Interference suppression method of metasurface array, electronic device and communication system

Abstract

The present disclosure provides an interference suppression method, electronic device and communication system for a metasurface array, the method comprising: determining an interference suppression model for the metasurface array; the target in the interference suppression model is to maximize the signal index of the target signal, and the decision variable in the interference suppression model is the phase shift coefficient of N metasurface units of the metasurface array; solving the interference suppression model based on an improved quantum genetic algorithm to obtain a corresponding current optimal solution; in the process of solving the quantum genetic algorithm, the individuals in the population are individual codes composed of quantum bits corresponding to the N metasurface units respectively, and the fitness of the individuals in the population is the corresponding signal index obtained when the individual code of the individual is converted into a phase shift coefficient and applied to the metasurface array; converting the current optimal solution into a phase shift coefficient for the metasurface array.

Description

Technical Field

[0001] The present disclosure relates to the field of wireless communication technology, and in particular to an interference suppression method of a metasurface array, an electronic device, and a communication system. Background Art

[0002] In the information age, with the rapid development of the Internet of Things, 5G and future 6G communication technologies, people's demands for wireless communication networks are increasing dramatically, including supporting large-scale device connections, providing high device information transmission rates, and increasing network throughput. In order to meet the above needs, it is necessary to densely deploy ultra-large-scale multiple-input multiple-output (mMIMO) communication systems. However, in real applications, the deployment of such communication systems faces many challenges. Especially in the traditional sub-6GHz frequency bands as well as the millimeter wave and terahertz frequency bands, the design and deployment of large-scale MIMO communication systems that can serve multiple users simultaneously and have hundreds or even thousands of antenna elements face a series of practical difficulties, including high manufacturing costs, increased power consumption, limited physical size and shape, and restrictions on the deployment environment.

[0003] 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 technology (DMA / Dynamic Metasurface Antennas / RIS / Reconfigurable Intelligent Surface / Large Intelligent Surface / Software DefinedSurface / Metasurface / IRS / Intelligent Reflecting Surface / Reconfigurable Meta-Surfaces / Holographic MIMO, etc.) meets the above requirements. Metamaterials are a type of artificial materials whose physical properties (especially dielectric constant and magnetic permeability) can be customized and regulated according to specific needs. When metamaterials are designed and deployed in the form of planar structures, namely metasurfaces, these planar structural units, namely metasurface units, are arranged according to certain rules to form a metasurface array. By adjusting its effective parameters, the transmission, reception, reflection or refraction of electromagnetic waves can be transformed and regulated.

[0004] Dynamic Metasurface Antennas (DMA) are a new type of array antenna developed based on metamaterials in recent years. They are a special type of metasurface array. Metasurface antennas contain a large number of tunable metamaterial antenna elements that can be packaged in a small physical space and support a wide range of operating frequencies. Metasurface antennas have the ability to customize beams and can control transmitted and received signals in a flexible and dynamically configurable manner in the analog domain through simple, low-cost system hardware. In addition, compared with traditional antenna arrays (such as antenna arrays based on patch arrays and phase shifters), communication systems based on metasurface antennas require lower power and cost, and eliminate complex feeding networks and / or active phase shifters. Metasurface antennas have excellent characteristics such as low cost, low hardware complexity, and low power consumption, which makes metasurface antennas have good prospects in the application of ultra-large-scale MIMO transceiver in 6G wireless networks.

[0005] Figure 1 A schematic diagram of a multi-base station signal reception system model assisted by a metasurface antenna. Figure 1 As shown, in practical applications, metasurface antennas can be mainly used for interference suppression at the receiving end and beamforming at the transmitting end, while the present invention mainly focuses on the application of spatial filtering and interference suppression when the metasurface antenna is used as a receiving end antenna.

[0006] In interference suppression research, the wireless communication system assisted by metasurface antennas can be analyzed and modeled first. Figure 1 A typical downlink communication system has K base stations, each equipped with M = M y M z antenna, and a Figure 1 The DMA shown is used as a signal receiving antenna. DMA is composed of N = N y N z Units, N y is the number of rows in the array, N z is the number of columns in the array. In engineering applications, the phase shift of a DMA unit is usually quantized into discrete bits. The phase shift of a DMA quantized by τ bits can be 2 τ value, expressed as Therefore, the space size for the full search of the DMA phase shift coefficient is 2 τN The phase shift coefficient of DMA is expressed as Among them, each θ n ∈Φ τ represents the phase shift of the nth unit. The channel between the kth base station and DMA is represented by express:

[0007]

[0008] Among them, L k is the number of paths between the DMA and the kth base station, is the path loss coefficient of the lth path, and Represent the elevation angle (azimuth) at the DMA and base station antennas, respectively. In addition, and Represent the steering vectors of DMA and base station respectively. For DMA, its steering vector can be expressed as:

[0009]

[0010]

[0011]

[0012] where d y and d z are the unit cell spacing along the y-axis and z-axis respectively, and λ is the free space wavelength.

[0013] Current 5G macro base stations usually use hybrid interference suppression technology to generate 8 fixed analog beams corresponding to different angles. Denotes the corresponding base station simulation interference suppression coefficient. Let s k represents the downlink signal transmitted from the kth base station. t is regarded as the desired signal, while the signals of other base stations are regarded as interference signals. The signal received by DMA can be expressed as:

[0014]

[0015] in is the additive Gaussian noise (AWGN) at the DMA receiver, and θ t Indicates the phase shift coefficient of DMA for the target base station signal.

[0016] The objective of the present invention is to suppress interference at the receiving end and maximize the signal to interference plus noise ratio (SINR) of the target signal. The problem to be solved by the present invention can be expressed as:

[0017] (P1)

[0018]

[0019] where p k is the transmission power of the kth base station.

[0020] For this interference suppression receiving beamforming problem, the most classic mathematical optimization method is Second-Order Cone Programming (SOCP). The classic SOCP method is usually used to maximize the signal-to-interference-to-noise ratio (SINR) of beamforming under the constraint of limited total antenna power. However, in the DMA relay communication system, the DMA used for receiving has only a single RF chain and lacks the ability to adjust the transmit power of each antenna unit. In addition, the actual DMA phase shift is usually quantized, and the unit phase shift cannot be continuously adjusted, while the phase shift calculated by the classic SOCP method is continuous. For this discrete phase shift constraint problem, a common practice is to first relax the constraint to a continuous space for solution, and finally quantize the obtained phase result to a discrete domain using the Nearest Point Projection (NPP) method. However, this quantization will result in a large performance loss.

[0021] On the other hand, to solve this problem mathematically, it is first necessary to obtain complete and accurate channel information. At present, channel estimation methods are mainly divided into two categories: blind estimation and non-blind estimation. Blind estimation does not require the use of pilot information, but its convergence speed is slow and its timeliness is poor, and its effect is poor in fast fading channels with high time variability. In current practical scenarios, channel estimation mainly adopts non-blind estimation based on pilots. Pilot-based channel estimation calculates channel information by transmitting a small amount of training sequences, then demodulating baseband information and performing corresponding processing. However, the DMA relay communication system operates independently of the base station and does not interact directly with the base station. More importantly, in order to minimize the signal processing overhead, it avoids the modulation and demodulation operations of the wireless signal. Under these conditions, channel estimation faces great difficulties and it is difficult to obtain channel information from the base station. Therefore, traditional interference suppression methods that rely on detailed channel information (such as SOCP) are impractical in the actual system environment of the present invention.

[0022] In summary, there are two main difficulties in solving problem P1: First, to solve this problem mathematically, we first need to obtain complete and accurate channel information H k However, from a practical point of view, it is very challenging to obtain channel information in actual relay communication systems. Secondly, the quadratic objective function in this problem is non-convex, and the computational complexity of directly solving this problem is high. In addition, the discrete phase shift constraint further increases the complexity of the problem and the difficulty of solving it. Summary of the invention

[0023] In a first aspect, the present invention provides an interference suppression method for a metasurface array, comprising the following steps:

[0024] Determine an interference suppression model of the metasurface array; the goal in the interference suppression model is to maximize the signal index of the target signal, and the decision variable in the interference suppression model is the phase shift coefficient of the N metasurface units of the metasurface array;

[0025] Solving the interference suppression model based on a quantum genetic algorithm to obtain a corresponding current optimal solution; in the process of solving the quantum genetic algorithm, the individuals in the population are individual codes composed of quantum bits corresponding to the N metasurface units, and the fitness of the individuals in the population is a signal index corresponding to the phase shift coefficient when the individual code of the individual is converted into the phase shift coefficient and applied to the metasurface array;

[0026] The current optimal solution is converted into phase shift coefficients for the metasurface array.

[0027] Furthermore, the signal indicator is one or more of a signal-to-interference plus noise ratio SINR, a signal-to-interference plus noise ratio SIR, a carrier-to-interference plus noise ratio CINR, a communication data rate, and a symbol rate.

[0028] Furthermore, the interference suppression model is solved based on the quantum genetic algorithm to obtain the corresponding current optimal solution, which specifically includes:

[0029] Step S201, initializing algorithm parameters of the quantum genetic algorithm;

[0030] Step S202, generating an initial population including a plurality of individuals, and using it as the original population corresponding to the first iteration process;

[0031] Step S203, determining whether the original population corresponding to the current iterative process meets the preset iteration termination condition;

[0032] 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 S205 is executed;

[0033] Step S204: Perform collapse operation on all quantum individuals in the original population corresponding to the g-th iteration process to obtain a binary deterministic solution, convert the binary deterministic solution into a phase shift coefficient and apply it to the metasurface array respectively, and record the corresponding signal index as fitness, record the current optimal fitness individual, and calculate the similarity between all individuals and the current optimal fitness individual, calculate the rotation angle of each individual according to the fitness of each individual and the similarity between each individual and the current optimal fitness individual, and perform rotation update processing on the original population corresponding to the g-th iteration process according to the rotation angle of each individual, and obtain the final updated population corresponding to the g-th iteration process, g∈[1,g max ] and is an integer, g max is the preset maximum number of iterations;

[0034] Step S205: taking the final updated population corresponding to the g-th iteration as the original population corresponding to the g+1-th iteration, and adding 1 to the current iteration number g for updating;

[0035] After step S205, execute step S203;

[0036] Step S206: output the individual with the best fitness in the original population of the g-th iteration to obtain the current optimal solution.

[0037] Furthermore, an initial population including multiple individuals is generated, specifically including: using a dynamic block initialization strategy to obtain the initial optimal phase shift coefficients of all metasurface units of the metasurface array, converting the initial optimal phase shift coefficients of all metasurface units of the metasurface array obtained using the dynamic block initialization strategy into individual codes, adding them to the initial population, and randomly initializing other individuals in the population.

[0038] Furthermore, the dynamic block initialization strategy is used to obtain the initial optimal phase shift coefficients of all metasurface units of the metasurface array, specifically including:

[0039] S2021, setting the initial state of all metasurface units of the metasurface array and the range and initial position of the dynamic block;

[0040] S2022, applying the cyclic phase offset to the current phase shift coefficients of all metasurface units in the block in sequence, and recording corresponding signal indicators;

[0041] S2023, selecting a phase offset that produces the best signal indicator, and applying it to all metasurface units in the block to update the phase shift coefficients of the metasurface units in the block;

[0042] S2023, moving the dynamic block according to the set rules, and returning to step S2022 after each movement of the dynamic block until the phase shift coefficients of all the metasurface units of the metasurface array are updated, and the initial optimal phase shift coefficients of all the metasurface units of the metasurface array are obtained.

[0043] Furthermore, setting the initial states of all the metasurface units of the metasurface array specifically includes: setting the initial states of all the metasurface units to the same initial state.

[0044] Further, the range of the dynamic block is set, specifically including: setting the block size parameter b, column sum The cells within a row range are considered a block.

[0045] Furthermore, the initial position of the dynamic block is set, specifically including: setting the upper left vertex of the initial block to (0, N z -1).

[0046] Furthermore, the similarity between each individual and the current optimal fitness individual is calculated, specifically including: calculating the maximum Hamming distance between all individuals in the current g-th iteration population and the binary deterministic solution of the current optimal fitness individual Calculate the minimum Hamming distance between all individuals in the current g-th iteration population and the binary deterministic solution of the current optimal fitness individual Calculate the Hamming distance between the qth individual in the gth iteration and the binary deterministic solution of the current optimal fitness individual

[0047] according to as well as Calculate the similarity between the qth individual in the gth iteration and the current optimal fitness individual The calculation formula is:

[0048]

[0049] The rotation angle of each individual is calculated according to the fitness of each individual and the similarity between each individual and the current optimal fitness individual. The calculation formula is:

[0050]

[0051] Among them, Δω0 is the preset rotation angle step, a is the preset weight factor, It represents the fitness of the qth individual in the gth iteration, i.e., the signal index value. represents the signal index value of the best fitness individual in the g-th iteration, represents the rotation angle of the qth individual at the gth iteration.

[0052] Furthermore, after performing the rotation update process on the original population corresponding to the g-th iteration process according to the rotation angle of each individual in step S204, the following further comprises:

[0053] The mutation probability P is calculated based on the similarity between each individual and the individual with the best fitness and the overall similarity of the current population. muta , the formula is:

[0054]

[0055] Among them, P0 is the preset basic mutation probability, represents the similarity between the qth individual in the gth iteration and the current optimal fitness individual, represents the overall similarity of the population in the g-th iteration;

[0056] According to the mutation probability P muta A batch of individuals are randomly selected for mutation. For each selected individual, a mutation block is randomly selected, and the mutation operation is performed on each quantum bit corresponding to each hypersurface unit in the mutation block.

[0057] Furthermore, a mutation operation is performed on each quantum bit corresponding to each hypersurface unit in the mutation block, specifically including: performing a Hadamard gate mutation operation on each quantum bit corresponding to each hypersurface unit in the mutation block.

[0058] Furthermore, step S204 also includes the following steps: calculating the average Hamming distance between all individuals in the group and the current optimal fitness individual according to Calculate the overall similarity of the current population The formula is:

[0059]

[0060] in, The maximum Hamming distance between all individuals in the g-th iteration population and the binary deterministic solution of the current optimal fitness individual It is the minimum Hamming distance between all individuals in the g-th iteration population and the binary deterministic solution of the current optimal fitness individual.

[0061] In a second aspect, an embodiment of the present disclosure provides an electronic device, including:

[0062] at least one processor; and

[0063] a memory communicatively connected to the at least one processor; wherein,

[0064] The memory stores one or more computer programs that can be executed by the at least one processor, and the one or more computer programs are executed by the at least one processor to enable the at least one processor to perform the steps of the interference suppression method as described in the first aspect.

[0065] In the third aspect, an embodiment of the present disclosure also provides a communication system, including a controller and a metasurface array, wherein the metasurface array is connected to the controller, the controller communicates with a user device, the controller is used to receive signal indicator data fed back in real time by the user device, and the controller is used to obtain a phase shift coefficient for the metasurface array according to the method described in the first aspect, and apply the phase shift coefficient to the metasurface array. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1A schematic diagram of a multi-base station signal receiving system model assisted by a metasurface antenna;

[0067] Figure 2 A schematic diagram of a relay wireless communication system assisted by a metasurface antenna;

[0068] Figure 3 A flow chart of an interference suppression method for a metasurface array provided in an embodiment of the present disclosure;

[0069] Figure 4 This is a flow chart of an optional implementation method of step S2 in the embodiment of the present disclosure;

[0070] Figure 5 A schematic diagram of a moving process of a dynamic block in the dynamic block initialization strategy disclosed in the present invention;

[0071] Figure 6 This is a comparison diagram of average SINR results of various algorithms under different noise conditions in the present disclosure;

[0072] Figure 7 It is a simulation comparison result diagram of the maximum SINR of each generation of various algorithms in the present disclosure;

[0073] Figure 8 This is a simulation comparison result diagram of the average maximum SINR of each generation of various algorithms in 1000 tests in the present disclosure;

[0074] Fig. 9 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 3 The following is a flow chart of an interference suppression method for a metasurface array provided in an embodiment of the present disclosure. Figure 3 As shown, a method for suppressing interference of a metasurface array comprises the following steps:

[0080] Determine an interference suppression model of the metasurface array; the goal in the interference suppression model is to maximize the signal index of the target signal, and the decision variable in the interference suppression model is the phase shift coefficient of the N metasurface units of the metasurface array;

[0081] Solving the interference suppression model based on a quantum genetic algorithm to obtain a corresponding current optimal solution; in the process of solving the quantum genetic algorithm, the individuals in the population are individual codes composed of quantum bits corresponding to the N metasurface units, and the fitness of the individuals in the population is a signal index corresponding to the phase shift coefficient when the individual code of the individual is converted into the phase shift coefficient and applied to the metasurface array;

[0082] The current optimal solution is converted into phase shift coefficients for the metasurface array.

[0083] Dynamic Metasurface Antennas (DMA) are a special type of metasurface array. DMA has significant advantages in large-scale multiple-input multiple-output (MIMO) systems. The present invention focuses on the research of wireless communication systems assisted by dynamic metasurface antennas (DMA). By adjusting the phase shift characteristics of metasurface antenna units, spatial filtering of received electromagnetic waves is achieved, the co-frequency interference of signals from non-target base stations is suppressed, the signal-to-interference-to-noise ratio of target base station signals is improved, and the quality of wireless communication is guaranteed.

[0084] Based on the difficulty of implementing related technologies and the consideration of computational complexity, the present invention proposes a novel blind beamforming and interference suppression algorithm inspired by quantum genetic algorithm to solve this problem. This embodiment takes dynamic metasurface antennas (DMA) as an example to describe the steps of the interference suppression method in detail.

[0085] A method for suppressing interference in receiving signals from multiple base stations for a metasurface antenna comprises the following steps:

[0086] Step S1: determine the interference suppression model of the metasurface antenna.

[0087] The goal of the interference suppression model is to maximize the signal index of the target signal, and the decision variable in the interference suppression model is the phase shift coefficient of the N antenna units of the metasurface antenna. The phase shift coefficients of the N antenna units of the metasurface antenna are bit quantized. The present invention selects a suitable number of quantization bits according to specific needs, and uses a quantum genetic algorithm to find the phase shift value combination corresponding to the current optimal solution.

[0088] It should be noted that when the algorithm disclosed in the present invention is used for interference suppression, the signal indicator may be, but is not limited to, SINR (Signal to Interference plus Noise Ratio), i.e., the signal to interference plus noise ratio. The larger the value, the less interference the target signal suffers, and the better the signal quality.

[0089] Furthermore, the signal indicator is one or more of a signal-to-interference plus noise ratio SINR, a signal-to-interference plus noise ratio SIR, a carrier-to-interference plus noise ratio CINR, a communication data rate, and a symbol rate.

[0090] Preferably, the signal indicator of the present invention is SINR (Signal to Interference plus Noise Ratio), that is, signal to interference plus noise ratio.

[0091] The target signal may be a target base station signal received by the metasurface antenna. The signal index may be fed back by the user end.

[0092] The algorithm disclosed in the present invention can also be used simply for signal enhancement, in which case the signal indicator is RSRP (Reference Signal Receiving Power), i.e., reference signal received power, or RSSI (Received Signal Strength Indication), i.e., received signal strength indication. In addition, some additional information can also be included for reference, such as the PCI cell number and BeamIdx beam number of the signal.

[0093] Step S2: solving the interference suppression model based on an improved quantum genetic algorithm to obtain a corresponding current optimal solution.

[0094] In the process of solving the quantum genetic algorithm, the individuals in the population are individual codes composed of quantum bits corresponding to the N antenna units, and the fitness of the individuals in the population is the corresponding signal index obtained when the individual code of the individual is converted into a phase shift coefficient and applied to the metasurface antenna. The present invention uses quantum bits to represent the phase shift coefficient of each antenna unit.

[0095] Specifically, the individual of the present invention is a series of quantum bits of length, namely a quantum bit sequence, and one quantum bit is determined by a set of probability amplitudes; the length of an individual quantum bit = the number of antenna units * the number of quantized bits.

[0096] Specifically, the fitness of an individual in the population is a series of quantum bits of the individual that are collapsed into a certain binary code, and the binary code is then mapped into a phase shift coefficient. The phase shift coefficient corresponds to the signal SINR indicator when applied to the metasurface antenna.

[0097] Step S3: convert the current optimal solution into a phase shift coefficient for the metasurface antenna, that is, convert the optimal solution recorded after the iteration into a phase shift coefficient of the DMA and output it.

[0098] The present invention converts the current optimal solution into a phase shift coefficient and applies it to a metasurface antenna, so that the zero point of the directional diagram of the metasurface antenna is aligned with the interfering base station and the peak point is aligned with the target base station, thereby achieving interference suppression.

[0099] Quantum Genetic Algorithm (QGA) is a newly developed probabilistic evolutionary algorithm that is a combination of quantum computing and genetic algorithm. In QGA, individual genes are represented by quantum bits instead of fixed binary bits, which can superimpose multiple possibilities, achieve parallel search and faster convergence. The quantum bit state of the gene is represented by ψ>=γ|0>+δ|1>, where γ and δ represent the complex probability amplitudes of the corresponding states and satisfy γ 2 +|δ 2 =1. Moreover, |0> and |1> represent quantum states "0" and "1" respectively. The mth individual in the tth generation population can be encoded as:

[0100]

[0101] Where m = 1, ..., M, M represents the population size; i = 1, ..., L, where L represents the individual code length. The DMA of N units quantized with τ bits corresponds to an individual code of length L = τN.

[0102] Through the collapse operation, the quantum bit will be measured according to the probability amplitude and reach a certain quantum state of "0" or "1" as a potential solution to the problem. After the individual state is collapsed, the obtained solution is applied to the target problem to determine the fitness of each individual. The population is usually updated by changing the probability amplitude of the individual using a quantum rotating gate. The process can be expressed as:

[0103]

[0104] Among them (α i ,β i) and (α′ i ,β′ i ) represent the probability amplitude of the individual i-th quantum bit before and after the update. The rotation angle Δω i It is usually related to the fitness of individuals and plays a vital role in determining performance and convergence speed. After the entire population is updated using quantum gates, a new next-generation population is obtained, and the above collapse and population update operations are repeated until the algorithm converges or the exit conditions are met. The quantum genetic algorithm incorporates the principles of quantum computing into the genetic algorithm, significantly speeding up the convergence speed of the population, with good convergence and global search capabilities, and showing certain advantages in solving high-dimensional complex problems.

[0105] However, QGA is easily affected by local optimal solutions and has the problem of premature convergence. In order to solve this problem, the present invention designs a diversified adaptive quantum update strategy based on the characteristics of DMA array optimization problems, and further improves the performance and convergence speed.

[0106] In QGA, the probability amplitudes of individuals are usually initialized to the same value The individuals in the initial population are generated completely randomly, resulting in a low initial population value and slow convergence. In the method of the present invention, based on the cyclic offset characteristics of the DMA phase shift coefficient optimization and the adjacent quantization characteristics between adjacent elements, a dynamic block method is designed to effectively obtain a higher initial SINR value. Considering the LoS path of the target signal, the gain of the corresponding channel can be expressed as:

[0107]

[0108] The intermediate variables are:

[0109]

[0110]

[0111] Further expanded to:

[0112]

[0113]

[0114] where y i ∈{0,...,N y -1} and z i ∈{0,...,N z -1} represent the i-th element in the y-axis and z-axis directions respectively. Therefore, the condition for maximizing the channel gain is: for any two elements i and j, their phase shift θ i and θ j satisfy:

[0115]

[0116] It can be observed that due to the existence of the cosine function, the optimal phase shift of the DMA unit presents a periodic characteristic. In the y-axis and z-axis directions, the phase shift differences between adjacent units are and Utilizing this cyclic shift characteristic, the present invention can iteratively apply cyclic phase shift to DMA units one by one to improve channel gain. In addition, the phase shift coefficient of actual DMA usually needs to be quantized. The optimal phase coefficients of adjacent units are usually different in the continuous domain, but are often quantized to the same discrete value when being quantized nearby. Therefore, by utilizing this adjacent quantization characteristic, the present invention contemplates performing offset operations on units in an entire block at the same time to further improve algorithm efficiency.

[0117] Therefore, the present invention proposes a novel dynamic block quantum genetic algorithm (DBQGA), which is mainly used for new multi-base station interference suppression of metasurface antennas. This method does not require accurate channel state information, achieves better system performance without channel estimation, and the system is relatively simple to implement, with good application prospects.

[0118] like Figure 4 As shown, in some embodiments, solving the interference suppression model based on a quantum genetic algorithm to obtain the corresponding current optimal solution specifically includes:

[0119] Step S201, initializing algorithm parameters of the quantum genetic algorithm.

[0120] The algorithm parameters include at least one of the following parameters: setting the block size parameter b, the population size M, the maximum number of iterations g max , rotation angle step Δω0, weight factor a, basic mutation probability P0, etc.

[0121] Step S202: Generate an initial population including a plurality of individuals and use it as the original population corresponding to the first iteration process.

[0122] Step S203, determining whether the original population corresponding to the current iterative process meets the preset iteration termination condition;

[0123] 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 S205 is executed;

[0124] Step S204: perform collapse operation on all quantum individuals in the original population corresponding to the g-th iteration process to obtain a binary deterministic solution (i.e., a determined binary solution), convert the binary deterministic solution into a phase shift coefficient and apply it to the metasurface array respectively, and record the corresponding signal indicators (the controller receives the signal indicators such as signal interference noise ratio information fed back by the user equipment and saves it as the individual fitness), as the fitness, record the current optimal fitness individual, and calculate the similarity between all individuals and the current optimal fitness individual, calculate the rotation angle of each individual according to the fitness of each individual and the similarity between each individual and the current optimal fitness individual, and perform rotation update processing on the original population corresponding to the g-th iteration process according to the rotation angle of each individual (i.e., perform rotation update operation using a quantum rotation gate), and obtain the final updated population corresponding to the g-th iteration process, g∈[1,g max ] and is an integer, g max is the preset maximum number of iterations;

[0125] Step S205: taking the final updated population corresponding to the g-th iteration as the original population corresponding to the g+1-th iteration, and adding 1 to the current iteration number g for updating;

[0126] After step S205, execute step S203;

[0127] Step S206: output the individual with the best fitness in the original population of the g-th iteration to obtain the current optimal solution.

[0128] In step S202, an initial population including multiple individuals is generated, specifically including: determining the coding length according to the number of DMA units, randomly initializing the individuals of the initial population, obtaining the initial optimal phase shift coefficients of all antenna units of the metasurface antenna using the dynamic block initialization strategy, converting the initial optimal phase shift coefficients of all antenna units of the metasurface antenna obtained using the dynamic block initialization strategy into individual codes, adding them to the initial population, and randomly initializing other individuals of the population.

[0129] Assuming the number of antenna units is N and the number of quantization bits is τ, the individual coding length is τ*N.

[0130] Furthermore, the dynamic block initialization strategy is used to obtain the initial optimal phase shift coefficients of all antenna units of the metasurface antenna, specifically including:

[0131] S2021. Set the initial state of all antenna units of the metasurface antenna and the range and initial position of the dynamic block.

[0132] Setting the initial states of all antenna units of the metasurface antenna includes: setting all DMA units to the same initial state.

[0133] Set the range of the dynamic block, including: setting the block size parameter b, column sum The cells within a row range are considered a block.

[0134] Set the initial position of the dynamic block, including: setting the upper left vertex of the initial block to (0, N z -1).

[0135] S2022, shift the cyclic phase by Δθ i ∈Φ τ Sequentially applied to the current phase shift coefficients of all antenna elements in the block, where i = 0,...,2 τ -1, record the corresponding signal indicator I i The phase shift is applied globally to all cells in the block, and the relative differences in their phase shift coefficients are preserved. For example, the phase coefficient can be obtained from Updated to

[0136] S2023. Select the best signal indicator I opti The phase shift Δθ opti (ie, retain an optimal overall phase shift of the block) and apply it to all antenna units in the block to update the phase shift coefficients of the antenna units in the block.

[0137] S2023, moving the dynamic block according to the set rules, and returning to step S2022 after each movement of the dynamic block until the phase shift coefficients of all antenna units of the metasurface antenna are updated, and obtaining the initial optimal phase shift coefficients of all antenna units of the metasurface antenna.

[0138] The dynamic block is moved according to the set rules, specifically including: first, the dynamic block is moved from the initial position, i.e., the leftmost position of the first row, to the right, one column at a time, and after each row is updated, the dynamic block is moved downward, one row at a time, until the phase shift coefficients of all antenna units of the metasurface antenna are updated. After each downward movement of one row, it returns to the leftmost position and then moves to the right. Of course, the dynamic block can also be moved according to other rules.

[0139] Step S2023 includes: moving the dynamic block, traversing the block phase shift and updating the optimal phase shift of the unit until the entire DMA array is updated. The optimal phase shift coefficient obtained by searching the dynamic block strategy is retained.

[0140] Specifically, step S2023 includes: moving the entire dynamic block to the right by one column, and repeating the operation of finding and applying the best overall phase offset. After updating a row, the dynamic block is moved downward, and the operation continues from the leftmost position until the entire DMA array is updated. The moving process of the dynamic block is as follows: Figure 5 shown.

[0141] Compared with the strategy of iterating the phase shift coefficients of each unit one by one, this initialization strategy may produce relatively better solutions with lower computational complexity. And significantly accelerate the convergence of the algorithm.

[0142] 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:

[0143] 1) The current number of iterations g is greater than the preset maximum number of iterations g max .

[0144] 2) The individuals with the best fitness in the original population processed by the current iteration tend to converge.

[0145] The present invention compares each iteration whether the individual with the best fitness tends to be long-term stable and unchanged, and if so, it determines convergence.

[0146] In the iterative update of the population, the rotation angle in QGA is determined based on a lookup table with fixed values, which makes the search process relatively inefficient and time-consuming. A diversified adaptive update strategy is introduced in the algorithm of the present invention to overcome this limitation. The strategy adjusts the evolution speed of individuals by considering their similarities and relative fitness.

[0147] The present invention uses the Hamming distance to evaluate the similarity between the definite solutions of two individuals. The present invention calculates the Hamming distance of the definite solutions of all individuals and the best individual to calculate the similarity. After the definite solutions obtained by collapse of all individuals are applied to DMA and the corresponding signal index SINR is obtained, the Hamming distance between the binary definite solutions of the individuals is calculated. A smaller Hamming distance indicates a higher degree of similarity.

[0148] Furthermore, the similarity between each individual and the current optimal fitness individual is calculated, specifically including: calculating the maximum Hamming distance between all individuals in the current g-th iteration population and the binary deterministic solution of the current optimal fitness individual Calculate the minimum Hamming distance between all individuals in the current g-th iteration population and the binary deterministic solution of the current optimal fitness individual Calculate the Hamming distance between the binary deterministic solution of the qth individual in the gth iteration and the current optimal fitness individual (such as the individual with the highest SINR) according to as well as Calculate the similarity between the qth individual in the gth iteration and the current optimal fitness individual The calculation formula is:

[0149]

[0150] In order to better adjust the rotation angle of the quantum rotating gate, the similarity and relative fitness obtained above are used to adjust the evolution speed of the individual. Individuals with less similarity to the optimal individual are assigned a larger rotation angle to accelerate their evolution speed. At the same time, individuals with greater similarity are assigned a smaller rotation angle for a more refined search. In addition, the rotation angle of the individual is affected by its relative fitness. When the fitness of the individual is significantly lower than that of the optimal individual, the rotation angle is increased to speed up convergence.

[0151] The rotation angle of the qth individual in the gth iteration The calculation formula is:

[0152]

[0153] Among them, Δω0 is the preset rotation angle step, a is the preset weight factor, and the weight factor a∈(0,1) determines the influence of the relative signal index value of the individual and its similarity with the optimal individual on the individual evolution. It represents the fitness of the qth individual in the gth iteration, i.e., the signal index value. Represents the signal index value of the optimal fitness individual in the g-th iteration.

[0154] set up It represents the average value of the Hamming distance between all individuals in the group and the current optimal fitness individual, and the similarity of the entire population can be calculated similarly. The similarity between the individual and the entire group will affect subsequent operations.

[0155] Step S204 also includes the following steps: calculating the average Hamming distance between all individuals in the group and the current optimal fitness individual according to Calculate the overall similarity of the current population The formula is:

[0156]

[0157] in, The maximum Hamming distance between all individuals in the g-th iteration population and the binary deterministic solution of the current optimal fitness individual It is the minimum Hamming distance between all individuals in the g-th iteration population and the binary deterministic solution of the current optimal fitness individual.

[0158] In order to solve the local convergence problem in QGA, the present invention also designs a block mutation operation. The mutation operation usually involves randomly changing a single bit of the encoding of an individual to maintain population diversity. For DMA, simply changing the phase shift coefficient of one or two units usually does not significantly change the overall beamforming performance. The present invention will perform a large-scale mutation operation within a randomly selected block.

[0159] In some embodiments, after performing the rotation update process on the original population corresponding to the g-th iteration process according to the rotation angle of each individual in step S203, the following further steps are included:

[0160] The mutation probability P is calculated based on the similarity between each individual and the individual with the best fitness and the overall similarity of the current population. muta , which is determined by the current similarity between the group and the individual, has good practical performance after simulation verification and is expressed as:

[0161]

[0162] Among them, P0 is the preset basic mutation probability, represents the similarity between the qth individual in the gth iteration and the current optimal fitness individual, represents the overall similarity of the population in the g-th iteration;

[0163] According to the mutation probability P muta A batch of individuals are randomly selected for mutation. For each selected individual, a mutation block is randomly selected to perform a mapping from antenna units to individual qubits, and a mutation operation is performed on each qubit corresponding to each antenna unit in the mutation block. The units within the mutation block range are selected as target mutation units. The range and position of the mutation block are randomly selected.

[0164] According to the mutation probability P muta A batch of individuals are randomly selected for mutation, which includes: for each individual in the population, a random number is generated, and if this random number is less than or equal to the mutation probability P muta , then the individual is selected for mutation.

[0165] Since the mutation probability is mainly related to the similarity, the mutation probability is adaptively adjusted as the iteration progresses.

[0166] Further, a mutation operation is performed on each qubit corresponding to each antenna unit in the mutation block, specifically including: performing a mapping from the DMA unit to the individual qubit, and applying a Hadamard gate mutation to each qubit corresponding to each unit in the mutation block. This mutation rotates the phase angle of each qubit corresponding to each antenna unit in the mutation block counterclockwise. Degrees can be expressed as follows:

[0167]

[0168] in, is the probability amplitude of the ith position of the qth individual in the gth generation.

[0169] The phase coefficient determination method for DMA multi-base station reception interference suppression, which is mainly based on quantum genetic algorithm, does not require channel estimation to obtain channel information and can be well applied to DMA relay communication systems. Compared with the traditional interference suppression algorithm that relies on channel information, this solution does not require channel estimation to obtain channel information, does not require channel estimation related hardware system and estimation overhead, and has better practicality. Compared with the search algorithm of discrete state exhaustive traversal, the solution of the present invention can reduce the time complexity from Reduced to (where M and T iter In addition, compared with the conventional solution based on mathematical non-convex optimization (usually As a blind optimization algorithm based on quantum genetic algorithm, this scheme can reduce computational complexity and alleviate the computational pressure of the hardware system while having higher versatility.

[0170] Figure 2 A schematic diagram of a super-surface antenna-assisted relay wireless communication system provided in an embodiment of the present disclosure. Figure 2 As shown, the communication system includes a controller and a metasurface antenna as a signal receiving antenna, the metasurface antenna is connected to the controller, the controller communicates with the user equipment, and the controller is used to receive signal indicator data fed back in real time by the user equipment. The user equipment and the metasurface antenna can communicate wirelessly using Bluetooth, WIFI, etc.

[0171] The working scenario of the relay communication system includes multiple signal source base stations and user devices (such as mobile phones) served by the relay communication system. The unit phase of the metasurface antenna is quantized to τ bits, the design parameters such as array scale and unit spacing are known, and the electromagnetic characteristics are known. The user device and the metasurface antenna controller can communicate in some way (Bluetooth, Wi-Fi, etc.), and the user device can feed back real-time signal indicator data to the metasurface antenna controller. Fix the metasurface antenna relay communication system in a multi-base station interference environment and configure various system settings. The controller is used to obtain the phase shift coefficient for the metasurface antenna according to the above-mentioned interference suppression method. The metasurface antenna controller applies the phase shift coefficient, i.e., the algorithm iteration result, to the metasurface antenna and maintains the state to complete multi-base station interference suppression based on the metasurface antenna.

[0172] In such Figure 2 In the relay wireless communication system assisted by the metasurface antenna shown, it is necessary to determine the phase shift coefficient of each antenna unit so that the zero point of the antenna array's directional diagram is aligned with the interfering base station and the peak point is aligned with the target base station to improve the signal-to-interference-noise ratio and achieve interference suppression. However, in the relay wireless communication system, it is difficult to obtain channel information, and only some signal index data can be obtained from the receiving end user. In order to optimize the discrete phase shift coefficient of DMA when only the signal index data of the received signal is available, the present invention proposes a new method for determining the phase shift coefficient when using a metasurface antenna for multi-base station interference suppression. The solution has lower system hardware requirements, higher practicality, lower computational complexity and better system performance.

[0173] In order to verify the effect of the technical solution of the present invention, the technical solution of the present invention was simulated. The simulation parameters are as follows:

[0174] Metasurface antenna parameters: total number of elements N = 16, number of rows N z =4,Number of columns y =4. The antenna array has uniformly distributed rows and columns, and the row and column spacings of the elements are both 0.5λ. The antenna element phase shift is quantized by 2 bits.

[0175] Spatial position: In a spatial coordinate system with a unit length of 1m, the metasurface antenna array is placed at (0, 0, 0) with the lower left corner unit of the array as the reference point. The antenna array is located on the yoz plane, and the K signal source base stations are randomly distributed in the hemispherical space in the positive direction of the x-axis in front of the metasurface antenna. The target signal source base station is 100-300m away from the metasurface antenna, while the interference signal base station is 300-900m away from the metasurface antenna.

[0176] Signal frequency: f=2.6GHz.

[0177] Channel model: Far field model, it is a plane wave when reaching the array surface.

[0178] Number of signal source base stations: K=5.

[0179] Algorithm-related parameters in the simulation: population size M is 100, maximum number of iterations g max is 50, the block size parameter b is 2, the rotation angle step Δω0 of quantum update is 0.01, the weight factor a of quantum update is 0.6, and the basic mutation probability P0 is 0.01.

[0180] During the simulation, in order to more comprehensively evaluate the interference suppression performance of the algorithm, the spatial positions of the target signal source base station and the four interfering base stations are randomly generated within the corresponding distance range. The base station positions in each simulation are independently and randomly generated, and the algorithm is executed under the corresponding base station conditions and the optimization results are recorded. The values ​​when the phase shift coefficients of the DMA under the corresponding environmental conditions are all set to 0 are used as a benchmark to evaluate the SINR improvement effect of the algorithm. In addition, in order to verify the performance of the algorithm proposed in the present invention, in addition to the simulation test of the DBQGA of the present invention, the performance of the following comparison schemes is also evaluated:

[0181] Random-Max Algorithm (RMA): randomly generates phase shift coefficients, applies the phase shift coefficients to DMA, and selects the phase shift coefficient that produces the best performance as the final result. In the performance comparison simulation, for the convenience of comparison, the number of random times of RMA is set to 5000, which is equivalent to the number of feedback acquisitions in the algorithm method of the present invention during simulation.

[0182] Interference suppression based on the minimum mean square error (MMSE) criterion: The minimum mean square error criterion is used for receiver-side filtering. However, the calculated continuous weight matrix needs to be quantized when used as a phase shift coefficient on the quantized DMA, which will lead to huge performance loss. In addition, the acquisition of channel information is very difficult in the engineering practice of DMA relay communication systems, making MMSE difficult to use directly in actual systems.

[0183] Greedy Fast Beamforming Algorithm (GFBA): Change the phase shift coefficient row by row or column by column, select and maintain the phase shift coefficient that produces the best performance for each row or column during the iteration process, thereby gradually improving the performance index. Scan row by row first, then scan column by column, and obtain the final optimization result after completing the scanning of the entire array.

[0184] Since the noise level in the environment will affect the SINR improvement effect of the algorithm, the present invention evaluates and records the algorithm performance under different noise levels in the simulation. The simulation was performed 500 times under the same noise condition, and the base station position was randomly generated each time. The result index values ​​of 500 times were averaged and plotted on Figure 6 The horizontal axis of the figure represents the signal-to-noise ratio (SNR) when all the phase shift coefficients of the DMA are set to 0 under various noise conditions, and the vertical axis represents the final average SINR value obtained by the four algorithms under the corresponding conditions, as well as the SINR value when all phase shifts are 0 as a comparison benchmark.

[0185] It can be observed that the DBQGA proposed in the present invention shows a significantly greater SINR improvement compared to other algorithms. Specifically, under lower noise conditions, the performance of the algorithm of the present invention is improved by 4dB compared to RMA; and compared to GFBA, the average SINR improvement is more significant, reaching about 10dB. In addition, under lower noise conditions, the performance of MMSE and GFBA is similar. Under higher noise conditions, the main factor affecting the SINR value is noise rather than interference from non-target base stations, but the average SINR improvement of the algorithm of the present invention under these conditions still has certain advantages over other algorithms.

[0186] The present invention is mainly based on the quantum genetic algorithm, which belongs to the evolutionary intelligent algorithm. Therefore, the present invention also simulates and compares it with the classical genetic algorithm (GA) and the quantum genetic algorithm (QGA), which are also evolutionary algorithms, to more comprehensively analyze its convergence speed, optimization performance and algorithm efficiency.

[0187] For the convenience of comparison, the parameter settings of the two comparison algorithms are basically consistent with those of the present invention: for the classical genetic algorithm, the population size is 100, the maximum number of iterations is 100, the crossover probability is 0.6, and the mutation probability is 0.01. For the classical quantum genetic algorithm, the population size is 100, the maximum number of iterations is 100, and the fixed rotation angle of the quantum rotating gate is 0.01π. Here, due to the slow convergence of the classical genetic algorithm, the upper limit of the number of iterations of the three algorithms is set to 100. During the simulation, a group of base station locations are randomly generated, and the three algorithms are subjected to SINR optimization simulation under this condition, and the SINR performance of the optimal individual of each generation of the population is recorded, such as Figure 7 shown.

[0188] Figure 7The horizontal axis represents the number of iterations of the algorithm, and the vertical axis represents the corresponding optimal SINR performance during the algorithm iteration process. It can be observed that since the DBQGA algorithm of the present invention uses a dynamic block initialization strategy, it has a better SINR performance at the beginning. In addition, the overall SINR improvement performance of DBQGA is better than the other two schemes.

[0189] Since evolutionary algorithms have a certain degree of randomness, even if the optimization problem is solved under exactly the same conditions, there are certain differences in performance each time. Therefore, a single optimization simulation is difficult to fully and accurately reflect the differences between the algorithms. In order to better compare the three algorithms, the present invention repeated the simulation 1000 times under the same conditions, recorded the SINR performance of the best individual in each generation of the population in each simulation, calculated the average of 1000 times, and plotted the results on Figure 8 middle.

[0190] like Figure 8 As shown in the figure, the dynamic block initialization strategy designed by the present invention brings a large improvement in the average initial value. In addition, the DBQGA designed by the present invention tends to converge in 40 to 50 generations on average, and the convergence speed is about 50% faster than the classical genetic algorithm. It is worth noting that the classical quantum genetic algorithm shows an obvious "precocious" phenomenon when solving this problem, with faster convergence but lower convergence value. The algorithm of the present invention successfully overcomes this problem and improves the final average optimization result by about 1dB.

[0191] Based on the same inventive concept, an embodiment of the present disclosure also provides an electronic device. Fig. 9 FIG. 1 is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. Fig. 9 As shown, an electronic device includes: 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 interference suppression 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.

[0192] 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.

[0193] 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.

[0194] In some embodiments, the one or more processors 101 include a field programmable gate array.

[0195] Based on the same inventive concept, the embodiment of the present disclosure further provides a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the steps in any interference suppression method in the above embodiments are implemented.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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 method for suppressing interference of a metasurface array, characterized in that: The steps include: Determine an interference suppression model of the metasurface array; the goal in the interference suppression model is to maximize the signal index of the target signal, and the decision variable in the interference suppression model is the phase shift coefficient of the N metasurface units of the metasurface array; Solving the interference suppression model based on a quantum genetic algorithm to obtain a corresponding current optimal solution; in the process of solving the quantum genetic algorithm, the individuals in the population are individual codes composed of quantum bits corresponding to the N metasurface units, and the fitness of the individuals in the population is a signal index corresponding to the phase shift coefficient when the individual code of the individual is converted into the phase shift coefficient and applied to the metasurface array; In the process of solving the quantum genetic algorithm, an initial population including multiple individuals is generated, specifically including: using a dynamic block initialization strategy to obtain the initial optimal phase shift coefficients of all the supersurface units of the supersurface array, converting the initial optimal phase shift coefficients of all the supersurface units of the supersurface array obtained by the dynamic block initialization strategy into individual codes, adding them to the initial population, and randomly initializing other individuals in the population; The dynamic block initialization strategy is used to obtain the initial optimal phase shift coefficients of all metasurface units of the metasurface array, including: S2021, setting the initial state of all metasurface units of the metasurface array and the range and initial position of the dynamic block; S2022, applying the cyclic phase offset to the current phase shift coefficients of all metasurface units in the block in sequence, and recording corresponding signal indicators; S2023, selecting a phase offset that produces the best signal indicator, and applying it to all metasurface units in the block to update the phase shift coefficients of the metasurface units in the block; S2023, moving the dynamic block according to the set rules, and returning to step S2022 after each movement of the dynamic block, until the phase shift coefficients of all the metasurface units of the metasurface array are updated, and the initial optimal phase shift coefficients of all the metasurface units of the metasurface array are obtained; The current optimal solution is converted into phase shift coefficients for the metasurface array.

2. The interference suppression method of the metasurface array according to claim 1, characterized in that: The signal indicator is one or more of a signal-to-interference plus noise ratio SINR, a signal-to-interference plus noise ratio SIR, a carrier-to-interference plus noise ratio CINR, a communication data rate, and a symbol rate.

3. The interference suppression method of the metasurface array according to claim 1, characterized in that: Solving the interference suppression model based on the quantum genetic algorithm to obtain the corresponding current optimal solution specifically includes: Step S201, initializing algorithm parameters of the quantum genetic algorithm; Step S202, generating an initial population including a plurality of individuals, and using 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 S205 is executed; Step S204: Perform collapse operation on all quantum individuals in the original population corresponding to the g-th iteration process to obtain a binary deterministic solution, convert the binary deterministic solution into a phase shift coefficient and apply it to the metasurface array respectively, and record the corresponding signal index as fitness, record the current optimal fitness individual, and calculate the similarity between all individuals and the current optimal fitness individual, calculate the rotation angle of each individual according to the fitness of each individual and the similarity between each individual and the current optimal fitness individual, and perform rotation update processing on the original population corresponding to the g-th iteration process according to the rotation angle of each individual, and obtain the final updated population corresponding to the g-th iteration process, g∈[1,g max ] and is an integer, g max is the preset maximum number of iterations; Step S205: taking the final updated population corresponding to the g-th iteration as the original population corresponding to the g+1-th iteration, and adding 1 to the current iteration number g for updating; After step S205 is completed, step S203 is executed; Step S206: output the individual with the best fitness in the original population of the g-th iteration to obtain the current optimal solution.

4. The interference suppression method of the metasurface array according to claim 3, characterized in that: Calculate the similarity between each individual and the current optimal fitness individual, specifically including: Calculate the maximum Hamming distance between all individuals in the current g-th iteration population and the binary deterministic solution of the current optimal fitness individual Calculate the minimum Hamming distance between all individuals in the current g-th iteration population and the binary deterministic solution of the current optimal fitness individual Calculate the Hamming distance between the qth individual in the gth iteration and the binary deterministic solution of the current optimal fitness individual according to as well as Calculate the similarity between the qth individual in the gth iteration and the current optimal fitness individual The calculation formula is: The rotation angle of each individual is calculated according to the fitness of each individual and the similarity between each individual and the current optimal fitness individual. The calculation formula is: Among them, Δω0 is the preset rotation angle step, a is the preset weight factor, It represents the fitness of the qth individual in the gth iteration, i.e., the signal index value. represents the signal index value of the best fitness individual in the g-th iteration, represents the rotation angle of the qth individual at the gth iteration.

5. The interference suppression method of the metasurface array according to claim 3 or 4, characterized in that: After performing the rotation update process on the original population corresponding to the g-th iteration process according to the rotation angle of each individual in step S204, the following further comprises: The mutation probability P is calculated based on the similarity between each individual and the individual with the best fitness and the overall similarity of the current population. muta , the formula is: Among them, P0 is the preset basic mutation probability, represents the similarity between the qth individual in the gth iteration and the current optimal fitness individual, represents the overall similarity of the population in the g-th iteration; According to the mutation probability P muta A batch of individuals are randomly selected for mutation. For each selected individual, a mutation block is randomly selected, and the mutation operation is performed on each quantum bit corresponding to each hypersurface unit in the mutation block.

6. The interference suppression method of the metasurface array according to claim 5, characterized in that: Performing a mutation operation on each quantum bit corresponding to each hypersurface unit in the mutation block, specifically including: performing a Hadamard gate mutation operation on each quantum bit corresponding to each hypersurface unit in the mutation block.

7. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs executable by the at least one processor, and the one or more computer programs are executed by the at least one processor to enable the at least one processor to perform the interference suppression method according to any one of claims 1 to 6.

8. A communication system, characterized in that: It includes a controller and a metasurface array, the metasurface array is connected to the controller, the controller communicates with a user device, the controller is used to receive signal indicator data fed back by the user device, the controller is used to obtain a phase shift coefficient for the metasurface array according to the method described in any one of claims 1 to 6, and apply the phase shift coefficient to the metasurface array.

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