenna unit.

[0199] The deployment module is configured to deploy the metasurface antenna according to the current optimal solution.

[0200] For the detailed description of the above functional modules, please refer to the contents of the previous embodiments, which will not be repeated here.

[0201] Based on the same inventive concept, an embodiment of the present disclosure also provides an electronic device. Fig.19 FIG. 1 is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. Fig.19 As shown, an embodiment of the present disclosure provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement any beamforming method in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, and are configured to implement information interaction between the processor and the memory.

[0202] Among them, the processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU), etc.; the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory (FLASH); the I / O interface (read-write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus), etc.

[0203] In some embodiments, the processor 101 , the memory 102 , and the I / O interface 103 are connected to each other via a bus 104 , and further connected to other components of the computing device.

[0204] In some embodiments, the one or more processors 101 include a field programmable gate array.

[0205] According to an embodiment of the present disclosure, a computer-readable medium is further provided. The computer-readable medium stores a computer program, wherein when the program is executed by a processor, the steps in any beamforming method in the above embodiments are implemented.

[0206] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, including a computer program carried on a machine-readable medium, the computer program containing a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above-mentioned functions defined in the system of the present disclosure are executed.

[0207] It should be noted that the computer-readable medium shown in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0208] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of the code, and the aforementioned module, program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0209] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present disclosure, but the present disclosure is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and substance of the present disclosure, and these modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. A beamforming method for a metasurface antenna, characterized in that: The beamforming method comprises: Step S1, constructing a beamforming model of the metasurface antenna; the goal in the beamforming model is to maximize the communication quality index between the user terminal cluster and the metasurface antenna, the decision variable in the beamforming model is the state codeword corresponding to the N antenna units included in the metasurface antenna, the state codeword is represented by b bits, b is a positive integer, and the user terminal cluster includes at least one user terminal; Step S2, solving the beamforming model based on the binary bat algorithm to obtain the corresponding current optimal solution; in the binary bat algorithm solving process, the individual in the population is an overall codeword composed of the state codewords corresponding to the N antenna units arranged in sequence, and the fitness of the individual in the population is the communication quality index between the user-end cluster and the super-surface antenna when the overall codeword corresponding to the individual is deployed on the super-surface antenna, and the optimal solution includes the optimal value of the state codeword of each antenna unit; Step S3, deploying the metasurface antenna according to the current optimal solution; The step S2 comprises: Step S201, initializing algorithm parameters of the binary bat algorithm, the algorithm parameters including: sound wave frequency, pulse emission rate and pulse loudness; Step S202: Generate an initialization population including a plurality of individuals and use it as the original population corresponding to the first iteration process; Step S203, determining whether the original population corresponding to the current iterative process meets the preset iteration termination condition; If it is determined that the original population corresponding to the current iterative processing does not meet the iteration termination condition, step S204 is executed; if it is determined that the original population corresponding to the current iterative processing meets the iteration termination condition, step S206 is executed; Step S204: calibrate the first The overall codeword of the individuals in the original population corresponding to the first iteration is updated to obtain The final updated population corresponding to the iterative processing is and is an integer, is the preset maximum number of iterations; Step S205: The final updated population corresponding to the iterative processing is taken as the The original population corresponding to the iterative processing, and the current number of iterations Add 1 to update. After step S205, execute step S203; Step S206: Output The best fitness individual in the original population of the iteration is used to obtain the current optimal solution.

2. The beamforming method according to claim 1, characterized in that: The initialization population includes 2 b groups, each group includes a individuals, a is a positive integer; In the initialized population, the first state code words of a individuals in the same group are all the same, the first state code words of any two individuals in different groups are all different, and the last N-1 state code words of each individual are randomly generated; In step S204, During the updating process of the overall codeword of the individuals in the original population corresponding to the iterative processing, the first state codeword of each individual in the original population corresponding to each iterative processing remains unchanged.

3. The beamforming method according to claim 2, characterized in that: Step S204 specifically includes: Step S2041: Determine the first The sound wave frequencies of each individual in the original population corresponding to the iterative processing are: ； Indicates The first iterative process corresponds to the first The sound frequency of an individual, and is an integer, and Respectively represent the preset minimum sound wave frequency and maximum sound wave frequency, Represents a random number in the range [0,1]; Step S2042: while keeping the first state codeword of each individual in the original population unchanged, The individuals in the original population corresponding to the first iteration are updated, and the The initial updated population corresponding to the iterative processing is: ； in, Indicates The first iteration corresponds to the first updated population The individual The value of bits, and is a positive integer, Indicates The first iterative process corresponds to the first The individual The value of bits, Express The result of the inversion process is Represents the first random number in the range [0,1], represents the transfer function for binary position updates, Indicates The first iteration corresponds to the first updated population The individual The speed of bit movement, Indicates The first iteration corresponds to the first update of the best fitness individual in the population. The value of bits; Step S2043: while maintaining the first state codeword of each individual in the initial update population unchanged, For each group in the initial update population corresponding to the first iteration, the following formula is used to update the individuals in the group, and the first The population is further updated corresponding to the iterative processing: ； Indicates The corresponding iterative processing further updates the population in the first In the group The individual The value of bits, Indicates The first iteration process corresponds to the initial update population in the first In the group The individual The value of bits, Indicates The first iteration corresponds to the first updated population The best fitness individual in the group is The value of bits, Indicates that the value is within the range The second random number within Indicates that the The group in The pulse emission rate configured during the iteration process, and is a positive integer, and is a positive integer; Step S2044: For each group, detect The group in The second best fitness corresponding to the iteration process Is it greater than the corresponding first optimal fitness? , and detection for The group in The values ​​generated during the iteration are in the range The third random number within Is it less than the pre-set The group in The configured impulse loudness during the iteration process , the first optimal fitness For the The first iteration process corresponds to the initial update population The fitness of the individual with the best fitness in the group, the second best fitness For the The next iteration process further updates the population The fitness of the individual with the best fitness in the group; Step S2045: According to the detection results of each group in step S2044, determine the The pulse emission rate and pulse loudness configured during the iterative processing; Among them, for the Group, if The second best fitness corresponding to the group Greater than the corresponding first optimal fitness , and the first The group in The third random number in the iteration Less than impulse loudness , then increase the pulse emission rate The processing result is taken as the The group in The pulse emission rate configured during the iteration process , and reduce pulse loudness The processing result is taken as the The group in The configured impulse loudness during the iteration process ; For Group, if The second best fitness corresponding to the group Less than or equal to the corresponding first optimal fitness , or The group in The third random number in the iteration Greater than or equal to impulse loudness , then the pulse emission rate As the The group in The pulse emission rate configured during the iteration process , and the impulse loudness As the The group in The configured ; Step S2046: The next iteration corresponds to further updating the population and determining the The final updated population corresponds to the iterative processing.

4. The beamforming method according to claim 3, characterized in that: In step S2045, the pulse emission rate is increased The processing result is taken as the The group in The pulse emission rate configured during the iteration process , and reduce pulse loudness The processing result is taken as the The group in The configured impulse loudness during the iteration process The steps specifically include: The following formula is used to determine the The group in The pulse emission rate configured during the iteration process and impulse loudness : ； in, Indicates the initial value of the pulse emission rate configured in the initialization phase, and These are all preset constants. and .

5. The beamforming method according to claim 3, characterized in that: Step S2046 specifically includes: Each group in the further updated population corresponding to the iterative processing is taken as the target group in turn, and the following steps are used to update the first group. The corresponding iterative processing further updates the target group in the population: Step S20461: The corresponding iterative processing further updates the 2 in the population except the target group b -1 other groups are each used as the target other group, and the equivalent codeword corresponding to the best fitness individual in the target other group is obtained when it is transferred to the target group, and the equivalent codeword refers to: each state codeword in the best fitness individual in the target other group is superimposed with the same conversion codeword to obtain the updated overall codeword, wherein the conversion codeword is equal to the difference between the first state codeword of the individual in the target group and the first state codeword of the best fitness individual in the target other group; Step S20462: The optimal fitness individual in the target group in the population corresponding to the iterative processing is further updated as the initial temporary optimal fitness individual in the target group. b -1 other groupings corresponding to 2 b Each of the -1 optimal fitness individuals is used as the target other optimal fitness individuals, and the following processes are performed in sequence: Determine whether the fitness of other target individuals with the best fitness is greater than the fitness of the target group's temporary best fitness individual; Wherein, if it is determined that the fitness of the target other optimal fitness individuals is greater than the fitness of the temporary optimal fitness individual of the target group, the overall codeword of the temporary optimal fitness individual in the target group is randomly covered with another overall codeword in the target group, and the equivalent codeword of the target other optimal fitness individual is covered with the overall codeword of the temporary optimal fitness individual in the target group, and the individual corresponding to the equivalent codeword of the target other optimal fitness individual in the target group is used as the new temporary optimal fitness individual of the target group; If it is determined that the fitness of the target other best fitness individuals is less than or equal to the fitness of the temporary best fitness individual of the target group, the equivalent codeword of the target other best fitness individual is randomly overwritten with the whole codeword of another individual in the target group except the temporary best fitness individual, and the temporary best fitness individual in the target group remains unchanged; The population obtained after step S2046 is completed is used as the first The final updated population corresponds to the iterative processing.

6. The beamforming method according to claim 2, characterized in that: The value range of a is a≥8.

7. The beamforming method according to any one of claims 1 to 6, characterized in that: The communication quality indicators are: The sum of the communication rates between each user terminal in the user terminal cluster and the metasurface antenna; Alternatively, a weighted sum of the communication rates between each user terminal in the user terminal cluster and the metasurface antenna; or, the minimum value of the communication rates between each user terminal in the user terminal cluster and the metasurface antenna; Alternatively, a weighted sum of the signal-to-interference-and-noise ratios of communications between each user terminal in the user terminal cluster and the metasurface antenna; Alternatively, a weighted sum of the received powers of reference information communicated between each user terminal in the user terminal cluster and the metasurface antenna.

8. A beamforming system for a metasurface antenna, characterized in that: The beamforming system is configured to implement the beamforming method according to any one of claims 1 to 7, and the beamforming system comprises: A construction module is configured to construct a beamforming model of the metasurface antenna; the goal in the beamforming model is to maximize the communication quality index between the user terminal cluster and the metasurface antenna, the decision variable in the beamforming model is the state codeword corresponding to the N antenna units included in the metasurface antenna, the state codeword is represented by b bits, b is a positive integer, and the user terminal cluster includes at least one user terminal; A solution module is configured to solve the beamforming model based on a binary bat algorithm to obtain a corresponding current optimal solution; in the binary bat algorithm solution process, the individual in the population is an overall codeword composed of state codewords corresponding to the N antenna units arranged in sequence, and the fitness of the individual in the population is a communication quality index between the user-end cluster and the super-surface antenna when the overall codeword corresponding to the individual is deployed on the super-surface antenna, and the optimal solution includes the optimal value of the state codeword of each antenna unit; A deployment module is configured to deploy the metasurface antenna according to the current optimal solution.

9. A computer-readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the steps in the beamforming method according to any one of claims 1 to 7 are implemented.

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