Intelligent metasurface unit phase shift optimization method, device, equipment, medium and product

Abstract

The invention discloses an intelligent metasurface unit phase shift optimization method and device, equipment, a medium and a product, and relates to the field of phase shift optimization, and the method comprises the steps: obtaining a first visible region and a second visible region based on the position information of a base station, the position information of an intelligent metasurface and the position information of each user; controlling the base station to randomly generate a plurality of groups of initial reflection coefficient vectors, wherein each group of initial reflection coefficient vectors comprises a first reflection coefficient vector and a second reflection coefficient vector; calculating the total traversal spectrum efficiency upper bound of each group of initial reflection coefficient vectors based on the cascade channel; optimizing the first reflection coefficient vector based on the total traversal spectrum efficiency upper bound; based on the optimized first reflection coefficient vector and the reset second reflection coefficient vector, obtaining the total traversal spectrum efficiency of each group of initial reflection coefficient vectors; and comparing the multiple groups of total traversal spectrum efficiencies, and selecting the maximum total traversal spectrum efficiency. The phase shift calculation efficiency of the intelligent metasurface unit can be improved.

Description

Technical Field

[0001] The present application relates to the field of phase shift optimization, and in particular to a method, device, equipment, medium and product for optimizing phase shift of an intelligent metasurface unit. Background Art

[0002] A complete wireless communication system usually includes a transmitter, a wireless channel, and a receiver. In the actual wireless communication process, due to factors such as the non-ideal characteristics of the channel, the transmitted signal will suffer severe fading or distortion. At this time, a wireless relay system needs to be introduced to improve the quality of signal transmission. The function of a traditional wireless relay system is to receive the signal from the transmitter and then amplify, regenerate or forward it to the receiver to extend the signal transmission distance or improve the signal quality. However, traditional repeaters usually contain a large number of active components and RF links, which increases the deployment cost.

[0003] In recent years, an intelligent metasurface composed of programmable two-dimensional electromagnetic metamaterials has been proposed. The intelligent metasurface is introduced into the wireless communication system as a new type of wireless relay, so that the spatial electromagnetic waves can be actively controlled in a programmable manner to form an electromagnetic field with controllable amplitude, phase shift, polarization and frequency.

[0004] However, as the optimization problems studied in wireless communication transmission systems assisted by smart metasurfaces become more and more complex, the number of smart metasurfaces will increase, and the increase in the number of each smart metasurface unit will lead to a sharp increase in the dimension of the channel matrix, resulting in a significant increase in the complexity of the phase shift optimization process of the smart metasurface unit and a decrease in efficiency. Summary of the invention

[0005] The purpose of this application is to provide a method, device, equipment, medium and product for optimizing the phase shift of an intelligent metasurface unit, which can improve the efficiency of optimizing the phase shift of an intelligent metasurface unit.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for optimizing phase shift of a smart metasurface unit, comprising:

[0008] Based on the base station location information, the smart metasurface location information and the location information of each user, obtaining a first visible area of ​​the base station on the smart metasurface and a second visible area of ​​each user on the smart metasurface;

[0009] Classify and mark all units in the first visible area and the second visible area to obtain a plurality of marked units, wherein the marked units include a plurality of multi-user common units and a plurality of user-specific units, wherein the multi-user common units are located in a common visible area of ​​a plurality of the users, and the user-specific units are located in a visible area of ​​only one user;

[0010] Controlling the base station to randomly generate multiple groups of initial reflection coefficient vectors, each group of the initial reflection coefficient vectors includes a first reflection coefficient vector and a second reflection coefficient vector;

[0011] Based on the cascaded channels, calculating the upper bound of the total ergodic spectrum efficiency of each group of the initial reflection coefficient vectors;

[0012] For each group of the initial reflection coefficient vectors, optimizing the first reflection coefficient vectors based on the total ergodic spectrum efficiency upper bound, and resetting the second reflection coefficient vectors according to the optimization result of the first reflection coefficient vectors;

[0013] Based on the optimized first reflection coefficient vector and the reset second reflection coefficient vector, obtaining a total ergodic spectrum efficiency of each group of the initial reflection coefficient vectors;

[0014] Compare multiple groups of the total ergodic spectrum efficiencies, select the maximum total ergodic spectrum efficiency, and the intelligent metasurface reflection coefficient vector corresponding to the maximum total ergodic spectrum efficiency is the optimization result.

[0015] In a second aspect, the present application provides a smart metasurface unit phase shift optimization device, comprising:

[0016] An acquisition module, configured to acquire a first visible area of ​​the base station on the smart super surface and a second visible area of ​​each user on the smart super surface based on the base station location information, the smart super surface location information and each user location information;

[0017] a marking module, configured to classify and mark all units in the first visible area and the second visible area to obtain a plurality of marked units, wherein the marked units include a plurality of multi-user common units and a plurality of user-specific units, wherein the multi-user common units are located in a common visible area of ​​a plurality of the users, and the user-specific units are located in a visible area of ​​only one user;

[0018] A control module, used for controlling the base station to randomly generate a plurality of groups of initial reflection coefficient vectors, each group of the initial reflection coefficient vectors comprising a first reflection coefficient vector and a second reflection coefficient vector;

[0019] A calculation module, used for calculating the upper bound of the total ergodic spectrum efficiency of each group of the initial reflection coefficient vectors based on the cascaded channels;

[0020] an optimization module, configured to optimize the first reflection coefficient vector for each group of the initial reflection coefficient vectors based on the total ergodic spectrum efficiency upper bound, and reset the second reflection coefficient vector according to the optimization result of the first reflection coefficient vector;

[0021] An obtaining module, configured to obtain a total ergodic spectrum efficiency of each group of the initial reflection coefficient vectors based on the optimized first reflection coefficient vector and the reset second reflection coefficient vector;

[0022] A selection module is used to compare multiple groups of the total ergodic spectrum efficiencies and select the maximum total ergodic spectrum efficiency, and the intelligent metasurface reflection coefficient vector corresponding to the maximum total ergodic spectrum efficiency is the optimization result.

[0023] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described smart metasurface unit phase shift optimization methods.

[0024] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described intelligent metasurface unit phase shift optimization methods.

[0025] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned smart metasurface unit phase shift optimization methods.

[0026] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0027] The present application provides a method, device, equipment, medium and product for optimizing the phase shift of a smart metasurface unit. The method obtains the first visible area of ​​the base station in the smart metasurface and the second visible area of ​​each user in the smart metasurface based on the base station location information, the smart metasurface location information and the location information of each user. By classifying and marking all units in the first visible area and the second visible area, a plurality of marked units are obtained, and the marked units include a plurality of multi-user common units and a plurality of user-specific units, wherein the multi-user common units are located in the common visible area of ​​multiple users, and the user-specific units are located in the visible area of ​​only one user; the base station is controlled to randomly generate a plurality of groups of initial reflection coefficient vectors, and each group of the initial reflection coefficient vectors includes a first reflection coefficient vector and a second reflection coefficient vector. Based on the cascade channel, the total ergodic spectral efficiency upper bound of each group of the initial reflection coefficient vectors is calculated; for each group of the initial reflection coefficient vectors, the first reflection coefficient vector is optimized based on the total ergodic spectral efficiency upper bound, and the second reflection coefficient vector is reset according to the optimization result of the first reflection coefficient vector. Based on the optimized first reflection coefficient vector and the reset second reflection coefficient vector, the total traversal spectrum efficiency of each group of the initial reflection coefficient vectors is obtained. Multiple groups of total traversal spectrum efficiencies are compared, and the maximum total traversal spectrum efficiency is selected. The intelligent metasurface reflection coefficient vector corresponding to the maximum total traversal spectrum efficiency is the optimization result. The present application maximizes the total traversal spectrum efficiency of the wireless communication system by optimizing the first reflection coefficient vector of the first metasurface and reset the second reflection coefficient vector, thereby reducing the calculation complexity of the phase shift of the intelligent metasurface unit and improving the calculation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 This is an application environment diagram of a smart metasurface unit phase shift optimization method in one embodiment of the present application;

[0030] Figure 2 A schematic diagram of a process flow of a phase shift optimization method for a smart metasurface unit provided in one embodiment of the present application;

[0031] Figure 3 A comparison diagram of the average number of convergence iterations of a uniform cylindrical array and a uniform planar array at different convergence thresholds provided in an embodiment of the present application;

[0032] Figure 4A schematic diagram of functional modules of a smart metasurface unit phase shift optimization device provided in one embodiment of the present application;

[0033] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0035] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0036] The smart metasurface unit phase shift optimization method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown in the figure, the base station is the transmitting end of the wireless communication system, responsible for sending signals to users. The smart metasurface consists of multiple programmable reflection units that can adjust the phase and amplitude of the signal to optimize signal transmission. The smart metasurface is located between the base station and the user for signal reflection and enhancement. Figure 1 In the figure, only the user end including user 1 and user 2 is taken as an example. User 1 and user 2 are receiving devices, which are used to receive signals from the base station through the smart metasurface. The part with common units for users 1 and 2 means that these reflection units serve both users 1 and 2 at the same time. The unique units of user 1 mean that these reflection units only serve user 1, and the unique units of user 2 mean that these reflection units only serve user 2. The signal sent by the base station first reaches the smart metasurface. The smart metasurface optimizes the common units and unique units of multiple users according to different reflection units, namely marking units, to improve signal quality and spectrum efficiency. By optimizing the phase shift of the reflection unit, the spectrum utilization can be maximized and the total traversal spectrum efficiency of the system can be improved.

[0037] A data storage system and a server are provided in the base station, and the data storage system can store data that the server needs to process. The data storage system can be provided separately, integrated on the server, or placed on the cloud or other servers. After receiving the above-mentioned first visible area and second visible area to be processed, the server classifies and marks all units of the first visible area and the second visible area to obtain multiple marked units, and the marked units include multiple multi-user common units and multiple user-specific units; the server controls the base station to randomly generate multiple groups of initial reflection coefficient vectors, and each group of initial reflection coefficient vectors includes a first reflection coefficient vector and a second reflection coefficient vector. Based on the cascade channel, the upper bound of the total ergodic spectrum efficiency of each group of initial reflection coefficient vectors is calculated. For each group of the initial reflection coefficient vectors, the first reflection coefficient vector is optimized based on the upper bound of the total ergodic spectrum efficiency. According to the optimization result of the first reflection coefficient vector, the second reflection coefficient vector is reset. Based on the optimized first reflection coefficient vector and the reset second reflection coefficient vector, the total ergodic spectrum efficiency of each group of initial reflection coefficient vectors is obtained. The total ergodic spectrum efficiency of multiple groups of the total ergodic spectrum efficiency is compared, and the maximum total ergodic spectrum efficiency is selected. The intelligent metasurface reflection coefficient vector corresponding to the maximum total ergodic spectrum efficiency is the optimization result. In addition, in some embodiments, the phase shift optimization method of the intelligent metasurface unit can also be implemented by a server alone, such as obtaining the first visible area and the second visible area to be processed from a data storage system by the server, and processing the first visible area and the second visible area to be processed. The server can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0038] In an exemplary embodiment, Figure 2 As shown, a method for optimizing phase shift of an intelligent metasurface unit is provided. The method is executed by a computer device located at a base station. Specifically, the method can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 Taking a server in a base station as an example, the method includes the following steps S201 to S207:

[0039] In step S201, based on the base station location information, the smart metasurface location information and the location information of each user, a first visible area of ​​the base station on the smart metasurface and a second visible area of ​​each user on the smart metasurface are obtained.

[0040] Specifically, the line-of-sight transmission link between the base station and user i{i=1,2,...,I} is severely attenuated due to obstacles, but due to the presence of multiple scatterers in the surrounding environment, a weak non-line-of-sight direct transmission path can be provided. Scatterers are objects that can change the propagation path of electromagnetic waves in a wireless communication environment. In order to improve the quality of wireless transmission, a scatterer such as Figure 1 The reflective uniform cylindrical smart metasurface shown in the figure is used to assist in establishing a reflective link. The smart metasurface can adopt a variety of geometric forms, such as planar, spherical, etc., according to the application scenario and design requirements.

[0041] In one embodiment, step S201 further includes the following sub-steps S2011-S2014:

[0042] S2011. Determine an arrival angle of a signal located on a first channel based on the base station location information and the smart metasurface location information, wherein the first channel is located between the base station and the smart metasurface.

[0043] According to the location coordinates of the base station and the location coordinates of the intelligent metasurface, the horizontal signal arrival angle in the line-of-sight component of the first channel is calculated. and vertical signal arrival angle

[0044] S2012. Based on the signal arrival angle, obtain a first visible area of ​​the base station on the smart metasurface, wherein the first visible area is used to indicate an area in which the signal sent from the base station can directly reach the smart metasurface without being blocked.

[0045] S2013. Determine a signal departure angle of a second channel based on the smart metasurface position information and the position information of each user, wherein the second channel is located between the smart metasurface and the user.

[0046] According to the position coordinates of the smart metasurface and the position coordinates of each user, the horizontal signal departure angle in the line-of-sight component of the second channel is calculated. and vertical signal departure angle

[0047] S2014. Based on the signal departure angle, obtain a second visible area of ​​each user on the smart metasurface, wherein the second visible area is used to indicate an area where the signal emitted from the smart metasurface can reach the user directly without being blocked.

[0048] Generally speaking, the horizontal signal departure angle and vertical signal departure angle of different users are different due to different user positions. The second visual area includes the visual area of ​​each user on the smart metasurface.

[0049] In step S202, all units in the first visible area and the second visible area are classified and marked to obtain multiple marked units, including multiple multi-user common units and multiple user-specific units, wherein the multi-user common units are located in the common visible area of ​​multiple users, and the user-specific units are located in the visible area of ​​only one user.

[0050] Specifically, the base station will first mark each unit of the smart metasurface according to its own visible area and in combination with the visible areas of each user. In this process, the base station will divide the units of the smart metasurface into two types: one is a unit shared by multiple users, and the other is a unit unique to a certain user. For those smart metasurface units that are in the visible area of ​​multiple users, the base station will mark them as units shared by multiple users; and for those smart metasurface units that are only in the visible area of ​​a certain user i and not in the visible area of ​​any other user, the base station will mark them as units unique to the user. Among them, the phase shift design of the unit shared by multiple users will affect the wireless transmission performance of multiple users. Therefore, when designing the phase shift of these units, the base station needs to comprehensively consider the needs and situations of multiple users to ensure that the wireless transmission performance of each user can be optimized. A unit unique to a certain user only affects the wireless transmission performance of this user. When designing the phase shift, it is only necessary to optimize it according to the needs of this user without considering the situation of other users.

[0051] The base station marks each unit of the smart metasurface one by one by obtaining the visual area of ​​each user: for smart metasurface units in the visual area of ​​two or more users, the base station marks them as smart metasurface units shared by multiple users; for internal units that are only in the visual area of ​​a certain user and not in the visual area of ​​any other user, the base station marks them as smart metasurface units unique to that user. The phase shift of smart metasurface units shared by multiple users will affect the wireless transmission performance of multiple users, and the situation of multiple users must be considered comprehensively when designing the phase shift; while smart metasurface units unique to a single user only affect the wireless transmission performance of this user, and the situation of other users does not need to be considered when designing the phase shift.

[0052] In step S203, the base station is controlled to randomly generate multiple groups of initial reflection coefficient vectors, each group of initial reflection coefficient vectors includes a first reflection coefficient vector and a second reflection coefficient vector.

[0053] In one embodiment, the first reflection coefficient vector and the second reflection coefficient vector each include multiple initial reflection coefficients, each initial reflection coefficient matches a marking unit, the initial reflection coefficient vector corresponding to the multi-user common unit is the first reflection coefficient vector, the initial reflection coefficient vector corresponding to the user-unique unit is the second reflection coefficient vector, and each initial reflection coefficient includes a phase shift.

[0054] Specifically, phase shift refers to the change in the phase of a signal when it passes through a smart metasurface unit. Each marker unit can independently adjust the phase shift angle of its reflected signal. By adjusting the phase shift, the directionality and intensity of the signal can be controlled, thereby affecting the propagation characteristics of the signal in space. The reflection coefficient refers to the reflection characteristics of each marker unit on the smart metasurface, and the reflection coefficient vector is the collection of all these individual reflection coefficients, describing the state of the entire smart metasurface. The reflection coefficient is a complex value. For each marker unit on the smart metasurface, the reflection coefficient determines how the unit reflects the incident signal, and the amplitude is usually fixed to 1 (i.e., complete reflection), so the main adjustment is the phase shift angle to control the directionality and intensity of the reflected signal. The reflection coefficient vector is a collection of all reflection coefficients.

[0055] In step S204, based on the concatenated channels, the upper bound of the total ergodic spectrum efficiency of each group of initial reflection coefficient vectors is calculated.

[0056] In one embodiment, step S204 includes the following sub-steps:

[0057] S2041. Establish a cascade channel.

[0058] In one embodiment, the establishment of the cascade channel includes the following sub-steps A1-A6:

[0059] A1. Assume that the intelligent metasurface has N units, N = N r ×N c , where N r Represents the number of layers of the smart metasurface, N c Represents the number of cells in each layer of the smart metasurface.

[0060] A2. Establish the first visual area model:

[0061]

[0062] in, The array activation vector representing the base station is used to characterize the visible area of ​​the base station in the smart metasurface, that is, the first visible area. The relationship between the signal arrival angle and the first visible area is represented by the first visible area model;

[0063] represents the horizontal signal arrival angle from the base station to the smart metasurface;

[0064] represents a column vector of all 1s,

[0065] express The function of

[0066] First viewing area The i(1 <i<N r ) elements r i Determined by the following formula:

[0067]

[0068] For N=N r ×N c The cylindrical intelligent metasurface consists of units. Each element in corresponds to a smart metasurface unit one by one. i is 1, then the unit is considered to be in the first visible area. i If the value is 0, it is considered that the unit is not in the first visible area. The unit in the first visible area is marked as a marked unit. Step A2 is also a further explanation of step S202.

[0069] A3. Establish the second visual area model:

[0070]

[0071] in, The array activation vector representing user i is used to characterize the visual area of ​​user i on the smart metasurface, that is, the second visual area. The relationship between the signal departure angle and the second visual area is represented by the second visual area model;

[0072] It represents the horizontal signal departure angle from the smart metasurface to the user;

[0073] represents a column vector of all 1s,

[0074] express The function of

[0075] Second viewing area The i(1 <i<N r ) elements r i Determined by the following formula:

[0076]

[0077] For N=N r ×N c The cylindrical intelligent metasurface consists of units. Each element in corresponds to a smart metasurface unit one by one. iis 1, then the unit is considered to be in the second visual area, and the element r i If it is 0, it is considered that the unit is not in the second visual area. The unit in the second visual area is marked as a marked unit. Step A3 is also a further explanation of step S202.

[0078] A4. Establish a first channel model based on the first visible area model.

[0079] The first channel model is expressed as:

[0080]

[0081]

[0082]

[0083] Among them, β br represents the large-scale fading factor of the first channel, represents the Ricean factor of the first channel, represents the array response vector of the uniform linear array at the base station, The array response vector representing the uniform cylindrical array in the first visible area of ​​the smart metasurface, represents the Kronecker product of two vectors, represents the non-line-of-sight transmission component of the first channel. The element corresponding to the visible area unit position satisfies the complex Gaussian distribution with a mean of 0 and a variance of 1. M represents the number of base station antenna units. The base station antennas are arranged in a uniform linear array. Indicates the signal departure angle of the signal at the base station.

[0084] represents the first visible area, d represents the distance between adjacent antennas of the base station, λ represents the wavelength of the propagation signal, and an item in the vector In represents the phase response of this unit, μ is related to the spacing between adjacent marking units, the wavelength of the propagating signal and the number of units in each layer, μ = πd / [2λsin(π / N r )], the cylindrical intelligent super surface has N = N r ×N c Units, N r is the number of units per layer, N c is the number of unit layers.

[0085] A5. Establish a second channel model based on the second visible area model.

[0086] The second channel model is expressed as:

[0087]

[0088]

[0089] in, represents the large-scale fading factor of the second channel, K i ru represents the Ricean factor of the second channel, The array response vector of the uniform cylindrical array representing the second visible zone of the smart metasurface, the visible zone of the user end for the smart metasurface, i.e., the second visible zone, refers to a set of smart metasurface units, i.e., marking units, in which the signal reflected by the smart metasurface unit can be transmitted to the user end through the line-of-sight path, i.e., the marking unit, and the marking unit includes a plurality of multi-user common units and a plurality of user-exclusive units. express and The Hadamard product. and They respectively represent the horizontal signal departure angle and vertical signal departure angle in the line-of-sight component of the second channel. It indicates that the non-line-of-sight transmission component of the second channel corresponds to the element of the visible area unit position that satisfies the complex Gaussian distribution with a mean of 0 and a variance of 1.

[0090] A6. Establish a cascade channel according to the first channel model and the second channel model.

[0091] The formula for the cascade channel is expressed as:

[0092]

[0093] Where H represents the first channel model, h i represents the second channel model, Represented by the initial reflection coefficient vector The resulting diagonal matrix is, In the reflection coefficient vector, e is a natural constant and j is an imaginary unit. It represents the reflection phase of the Nth unit of the smart metasurface, and the reflection phase is the phase shift.

[0094] Specifically, there are two channels between the base station and user i, one is the cascade channel through the smart metasurface One is a direct channel Each element has a mean of 0 and a variance of The complex Gaussian distribution of ,∈ indicates that it belongs to the symbol, Represents a complex space of dimension M×1.

[0095] S2042: Based on the established cascade channel, obtain a received signal of user i.

[0096] The received signal of user i is expressed by the following formula:

[0097]

[0098] Among them, p i Indicates the transmission power of the signal, represents the conjugate transpose operation, f i represents the precoder, n i It means the mean is 0 and the variance is Complex Gaussian white noise.

[0099] Consider using frequency division multiple access technology, ignoring the mutual interference between multiple users, and adopting MRT precoder, that is,

[0100] S2043: Obtain a signal-to-noise ratio of user i based on the received signal of user i.

[0101] The signal-to-noise ratio of user i is expressed as follows:

[0102]

[0103] Among them, the above and The meanings of the parameters in are consistent.

[0104] S2044, the signal-to-noise ratio of user i, using Jensen inequality to obtain the upper bound of the ergodic spectrum efficiency of user i:

[0105]

[0106] We further obtain the upper bound of the spectrum efficiency of user i as:

[0107]

[0108] S2045. Based on the ergodic spectrum efficiency upper bound of user i, obtain a total ergodic spectrum efficiency upper bound for each group of initial reflection coefficient vectors.

[0109] The base station randomly generates K groups of initial reflection coefficient vectors for the smart metasurface, and measures the initial wireless transmission performance of the system by calculating the total ergodic spectrum efficiency of the wireless communication system corresponding to each group of initial reflection coefficient vectors. The upper bound of the total ergodic spectrum efficiency of the wireless communication system is expressed by the following formula:

[0110]

[0111]

[0112]

[0113]

[0114] in, It represents the sum operation of the variables in the brackets from i=1 to i=I, p i Indicates the transmission power of the signal, It represents the variance of Gaussian white noise in the receiving end signal in the modeling, and also represents the energy of the noise. represents the line-of-sight path component in the channel from the smart metasurface to user i, Φ represents the smart metasurface phase shift matrix, which is composed of the initial reflection coefficient vector The resulting diagonal matrix is, Each element in represents the phase shift of each unit. represents the line-of-sight component in the channel from the base station to the smart metasurface, M represents the number of units of the smart metasurface, and r bs Right now represents the array activation vector of the base station, r ue,i Right now represents the array activation vector of user i, represents the Gaussian white noise variance at the receiving end of user i, which is related to the large-scale fading factor in the direct channel. η i represents the channel gain coefficient of user i, which is usually related to the large-scale fading factor β of the channel from the base station to the smart metasurface. br and the large-scale fading factor of the channel from the smart metasurface to user i It is also related to the Rice factor of the channel from the base station to the smart metasurface. And the Ricean factor of the channel from the smart metasurface to user i The Rice factor represents the ratio of line-of-sight to non-line-of-sight components in a channel and is related to η i similar, It is also a simplified coefficient, and the parameter meaning is the same as η i same.

[0115] It can be understood that the total ergodic spectrum efficiency of a wireless communication system refers to the effective bandwidth or data transmission rate that can be used by all users in a given time in the entire wireless communication network on average. It is a comprehensive indicator that reflects the overall data transmission capacity and resource utilization of the wireless communication system. A wireless communication system refers to a wireless communication network composed of base stations, smart metasurfaces, and multiple user devices. Ergodic means that the average performance over a long period of time is taken into account, rather than the instantaneous value. Wireless channel conditions change over time, and ergodic performance is more representative of the stability and reliability of the system. Spectral efficiency indicates the amount of data that can be transmitted within a unit bandwidth, usually measured in bits per second per hertz (bps / Hz).

[0116] In step S205, for each group of initial reflection coefficient vectors, the first reflection coefficient vector is optimized based on the total ergodic spectrum efficiency upper bound, and the second reflection coefficient vector is reset according to the optimization result of the first reflection coefficient vector.

[0117] In one embodiment, the above step S205 specifically includes the following sub-steps S2051-S2053:

[0118] S2051. Set the upper bound of the total ergodic spectrum efficiency as the objective function.

[0119] S2052: After it is determined that the objective function has converged, the iterative optimization of the first reflection coefficient vector is stopped, and the first reflection coefficient vector corresponding to when the iterative optimization is stopped is the first final reflection coefficient vector.

[0120] Specifically, determining whether the objective function converges includes the following sub-steps B1-B4:

[0121] B1. Setting iteration parameters according to the number of smart metasurface units, the iteration parameters including iteration step size and convergence threshold;

[0122] B2, calculating the phase shift gradient of the objective function for the multi-user shared unit;

[0123] B3, updating the first reflection coefficient vector by phase shift gradient and iteration step size;

[0124] B4. Recalculate the total ergodic spectrum efficiency of the wireless communication system by using the updated first reflection coefficient vector and the second reflection coefficient vector until the difference between two adjacent total ergodic spectrum efficiencies is less than the convergence threshold, and determine that the objective function converges.

[0125] Specifically, the total ergodic spectrum efficiency upper bound is Set as the objective function, the objective function reflects the maximum data transmission rate that the system can achieve under given channel conditions. Use an iterative algorithm to optimize the first reflection coefficient vector, especially for multi-user shared units, and do not process user-specific units.

[0126] For example, the gradient descent algorithm is selected to iteratively optimize the first reflection coefficient vector: the iteration step ε and the convergence threshold υ in the iteration process are set according to the number of smart hypersurface units N, the iteration step ε is set to -c1log2(N), and the convergence threshold υ is set to -c2log2(N), where c1 and c2 are constants. Calculate the objective function Phase shifting for multi-user shared units on smart metasurfaces Gradient

[0127] Combined with the set iteration step size ε and phase shift gradient The first reflection coefficient vector of the smart metasurface After iterative optimization, the phase shift of the mth marking unit of the smart metasurface is updated as follows:

[0128]

[0129] in, represents the phase shift of the mth unit in the multi-user shared unit of the smart metasurface, after the oth optimization; Represents the result after this update, that is, the phase shift of the mth unit in the multi-user shared unit of the smart metasurface, the value after the o+1th optimization.

[0130] After each step of iterative optimization, calculate the total traversal spectrum efficiency of the system at this time Then repeat the above iterative process until the difference between the total ergodic spectrum efficiency of two consecutive iterations is less than the set convergence threshold υ, that is, after calculating the total ergodic spectrum efficiency of the system for the t+1th time, The algorithm stops iterative optimization and considers that the objective function has converged. Since the base station randomly generates K groups of initial reflection coefficient vectors for the intelligent metasurface, each group of initial reflection coefficient vectors includes a first reflection coefficient vector and a second reflection coefficient vector, the first reflection coefficient vector of each group of initial reflection coefficient vectors is iteratively optimized using the above-mentioned S2051-S2052 method.

[0131] S2053. Reset the second reflection coefficient vector according to the first final reflection coefficient vector.

[0132] Specifically, after the base station completes the phase shift update of the multi-user common unit, the base station directly sets the phase shift of the user-specific unit of the intelligent metasurface according to the optimization result of the phase shift of the multi-user common unit in its visual area and the channel state information of the user, that is, for the unit n that is only in the visual area of ​​user i, the phase shift of the user-specific unit is It can be expressed by the following formula:

[0133]

[0134] in, represents the target phase shift of the nth user-unique unit, The array response vector representing the uniform cylindrical array in the first viewing area is The kth item in The array response vector representing the uniform cylindrical array in the second viewing area is The kth item in represents the phase shift factor of the kth unit of the smart metasurface, Arg represents the complex number of the phase angle, i.e., the phase shift, and Indicates user channel status information, It represents the phase shift optimization result of the multi-user shared unit in the visible area.

[0135] Specifically, the calculated target phase shift of the user-specific unit is directly applied to the second reflection coefficient vector, and for each user-specific unit n, its phase shift is Directly set to the target phase shift, thus forming a new second reflection coefficient vector By resetting the second reflection coefficient vector Combined with the first final reflection coefficient vector, recalculate the total ergodic spectral efficiency Unlike the first reflection coefficient vector, the second reflection coefficient vector does not require iterative optimization and can be directly configured based on the result of iterative optimization of the first reflection coefficient vector and the user channel state information. At this point, all reflection coefficient vectors of the smart metasurface have been optimized.

[0136] It should be noted that the first reflection coefficient vector is gradually adjusted by an iterative optimization algorithm (such as the gradient descent method), mainly targeting the phase shift of the multi-user shared unit, and its goal is to maximize the total ergodic spectrum efficiency of the system. And it takes multiple iterations to reach convergence. In each iteration, the phase shift value is updated to gradually approach the optimal solution. It is necessary to set parameters such as the iteration step size and the convergence threshold to control the optimization process. The second reflection coefficient vector is set directly based on the user's channel state information and the optimization result of the first reflection coefficient vector. It is mainly used for the phase shift of the user's unique unit. It does not require iterative optimization, but is directly configured based on the existing information. It is equivalent to optimizing only part of the entire reflection coefficient vector, but it can also obtain a local optimal solution, which simplifies the calculation process and speeds up the calculation of the phase shift of the intelligent metasurface unit.

[0137] In step S206, based on the optimized first reflection coefficient vector and the reset second reflection coefficient vector, the total ergodic spectrum efficiency of each group of initial reflection coefficient vectors is obtained.

[0138] Specifically, the base station randomly generates K groups of initial reflection coefficient vectors for the intelligent metasurface, and recalculates the total traversal spectrum efficiency corresponding to each group for the K groups of optimized first reflection coefficient vectors and the reset second reflection coefficient vectors. First, the first reflection coefficient vector and the second reflection coefficient vector are merged into a complete reflection coefficient vector. For each user i, the final signal-to-noise ratio of each user is calculated using the merged reflection coefficient vector, and the total ergodic spectrum efficiency corresponding to the reflection coefficient vector (including the optimized first reflection coefficient vector and the reset second reflection coefficient vector) is calculated according to the final signal-to-noise ratio.

[0139] In step S207, multiple groups of total ergodic spectrum efficiencies are compared, and the maximum total ergodic spectrum efficiency is selected, and the intelligent metasurface reflection coefficient vector corresponding to the maximum total ergodic spectrum efficiency is the optimization result.

[0140] The present application discloses a low-complexity curved metasurface unit phase shift optimization algorithm based on the visible area. The algorithm divides the visible area of ​​the smart metasurface for different users by utilizing the unique physical structure of the uniform cylindrical smart metasurface. Compared with the traditional uniform planar smart metasurface, the multi-user shared units of the uniform cylindrical smart metasurface are less than the total number of smart metasurface units. The algorithm only uses an iterative algorithm to optimize the phase shift of the shared units, and further directly sets the phase shift of the single-user unique unit based on the optimization result of the shared unit phase shift and the channel state information of the single user, thereby significantly reducing the complexity of the smart metasurface unit phase shift design. Figure 3 As shown, Figure 3 This is a comparison of the average number of convergence iterations at different convergence thresholds when the smart metasurface is set to a uniform cylindrical array and a uniform planar array in the same scenario. The horizontal axis represents the convergence threshold from 10 -12 to 10 -6 , the vertical axis represents the average number of iterations for convergence from 800 to 2800. As the convergence threshold decreases (i.e., the accuracy requirement increases), the average number of iterations for convergence gradually increases. This indicates that more iterations are required to achieve higher accuracy. Under all convergence thresholds, the average number of iterations for convergence of the uniform cylindrical array is lower than that of the uniform planar array, which means that under the same accuracy requirement, the uniform cylindrical array requires fewer iterations to converge. When the convergence threshold is 10 -6 When the convergence threshold is 10, the average number of iterations for the uniform cylindrical array is about 800, while that for the uniform planar array is about 1400. -12 The average number of iterations for convergence of the uniform cylindrical array is about 2200, while that of the uniform planar array is about 2700. The uniform cylindrical array converges faster than the uniform planar array at all tested convergence thresholds. The chart clearly shows the difference in convergence performance between the uniform cylindrical array and the uniform planar array under different accuracy requirements. The uniform cylindrical array shows better convergence performance under all tested conditions, which means that in practical applications, choosing a uniform cylindrical array will make the calculation more efficient.

[0141] Based on the same inventive concept, the embodiment of the present application also provides a device for realizing the phase shift optimization of the intelligent metasurface unit involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more intelligent metasurface unit phase shift optimization devices provided below can refer to the limitations of the intelligent metasurface unit phase shift optimization method above, and will not be repeated here.

[0142] In an exemplary embodiment, Figure 4 As shown, a smart metasurface unit phase shift optimization device is provided, comprising:

[0143] An acquisition module 410 is used to acquire a first visible area of ​​the base station on the smart super surface and a second visible area of ​​each user on the smart super surface based on the base station location information, the smart super surface location information and each user location information;

[0144] The marking module 420 is used to classify and mark all the units in the first visible area and the second visible area to obtain a plurality of marked units, wherein the marked units include a plurality of multi-user common units and a plurality of user-specific units, wherein the multi-user common units are located in a common visible area of ​​the plurality of users, and the user-specific units are located in a visible area of ​​only one user;

[0145] A control module 430, configured to control the base station to randomly generate multiple groups of initial reflection coefficient vectors, each group of the initial reflection coefficient vectors including a first reflection coefficient vector and a second reflection coefficient vector;

[0146] A calculation module 440 is used to calculate the upper bound of the total ergodic spectrum efficiency of each group of the initial reflection coefficient vectors based on the cascaded channels;

[0147] An optimization module 450 is used to optimize the first reflection coefficient vector for each group of the initial reflection coefficient vectors based on the total ergodic spectrum efficiency upper bound, and reset the second reflection coefficient vector according to the optimization result of the first reflection coefficient vector;

[0148] An obtaining module 460 is used to obtain a total ergodic spectrum efficiency of each group of the initial reflection coefficient vectors based on the optimized first reflection coefficient vector and the reset second reflection coefficient vector;

[0149] The selection module 470 is used to compare the multiple groups of total ergodic spectrum efficiencies and select the maximum total ergodic spectrum efficiency. The intelligent metasurface reflection coefficient vector corresponding to the maximum total ergodic spectrum efficiency is the optimization result.

[0150] As an optional implementation, the acquisition module 410 is used to:

[0151] Determine, based on the base station location information and the smart metasurface location information, a signal arrival angle on a first channel, wherein the first channel is located between the base station and the smart metasurface;

[0152] Based on the signal arrival angle, obtaining the first visible area of ​​the base station on the smart metasurface, wherein the first visible area is used to indicate an area where a signal sent from the base station can directly reach the smart metasurface without being blocked;

[0153] Determine a signal departure angle located at a second channel based on the smart metasurface position information and each of the user position information, wherein the second channel is located between the smart metasurface and the user;

[0154] Based on the signal departure angle, the second visible area of ​​each user on the smart metasurface is obtained, wherein the second visible area is used to indicate an area in which the signal emitted from the smart metasurface can directly reach the user without being blocked.

[0155] As an optional implementation, the first reflection coefficient vector and the second reflection coefficient vector each include multiple initial reflection coefficients, each of the initial reflection coefficients matches one of the marking units, the initial reflection coefficient vector corresponding to the multi-user common unit is the first reflection coefficient vector, the initial reflection coefficient vector corresponding to the user-unique unit is the second reflection coefficient vector, and each initial reflection coefficient includes a phase shift.

[0156] As an optional implementation, the cascade channel is acquired through the first visible area and the second visible area.

[0157] As an optional implementation, the optimization module 450 is used to:

[0158] Setting the total ergodic spectrum efficiency upper bound as an objective function;

[0159] After it is determined that the objective function has converged, the iterative optimization of the first reflection coefficient vector is stopped, and the first reflection coefficient vector corresponding to the time when the iterative optimization is stopped is the first final reflection coefficient vector;

[0160] The second reflection coefficient vector is optimized according to the first final reflection coefficient vector.

[0161] As an optional implementation, in determining whether the objective function converges, the optimization module 450 is specifically configured to:

[0162] Setting iteration parameters according to the number of marked units, the iteration parameters including iteration step length and convergence threshold;

[0163] Calculating the phase shift gradient of the objective function for the multi-user shared unit;

[0164] Update the first reflection coefficient vector by using the phase shift gradient and the iteration step size;

[0165] The total ergodic spectrum efficiency of the wireless communication system is recalculated by using the updated first reflection coefficient vector until the difference between two consecutive total ergodic spectrum efficiencies is less than the convergence threshold, and it is determined that the objective function converges.

[0166] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Wherein, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Wherein, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the final reflection coefficient vector. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a phase shift optimization method for an intelligent metasurface unit is implemented.

[0167] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0168] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0169] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0170] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0171] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0172] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0173] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0174] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0175] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for optimizing phase shift of a smart metasurface unit, characterized in that: The intelligent metasurface unit phase shift optimization method comprises: Based on the base station location information, the smart metasurface location information and the location information of each user, obtaining a first visible area of ​​the base station on the smart metasurface and a second visible area of ​​each user on the smart metasurface; Classify and mark all units in the first visible area and the second visible area to obtain a plurality of marked units, wherein the marked units include a plurality of multi-user common units and a plurality of user-specific units, wherein the multi-user common units are located in a common visible area of ​​a plurality of the users, and the user-specific units are located in a visible area of ​​only one user; Controlling the base station to randomly generate multiple groups of initial reflection coefficient vectors, each group of the initial reflection coefficient vectors includes a first reflection coefficient vector and a second reflection coefficient vector; Based on the cascaded channels, calculating the upper bound of the total ergodic spectrum efficiency of each group of the initial reflection coefficient vectors; For each group of the initial reflection coefficient vectors, optimizing the first reflection coefficient vectors based on the total ergodic spectrum efficiency upper bound, and resetting the second reflection coefficient vectors according to the optimization result of the first reflection coefficient vectors; Based on the optimized first reflection coefficient vector and the reset second reflection coefficient vector, obtaining a total ergodic spectrum efficiency of each group of the initial reflection coefficient vectors; Compare multiple groups of the total ergodic spectrum efficiencies, select the maximum total ergodic spectrum efficiency, and the intelligent metasurface reflection coefficient vector corresponding to the maximum total ergodic spectrum efficiency is the optimization result.

2. The phase shift optimization method of the smart metasurface unit according to claim 1, characterized in that: The method of acquiring the first visible area of ​​the base station on the smart super surface and the second visible area of ​​each user on the smart super surface based on the base station location information, the smart super surface location information and the location information of each user includes: Determine, based on the base station location information and the smart metasurface location information, a signal arrival angle on a first channel, wherein the first channel is located between the base station and the smart metasurface; Based on the signal arrival angle, obtaining the first visible area of ​​the base station on the smart metasurface, wherein the first visible area is used to indicate an area where a signal sent from the base station can directly reach the smart metasurface without being blocked; Determine a signal departure angle located at a second channel based on the smart metasurface position information and each of the user position information, wherein the second channel is located between the smart metasurface and the user; Based on the signal departure angle, the second visible area of ​​each user on the smart metasurface is obtained, wherein the second visible area is used to indicate an area in which the signal emitted from the smart metasurface can directly reach the user without being blocked.

3. The phase shift optimization method of the smart metasurface unit according to claim 1, characterized in that: The first reflection coefficient vector and the second reflection coefficient vector each include multiple initial reflection coefficients, each of the initial reflection coefficients matches one of the marking units, the initial reflection coefficient vector corresponding to the multi-user common unit is the first reflection coefficient vector, the initial reflection coefficient vector corresponding to the user-unique unit is the second reflection coefficient vector, and each initial reflection coefficient includes a phase shift.

4. The phase shift optimization method of the smart metasurface unit according to claim 1, characterized in that: The cascade channel is acquired through the first visible area and the second visible area.

5. The method for optimizing phase shift of a smart metasurface unit according to any one of claims 1 to 4, characterized in that: The optimizing the first reflection coefficient vector based on the total ergodic spectrum efficiency upper bound, and resetting the second reflection coefficient vector according to the optimization result of the first reflection coefficient vector, comprises: Setting the total ergodic spectrum efficiency upper bound as an objective function; After it is determined that the objective function has converged, the iterative optimization of the first reflection coefficient vector is stopped, and the first reflection coefficient vector corresponding to the time when the iterative optimization is stopped is the first final reflection coefficient vector; The second reflection coefficient vector is reset according to the first final reflection coefficient vector.

6. The method for optimizing phase shift of a smart metasurface unit according to claim 5, characterized in that: The determining that the objective function converges comprises: Setting iteration parameters according to the number of smart metasurface units, wherein the iteration parameters include an iteration step size and a convergence threshold; Calculating the phase shift gradient of the objective function for the multi-user shared unit; Update the first reflection coefficient vector by using the phase shift gradient and the iteration step size; The total ergodic spectrum efficiency of the wireless communication system is recalculated by using the updated first reflection coefficient vector until the difference between two consecutive total ergodic spectrum efficiencies is less than the convergence threshold, and it is determined that the objective function converges.

7. An intelligent metasurface unit phase shift optimization device, characterized in that: The intelligent metasurface unit phase shift optimization device comprises: An acquisition module, configured to acquire a first visible area of ​​the base station on the smart super surface and a second visible area of ​​each user on the smart super surface based on the base station location information, the smart super surface location information and each user location information; a marking module, configured to classify and mark all units in the first visible area and the second visible area to obtain a plurality of marked units, wherein the marked units include a plurality of multi-user common units and a plurality of user-specific units, wherein the multi-user common units are located in a common visible area of ​​a plurality of the users, and the user-specific units are located in a visible area of ​​only one user; A control module, used for controlling the base station to randomly generate a plurality of groups of initial reflection coefficient vectors, each group of the initial reflection coefficient vectors comprising a first reflection coefficient vector and a second reflection coefficient vector; A calculation module, used for calculating the upper bound of the total ergodic spectrum efficiency of each group of the initial reflection coefficient vectors based on the cascaded channels; an optimization module, configured to optimize the first reflection coefficient vector for each group of the initial reflection coefficient vectors based on the total ergodic spectrum efficiency upper bound, and reset the second reflection coefficient vector according to the optimization result of the first reflection coefficient vector; An obtaining module, configured to obtain a total ergodic spectrum efficiency of each group of the initial reflection coefficient vectors based on the optimized first reflection coefficient vector and the reset second reflection coefficient vector; A selection module is used to compare multiple groups of the total ergodic spectrum efficiencies and select the maximum total ergodic spectrum efficiency, and the intelligent metasurface reflection coefficient vector corresponding to the maximum total ergodic spectrum efficiency is the optimization result.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the smart metasurface unit phase shift optimization method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the smart metasurface unit phase shift optimization method described in any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the smart metasurface unit phase shift optimization method described in any one of claims 1 to 6 are implemented.

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