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

Abstract

This application discloses a method, apparatus, device, medium, and product for optimizing the phase shift of an intelligent metasurface unit, relating to the field of phase shift optimization. The method includes obtaining a first visible area and a second visible area based on base station location information, intelligent metasurface location information, and location information of each user; controlling the base station to randomly generate multiple groups of initial reflection coefficient vectors, each group of initial reflection coefficient vectors including a first reflection coefficient vector and a second reflection coefficient vector; calculating an upper bound on the total ergodic spectrum efficiency of each group of initial reflection coefficient vectors based on a cascaded channel; optimizing the first reflection coefficient vector based on the upper bound on the total ergodic spectrum efficiency; obtaining the total ergodic spectrum efficiency of each group of initial reflection coefficient vectors based on the optimized first reflection coefficient vector and the reset second reflection coefficient vector; and comparing the multiple groups of total ergodic spectrum efficiencies to select the maximum total ergodic spectrum efficiency. This application can improve the efficiency of calculating the phase shift of an intelligent metasurface unit.

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 phase shift optimization of an intelligent metasurface unit. Background Art

[0002] A complete wireless communication system typically consists of a transmitter, a wireless channel, and a receiver. In actual wireless communication, factors such as channel non-idealities can cause significant fading or distortion of the transmitted signal. This necessitates the introduction of wireless relay systems to improve signal transmission quality. Traditional wireless relay systems receive signals from the transmitter and then amplify, regenerate, or forward them to the receiver to extend the signal's transmission distance or improve signal quality. However, traditional repeaters typically include numerous active components and RF links, increasing deployment costs.

[0003] In recent years, a smart metasurface composed of programmable two-dimensional electromagnetic metamaterials has been proposed. The smart metasurface is introduced into the wireless communication system as a new type of wireless relay, so that 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. Moreover, 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 an intelligent 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] Classifying and marking all cells in the first visual area and the second visual area to obtain a plurality of marked cells, the marked cells including a plurality of multi-user common cells and a plurality of user-specific cells, wherein the multi-user common cells are located in a common visual area of ​​the plurality of users, and the user-specific cells are located in a visual 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] Calculating the upper bound of the total ergodic spectrum efficiency of each group of the initial reflection coefficient vectors based on the cascaded channels;

[0012] For each group of the initial reflection coefficient vectors, 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;

[0013] Obtaining 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;

[0014] Compare multiple groups of the 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.

[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 metasurface and a second visible area of ​​each user on the smart metasurface based on the base station location information, the smart metasurface location information, and the location information of each user;

[0017] a marking module, configured to classify and mark all units in the first visual area and the second visual 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 visual area of ​​the plurality of users, and the user-specific units are located in a visual area of ​​only one user;

[0018] a control module, 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;

[0019] A calculation module, configured to calculate an upper bound of a 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 the smart metasurface unit phase shift optimization method described above.

[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 one of the above-mentioned smart metasurface unit phase shift optimization methods.

[0025] In a fifth aspect, the present application provides a computer program product, comprising 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 on 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, 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 upper bound of the total ergodic spectrum efficiency 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 upper bound of the total ergodic spectrum efficiency, 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. This 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 following briefly introduces the drawings required for use in the embodiments. 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 creative work.

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

[0030] Figure 2 A schematic flow chart of a method for optimizing phase shift of an intelligent metasurface unit provided in one embodiment of the present application;

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

[0032] Figure 4A schematic diagram of the 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 this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0035] In order to make the above-mentioned purposes, 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 phase shift optimization method of the intelligent metasurface unit provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, a base station is the transmitter in a wireless communication system, responsible for sending signals to users. The smart metasurface, composed of multiple programmable reflective units, 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, providing signal reflection and amplification. Figure 1 In this example, only user terminals including user 1 and user 2 are used as receiving devices, and are used to receive signals from the base station through the smart metasurface. The part with common units for users 1 and 2 indicates that these reflective units serve both users 1 and 2. The unique units of user 1 indicate that these reflective units only serve user 1, and the unique units of user 2 indicate that these reflective 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 based on different reflective units, namely marking units, to improve signal quality and spectrum efficiency. By optimizing the phase shift of the reflective units, the spectrum utilization can be maximized and the total traversal spectrum efficiency of the system can be improved.

[0037] The base station is equipped with a data storage system and a server. The data storage system can store data that the server needs to process. The data storage system can be set up separately, integrated on the server, or placed on the cloud or other servers. After receiving the first and second visual areas to be processed, the server classifies and labels all units in the first and second visual areas to obtain multiple labeled units, including multiple multi-user shared units and multiple user-specific units. The server controls the base station to randomly generate multiple sets of initial reflection coefficient vectors, each set of initial reflection coefficient vectors containing a first reflection coefficient vector and a second reflection coefficient vector. Based on the cascaded channel, the upper bound of the total ergodic spectrum efficiency of each set of initial reflection coefficient vectors is calculated. For each set of initial reflection coefficient vectors, the first reflection coefficient vector is optimized based on the upper bound of the total ergodic spectrum efficiency. Based on 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 set of initial reflection coefficient vectors is obtained. The multiple sets of total ergodic spectrum efficiencies are 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. Furthermore, in some embodiments, the intelligent metasurface unit phase shift optimization method can also be implemented solely by a server. For example, the server can retrieve the first and second visual areas to be processed from a data storage system and process the first and second visual areas. The server can be implemented as a standalone server or a server cluster consisting of multiple servers, or even 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 following steps S201 to S207 are included:

[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 the obstruction of 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 network such as Figure 1 The reflective, uniform cylindrical smart metasurface shown in Figure 1 is used to assist in establishing a reflective link. Smart metasurfaces can adopt various geometric forms, such as planar and spherical, depending on 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, where the first channel is located between the base station and the smart metasurface.

[0043] Calculate the horizontal signal arrival angle in the line-of-sight component of the first channel according to the base station location coordinates and the location coordinates of the smart metasurface 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, where the first visible area is used to indicate an area where 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, where the second channel is located between the smart metasurface and the user.

[0046] According to the position coordinates of the intelligent 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 for each user on the smart metasurface, where the second visible area is used to represent an area where the signal emitted from the smart metasurface can be unobstructed and directly reach the user.

[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 visual area and the second visual area are classified and marked to obtain a plurality of marked units, which 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 visual area of ​​multiple users, and the user-specific units are located in the visual area of ​​only one user.

[0050] Specifically, the base station will first mark each unit of the smart metasurface based on its own visible area and the visible areas of each user. During 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 within the visible areas of multiple users, the base station will mark them as units shared by multiple users; for those smart metasurface units that are only within the visible area of ​​a certain user i and not within the visible area of ​​any other user, the base station will mark them as units unique to that user. Among them, the phase shift design of the units 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. The units unique to a certain user only affect the wireless transmission performance of this user. When designing the phase shift, it is only necessary to optimize according to the needs of this user without considering the situations of other users.

[0051] The base station uses the visual information of each user to individually label each unit of the smart metasurface: For smart metasurface units within the visual areas of two or more users, the base station marks them as shared by multiple users; for units within the visual area of ​​only one user and not in the visual area of ​​any other user, the base station marks them as unique to that user. The phase shift of a shared smart metasurface unit will affect the wireless transmission performance of multiple users, so the multi-user situation must be comprehensively considered when designing the phase shift. However, a smart metasurface unit unique to a single user only affects the wireless transmission performance of that 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 shared unit is the first reflection coefficient vector, and the initial reflection coefficient vector corresponding to the user-specific 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 set 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 set containing 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, establishing 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 units in each layer of the smart metasurface.

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

[0061]

[0062] in, The array activation vector of the base station is used to characterize the visible area of ​​the base station on the smart metasurface, that is, the first visible area. The relationship between the signal arrival angle and the first visible area is expressed through 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 function,

[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 a unit. Each element in corresponds to a smart metasurface unit one by one. If r i If it is 1, the unit is considered to be in the first visual area. i If the value is 0, the unit is considered not to be 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, i.e., the second visual area. The relationship between the signal departure angle and the second visual area is expressed through the second visual area model.

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

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

[0074] express function,

[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 a unit. Each element in corresponds to a smart metasurface unit one by one. If r iIf it is 1, then the unit is considered to be in the second visual area, and the element r i If the value is 0, the unit is considered not to be 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 visual 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, It represents the non-line-of-sight transmission component of the first channel. The elements corresponding to the visible area unit position satisfy 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 base station antennas, 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 metasurface 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 visual 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 the set of smart metasurface units, i.e., the 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. The marking units include multiple multi-user shared units and multiple user-unique 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 components transmitted from the non-line-of-sight path of the second channel, the elements corresponding to the visible area unit positions satisfy the complex Gaussian distribution with mean 0 and variance 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:

[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, In the reflection coefficient vector, e is a natural constant, 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 the 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 signal transmission power, represents the conjugate transpose operation, f i represents the precoder, n i The mean is 0 and the variance is complex Gaussian white noise.

[0099] Consider using frequency division multiple access technology, do not consider the mutual interference between multiple users, and adopt 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 parameters in have the same meaning.

[0104] S2044, the signal-to-noise ratio of user i. Using Jensen’s inequality, the upper bound of the ergodic spectrum efficiency of user i is obtained as:

[0105]

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

[0107]

[0108] S2045 . Obtain an upper bound on the total ergodic spectrum efficiency of each group of initial reflection coefficient vectors based on the upper bound on the ergodic spectrum efficiency of user i.

[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, Indicates the sum operation of the variables in the brackets from i = 1 to i = I, p i Indicates the signal transmission power, 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, 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, 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. 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 characterizes the ratio of line-of-sight components to non-line-of-sight components in a channel, and is related to η i similar, It is also a simplified coefficient, in which the parameter meaning is the same as η i same.

[0115] The total ergodic spectrum efficiency of a wireless communication system refers to the effective bandwidth or data transmission rate available to all users in a given time period across the entire wireless communication network. It is a comprehensive indicator that reflects the overall data transmission capacity and resource utilization of the wireless communication system. A wireless communication system is a wireless communication network consisting of base stations, smart metasurfaces, and multiple user devices. Ergodic performance considers average performance over a long period of time, rather than instantaneous values. Wireless channel conditions vary over time, and ergodic performance better reflects the stability and reliability of the system. Spectral efficiency indicates the amount of data that can be transmitted within a unit of bandwidth and is typically 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 upper bound of the total ergodic spectrum efficiency, 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 the time when the iterative optimization is stopped is the first final reflection coefficient vector.

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

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

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

[0123] B3. Update the first reflection coefficient vector by using the phase shift gradient and the iteration step size;

[0124] B4. Recalculate the total ergodic spectrum efficiency of the wireless communication system 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 metasurface 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 of 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 Perform iterative optimization. After the oth iteration, the phase shift of the mth marking unit of the smart metasurface is updated to:

[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 adjacent 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 completing the phase shift update of the multi-user shared unit, the base station sets the phase shift of the user-specific unit of the smart metasurface according to the optimization result of the phase shift of the multi-user shared unit in its visual area and the channel state information of the user directly. That is, for 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 argument, i.e., 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 results of the 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 obtained by gradually adjusting the 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. Multiple iterations are required to achieve convergence. During each iteration, the phase shift value is updated to gradually approach the optimal solution. Parameters such as the iteration step size and convergence threshold are set to control the optimization process. The second reflection coefficient vector is set directly based on the user's channel state information and the optimization results of the first reflection coefficient vector. It is primarily used for the phase shift of the user's unique unit. It does not require iterative optimization, but is directly configured based on existing information. This is equivalent to optimizing only a portion of the entire reflection coefficient vector, but it can also obtain a local optimal solution. This simplifies the calculation process and speeds up the calculation of the phase shift of the intelligent metasurface unit.

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

[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 reset second reflection coefficient vectors. First, merge the first and second reflection coefficient vectors into a complete reflection coefficient vector. For each user i, use the merged reflection coefficient vector to calculate the final signal-to-noise ratio for each user. Based on the final signal-to-noise ratio, calculate 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).

[0139] In step S207, multiple groups of total ergodic spectrum efficiencies are 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.

[0140] The present application discloses a low-complexity curved metasurface unit phase shift optimization algorithm based on the visual zone, which divides the visual zones of different users for the smart metasurface 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 phase shift design of the smart metasurface unit. Figure 3 As shown, Figure 3 The comparison of the average number of convergence iterations at different convergence thresholds when the smart metasurface is set as 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 in order to achieve higher accuracy, more iterations are required. At 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 approximately 2200, while that of the uniform planar array is approximately 2700. The uniform cylindrical array converges faster than the uniform planar array at all tested convergence thresholds. This chart clearly demonstrates the difference in convergence performance between the uniform cylindrical array and the uniform planar array under different accuracy requirements. The uniform cylindrical array demonstrates better convergence performance under all tested conditions, indicating that in practical applications, choosing the uniform cylindrical array results in more efficient computations.

[0141] Based on the same inventive concept, the present application also provides an embodiment for implementing the aforementioned intelligent metasurface unit phase shift optimization device. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the intelligent metasurface unit phase shift optimization device provided below can be found in the above-mentioned limitations of the intelligent metasurface unit phase shift optimization method, 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 configured to acquire, 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;

[0144] a marking module 420 configured to classify and mark all cells in the first and second visual areas to obtain a plurality of marked cells, the marked cells including a plurality of multi-user common cells and a plurality of user-specific cells, wherein the multi-user common cells are located in a common visual area for the plurality of users, and the user-specific cells are located in a visual area for only one user;

[0145] A control module 430 is 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 configured to calculate an upper bound of a total ergodic spectrum efficiency of each group of the initial reflection coefficient vectors based on the cascaded channels;

[0147] an optimization module 450, 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;

[0148] An obtaining module 460 is 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;

[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 configured to:

[0151] Determining an arrival angle of a signal 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;

[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 in which a signal sent from the base station can directly reach the smart metasurface without being blocked;

[0153] Determining a signal departure angle on 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;

[0154] Based on the signal departure angle, the second visual area of ​​each user on the smart metasurface is obtained, wherein the second visual 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 embodiment, 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 shared unit is the first reflection coefficient vector, and 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 configured to:

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

[0159] When 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 include iteration step size and convergence threshold;

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

[0164] updating 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 using the updated first reflection coefficient vector until the difference between two adjacent 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, abbreviated as I / O) and a communication interface. 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. 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 shown in the figure, or combine certain components, or have a different component arrangement.

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

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

[0170] In an exemplary embodiment, a computer program product is provided, including a computer program. 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 skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented 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 memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may 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 may 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 databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0174] The technical features of the above embodiments can 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 document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may 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 an intelligent 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; Classifying and marking all cells in the first visual area and the second visual area to obtain a plurality of marked cells, the marked cells including a plurality of multi-user common cells and a plurality of user-specific cells, wherein the multi-user common cells are located in a common visual area of ​​the plurality of users, and the user-specific cells are located in a visual 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, 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 a multi-user shared unit is a first reflection coefficient vector, the initial reflection coefficient vector corresponding to a user-specific unit is a second reflection coefficient vector, and each initial reflection coefficient includes a phase shift; Calculating the upper bound of the total ergodic spectrum efficiency of each group of the initial reflection coefficient vectors based on the cascaded channels; For each group of the initial reflection coefficient vectors, 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; Obtaining 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; Compare multiple groups of the 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.

2. The phase shift optimization method of the intelligent metasurface unit according to claim 1, characterized in that: The obtaining, based on the base station location information, the smart metasurface location information, and the location information of each user, of a first visible area of ​​the base station on the smart metasurface and a second visible area of ​​each user on the smart metasurface includes: Determining an arrival angle of a signal 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; 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 in which a signal sent from the base station can directly reach the smart metasurface without being blocked; Determining a signal departure angle on 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; Based on the signal departure angle, the second visual area of ​​each user on the smart metasurface is obtained, wherein the second visual 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 intelligent metasurface unit according to claim 1, characterized in that: The cascade channel is acquired through the first visual area and the second visual area.

4. The method for optimizing phase shift of an intelligent metasurface unit according to any one of claims 1 to 3, wherein: 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 upper bound of the total ergodic spectrum efficiency as an objective function; When 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.

5. The phase shift optimization method of the intelligent metasurface unit according to claim 4, characterized in that: Determining whether the objective function converges includes: 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 a phase shift gradient of the objective function for the multi-user shared unit; updating 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 using the updated first reflection coefficient vector until a difference between two adjacent total ergodic spectrum efficiencies is less than the convergence threshold, and it is determined that the objective function converges.

6. 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 metasurface and a second visible area of ​​each user on the smart metasurface based on the base station location information, the smart metasurface location information, and the location information of each user; a marking module, configured to classify and mark all units in the first visual area and the second visual 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 visual area of ​​the plurality of users, and the user-specific units are located in a visual area of ​​only one user; a control module, 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, the first reflection coefficient vector and the second reflection coefficient vector each including multiple initial reflection coefficients, each of the initial reflection coefficients matching one of the marking units, the initial reflection coefficient vector corresponding to a multi-user shared unit being the first reflection coefficient vector, the initial reflection coefficient vector corresponding to a user-specific unit being the second reflection coefficient vector, and each initial reflection coefficient including a phase shift; A calculation module, configured to calculate an upper bound of a 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.

7. 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 5.

8. 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 5 are implemented.

9. 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 5 are implemented.

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