Reconfigurable antenna pattern design method and system based on zero-forcing precoding

Abstract

The invention discloses a reconfigurable antenna pattern design method and system based on zero-forcing precoding, and belongs to the technical field of wireless communication, and the method comprises the steps: building a non-convex optimization model for maximizing a user signal-to-noise ratio in a single mode through employing the user receiving signal-to-noise ratio under zero-forcing precoding; solving the non-convex optimization model for maximizing the signal-to-noise ratio of the user in the single mode through a continuous convex approximation SCA algorithm, and designing a related convex problem by using first-order Taylor expansion to realize iterative solution of the non-convex optimization model; and designing a new proxy function by using a dominating-minimizing MM algorithm to simplify the solving process of the non-convex optimization model, and calculating to obtain a reconfigurable antenna pattern design scheme based on zero-forcing precoding. According to the method, the convergence speed of the model is high, the time complexity of the solving process is low, the efficiency of solving the single-mode design problem of the directional diagram reconfigurable system under zero-forcing precoding is improved, and application of the directional diagram reconfigurable antenna in a single-mode scene can be promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to a method and system for designing a reconfigurable antenna pattern based on zero-forcing precoding. Background Art

[0002] With the change and development of wireless communication technology, the contradiction between the rapid growth of the number of wireless devices and communication rates and limited spectrum resources is becoming increasingly severe. Faced with the inevitable fading, interference and other effects in the wireless propagation environment, how to make more full use of spectrum resources has become a great challenge in the design of communication systems. As an emerging technology in the physical layer of wireless communication, reconfigurable antenna technology can achieve frequency, polarization mode or antenna pattern reconstruction by changing the current distribution on the surface of its own antenna, thereby using additional degrees of freedom to improve the spectrum utilization of the communication system. Among them, the pattern reconfigurable antenna has received more extensive attention and application research due to its ability to actively change the channel. In the downlink multi-user multi-antenna (Multiple Input Multiple Output, MIMO) communication system, considering the complexity of user processing at the receiving end, the pattern reconfigurable system is more inclined to place the pattern reconfigurable antenna at the transmitting end to achieve it, that is, the transmitting end is reconfigurable.

[0003] The traditional reconfigurable pattern system selects the optimal pattern from a limited number of discrete patterns, aiming to select the pattern that makes the communication system perform best from multiple channels realized by different patterns, which is called the modal selection scheme of the reconfigurable pattern system. However, the modal selection scheme only achieves performance gain by utilizing the degree of freedom of pattern reconfiguration, and has not yet revealed the mechanism by which the reconfigurable pattern changes the channel to obtain performance gain. In recent years, the industry has proposed a modal design scheme for pattern reconfigurable systems based on the continuous adjustable assumption, which sets the pattern of the reconfigurable antenna to be continuously variable, describes the mechanism of the pattern reconfigurable system changing the channel through the pattern sampling matrix, and explores the maximum theoretical performance gain of the pattern reconfigurable system. Among them, the pattern sampling matrix refers to the matrix composed of the antenna pattern gain corresponding to the multipath angle in the wireless propagation environment.

[0004] At present, the research in recent years divides the modal design problem of the reconfigurable pattern system into the modal design problem of single mode and the modal design problem of multi-mode, and uses the mechanism of the reconfigurable pattern to redistribute the equivalent channel gain and the correlation between sub-channels to achieve the optimization solution of the modal design problem. Among them, single mode means that all reconfigurable antennas use the same continuously adjustable pattern, while multi-mode means that different reconfigurable antennas use different continuously adjustable patterns. The joint design problem of symbol-level precoding and modal design has been proposed in the existing literature, which splits the joint design problem into two sub-problems and then solves them by alternating optimization. Among them, precoding refers to the design of symbols transmitted by antennas, so that the receiving end user can effectively receive the target signal and achieve the performance indicators preset by the system, thereby completing an effective and reliable communication process. However, this scheme is always an approximation of the original problem and cannot achieve the optimal performance; secondly, this scheme only considers symbol-level precoding, and the precoding matrix needs to be updated in each time slot, which brings high complexity. Summary of the invention

[0005] The purpose of the present invention is to address the problems in the above-mentioned prior art and provide a method and system for designing a reconfigurable antenna pattern based on zero-forcing precoding, so as to effectively reduce the complexity of the solution, reduce the actual calculation overhead, and promote the application of reconfigurable antenna patterns in single-mode scenarios.

[0006] In order to achieve the above object, the present invention has the following technical solutions:

[0007] In a first aspect, a method for designing a reconfigurable antenna pattern based on zero-forcing precoding is provided, comprising:

[0008] Using the user received signal-to-noise ratio under zero-forcing precoding, a non-convex optimization model for maximizing the user signal-to-noise ratio under single mode is established.

[0009] The non-convex optimization model for maximizing the user signal-to-noise ratio under the single mode is solved by a continuous convex approximation SCA algorithm, and the first-order Taylor expansion is used to design related convex problems to achieve iterative solution of the non-convex optimization model;

[0010] A new proxy function is designed using the principalization-minimization MM algorithm to simplify the solution process of the non-convex optimization model, and the reconfigurable antenna pattern design scheme based on zero-forcing precoding is calculated.

[0011] As a preferred solution, the user receiving signal-to-noise ratio under the zero-forcing precoding is the same for all receiving users without considering power allocation, and the calculation expression is as follows:

[0012]

[0013] Where P is the total power of the transmitter; σ2 is the power of additive white Gaussian noise; N t is the number of antennas equipped at the base station; H is the reconfigurable channel of the single-mode directional pattern, and the calculation expression is:

[0014] H=MH path

[0015] Where M represents the matrix set of all user pattern sampling vectors; H path is the set of all user physical subchannels;

[0016]

[0017] H path =[H 1 ,H 2 ,…,H k ] T , m k and H k are the directional pattern sampling vector and physical subchannel of the kth user respectively.

[0018] As a preferred solution, the non-convex optimization model expression for maximizing the user signal-to-noise ratio under the single mode is as follows:

[0019]

[0020] st0≤M≤ε

[0021]

[0022] In the formula, is a semi-positive definite matrix; ε is a real number representing the maximum gain of the reconfigurable directional pattern; Represents the energy constraint of all user subchannels.

[0023] As a preferred solution, in the step of solving the non-convex optimization model for maximizing the user signal-to-noise ratio under the single mode by the continuous convex approximation SCA algorithm, the objective function is g(M)=tr((MRM H ) -1 ), is a non-convex function under the sparse matrix argument M;

[0024] The objective function is approximated by a first-order Taylor expansion for a non-convex function, and the expression is:

[0025]

[0026] In the formula, represents the gradient of the function g(·);

[0027] The gradient of the objective function is:

[0028]

[0029] The specific expression of the design-related convex problem is as follows:

[0030]

[0031] st0≤M≤ε

[0032]

[0033] Where M i is the iteration point corresponding to the i-th iteration; the related convex problem is solved by the convex optimization toolbox; using the obtained result M * The direction in which the original objective function descends is M * -M i , the iterative point of the i+1th iteration is deduced as:

[0034] M i+1 =M i +d(M * -M i )

[0035] Wherein, the iterative step size d∈(0,1) is calculated by the backtracking straight line search method.

[0036] As a preferred solution, under the continuous convex approximation SCA algorithm, the step length of the iteration is calculated by the backtracking straight line search method, including:

[0037] The maximum allowable step length s is calculated as follows: max :

[0038] s max =min{1,min{-m i / △m i |△m i <0}}

[0039] Repeat for the maximum allowed step size s max Multiply by the parameter β until:

[0040] 0≤M i+1 ≤ε, and All of them hold true, where α∈[0.01,0.1], β∈[0.3,0.8], M i+1 =M i +s·△M.

[0041] As a preferred solution, the process of designing a new surrogate function by using the mainization-minimization MM algorithm to simplify the solution of the non-convex optimization model includes:

[0042] For the original objective function, using the properties of matrix inversion, it can be rewritten as follows:

[0043]

[0044] Where adj(·) represents the adjoint matrix of the matrix;

[0045] According to the Hamilton-Cayley theorem, we get det(MRM H ) / tr(MRM H ) and det(MRM H ) / tr(adj(MRM H )) have the same monotonicity;

[0046] According to the constraints Combined with the relationship between the total channel energy and the user sub-channel energy, we get:

[0047]

[0048] The simplified non-convex optimization model expression for maximizing the user signal-to-noise ratio under single mode is as follows:

[0049]

[0050] st0≤M≤ε

[0051]

[0052] The design scheme of the reconfigurable antenna pattern based on zero-forcing precoding is calculated and obtained including:

[0053] Objective function f(M) = det(MRM H ) is a non-convex function under the sparse matrix independent variable M. The first-order Taylor expansion is used to approximate the non-convex function, and the expression is:

[0054]

[0055] The gradient of the objective function is:

[0056]

[0057] A related convex problem is constructed to solve the non-convex optimization model that maximizes the user signal-to-noise ratio under the simplified single mode. The specific expression of the related convex problem is as follows:

[0058]

[0059] st0≤M≤ε

[0060]

[0061] Where M i is the iteration point corresponding to the i-th iteration; the related convex problem is solved by the convex optimization toolbox; using the obtained result M * The direction in which the original objective function descends is M * -M i , the iterative point of the i+1th iteration is deduced as:

[0062] M i+1 =M i +d(M * -M i )

[0063] Wherein, the iterative step size d∈(0,1) is calculated by the backtracking straight line search method.

[0064] As a preferred solution, under the mainization-minimization MM algorithm, the step length of the iteration is calculated by the backtracking straight line search method, including:

[0065] The maximum allowable step length s is calculated as follows: max :

[0066] s max =min{1,min{-m i / △m i |△m i <0}}

[0067] Repeat for the maximum allowed step size s mac Multiply by the parameter β until:

[0068] 0≤M i+1 ≤ε, and All of them hold true, where α∈[0.01,0.1], β∈[0.3,0.8], M i+1 =M i +s·△M.

[0069] In a second aspect, a reconfigurable antenna pattern design system based on zero-forcing precoding is provided, comprising:

[0070] A non-convex optimization model building module is used to establish a non-convex optimization model that maximizes the user signal-to-noise ratio under a single mode by using the user receiving signal-to-noise ratio under zero-forcing precoding;

[0071] A convex optimization design module is used to solve the non-convex optimization model for maximizing the user signal-to-noise ratio under the single mode by using a continuous convex approximation SCA algorithm, and to iteratively solve the non-convex optimization model by using a first-order Taylor expansion to design related convex problems;

[0072] The non-convex optimization model simplification solution module is used to design a new proxy function using the mainization-minimization MM algorithm to simplify the solution process of the non-convex optimization model, and calculate the reconfigurable antenna radiation pattern design scheme based on zero-forcing precoding.

[0073] According to a third aspect, an electronic device is provided, including:

[0074] A memory storing at least one instruction; and a processor executing the instruction stored in the memory to implement the reconfigurable antenna pattern design method based on zero-forcing precoding.

[0075] In a fourth aspect, a computer-readable storage medium is provided, wherein at least one instruction is stored in the computer-readable storage medium, and the at least one instruction is executed by a processor in an electronic device to implement the reconfigurable antenna pattern design method based on zero-forcing precoding.

[0076] Compared with the prior art, the present invention has at least the following beneficial effects:

[0077] The reconfigurable antenna pattern design method based on zero-forcing precoding of the present invention is applicable to the base station end equipped with N t Antennas, the receiving end is K single-antenna users, the transmitting end reconstructs the channel through a single-mode directional pattern reconfigurable antenna array and eliminates interference between multiple users through zero-forcing precoding, and the scenario considered is the downlink of multi-user MIMO. The present invention uses the user receiving signal-to-noise ratio under zero-forcing precoding to establish a non-convex optimization model that maximizes the user signal-to-noise ratio under a single mode, and solves the non-convex optimization model that maximizes the user signal-to-noise ratio under a single mode through a continuous convex approximation SCA (Successive Convex Approximation) algorithm. The first-order Taylor expansion is used to design related convex problems to achieve iterative solution of the non-convex optimization model, and the original non-convex problem can be solved through a series of related convex problems. Taking into account the complexity of the function form in the above-mentioned solution process, the present invention uses the majorization-minimization (MM) algorithm to find a new proxy function, and then designs a simpler non-convex proxy function and the corresponding convex optimization problem. Under the same initial point conditions, it has similar complexity performance in a single iteration and fewer iterative convergence times. Therefore, the algorithm converges quickly and has low time complexity, which improves the efficiency of solving the single-mode design problem of the directional pattern reconfigurable system under zero-forcing precoding. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0079] Figure 1 The bit error rate of the method proposed in the embodiment of the present invention varies with the signal-to-noise ratio. QPSK modulation is adopted and the transmitting end is equipped with N t =4 antennas, the number of single-antenna users at the receiving end is K=4, and the number of multipaths is L=6.

[0080] Figure 2 The average sum rate of the method proposed in the embodiment of the present invention varies with the signal-to-noise ratio. QPSK modulation is adopted and the transmitting end is equipped with N t =4 antennas, the number of single-antenna users at the receiving end is K=4, and the number of multipaths is L=6.

[0081] Figure 3 The average sum rate of the method proposed in the embodiment of the present invention varies with the number of multipaths. QPSK modulation is adopted and the transmitting end is equipped with N t =8 antennas, the number of single-antenna users at the receiving end is K=8, and the signal-to-noise ratio at the transmitting end is SNR=5dB.

[0082] Figure 4 The average sum rate of the method proposed in the embodiment of the present invention varies with the number of users. QPSK modulation is adopted and the transmitting end is equipped with N t =8 antennas, the number of multipaths is L=12, and the signal-to-noise ratio at the transmitting end is SNR=5dB.

[0083] Figure 5 This is a graph showing how iteration-related parameters of the method proposed in an embodiment of the present invention vary with the number of iterations. DETAILED DESCRIPTION

[0084] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, ordinary technicians in this field can also obtain other embodiments without making creative work.

[0085] Example 1

[0086] The embodiment of the present invention proposes a method for designing a reconfigurable antenna pattern based on zero-forcing precoding, comprising:

[0087] Using the user received signal-to-noise ratio under zero-forcing precoding, a non-convex optimization model for maximizing the user signal-to-noise ratio under single mode is established.

[0088] The non-convex optimization model for maximizing the user signal-to-noise ratio under the single mode is solved by a continuous convex approximation SCA algorithm, and the first-order Taylor expansion is used to design related convex problems to achieve iterative solution of the non-convex optimization model;

[0089] A new proxy function is designed using the principalization-minimization MM algorithm to simplify the solution process of the non-convex optimization model, and the reconfigurable antenna pattern design scheme based on zero-forcing precoding is calculated.

[0090] The reconfigurable antenna pattern design method based on zero-forcing precoding in the embodiment of the present invention is applicable to the following occasions:

[0091] The base station is equipped with N t The receiver has K single-antenna users. The transmitter reconstructs the channel through a single-mode reconfigurable antenna array and eliminates interference between multiple users through zero-forcing precoding. The scenario considered is the downlink of multi-user MIMO.

[0092] In a possible implementation, the user signal-to-noise ratio under zero-forcing precoding is first considered. Without considering power allocation, the signal-to-noise ratios of all receiving users are the same, and the corresponding expression is:

[0093]

[0094] Where P is the total power at the transmitting end, σ 2 is the power of additive white Gaussian noise, H is the reconstructible channel of the single-mode directional pattern, which is specifically expressed as:

[0095] H=MH path

[0096] Where M represents the matrix set of all user pattern sampling vectors, H path is the set of all user physical subchannels, which are expressed as:

[0097]

[0098] and H path =[H 1 ,H 2 ,…,H K ] T , m k and H k are the directional pattern sampling vector and physical subchannel of the kth user respectively.

[0099] The single-mode design problem of maximizing the signal-to-noise ratio of the directional pattern reconfigurable system based on zero-forcing precoding can be transformed into:

[0100]

[0101] st0≤M≤ε

[0102]

[0103] in, is a semi-positive definite matrix, and ε is a real number representing the maximum gain of the reconfigurable directional pattern. In the equation, 0≤M≤ε represents that the gain of the reconfigurable pattern to the transmitted signal is greater than or equal to 0 and less than or equal to the threshold, which corresponds to the zero point of the reconfigurable pattern and the maximum gain of the main lobe, respectively. This means that after the introduction of the reconfigurable radiation pattern, the maximum channel gain of each user will not change, which ensures that the performance gain of the radiation pattern reconfigurable system comes from the different antenna radiation patterns that can be reconstructed, rather than introducing additional energy to enhance the channel gain.

[0104] Furthermore, considering the objective function g(M)=tr((MRM H ) -1 ) is a non-convex function under the sparse matrix independent variable M, so it can be solved using the SCA algorithm, and the first-order Taylor expansion of the objective function is used to approximate the original non-convex function. The specific form is:

[0105]

[0106] in, represents the gradient of the function g(·).

[0107] Specifically, the gradient of the original objective function is:

[0108]

[0109] Furthermore, the original non-convex problem It can be solved through a series of related convex problems. The specific form of constructing related convex problems is:

[0110]

[0111] st0≤M≤ε

[0112]

[0113] Where M i is the iteration point corresponding to the i-th iteration. As a convex problem, it can be solved directly using the standard convex optimization toolbox. * It can indicate the direction in which the original objective function decreases M * -M i .

[0114] Furthermore, it can be deduced that the iteration point of the i+1th iteration is:

[0115] M i+1 =M i +d(M * -M i )

[0116] The iteration step length d∈(0,1) can be calculated by backtracking straight line search method. Solution to the problem * They are all within the feasible domain, so the iteration point of the i+1th iteration must also satisfy the feasible domain.

[0117] In a possible implementation, considering the complexity of the above functional form, tr((MRM H ) -1 ) function can use the MM algorithm to design a new proxy function for solution.

[0118] Specifically, for the original objective function, using the properties of matrix inversion, it can be further rewritten as:

[0119]

[0120] Where adj(·) represents the adjoint matrix of the matrix.

[0121] Furthermore, according to the Hamilton-Cayley theorem, the adjoint matrix can be represented by the power of the original matrix and the trace of the original matrix. It has been proved that det(MRM H ) / tr(MRM H ) and det(MRM H ) / tr(adj(MRM H )) have the same monotonicity. Therefore, in the modal design scheme of the directional pattern reconfigurable system, det(MRM H ) / tr(MRM H ) can also be used as the objective function to maximize the user signal-to-noise ratio.

[0122] Specifically, the new proxy function det(MRM H ) / tr(MRM H ) includes a numerator and a denominator, where the numerator appears in the channel capacity of the MIMO system and the denominator represents the energy of the MIMO system channel.

[0123] Furthermore, considering the original optimization problem The constraints in represents the energy constraint of all user subchannels. Combining the relationship between the total channel energy and the user subchannel energy, it can be deduced that:

[0124]

[0125] In the feasible domain of the optimization problem, the above formula gives a lower bound of the surrogate function, thereby eliminating the adverse effect of the fractional form on the surrogate function. By introducing the above formula, the modal design problem based on this surrogate function can be further transformed into:

[0126]

[0127] st0≤M≤ε

[0128]

[0129] Further, the problem The objective function in f(M)=det(MRM H ) is a non-convex function under the sparse matrix independent variable M, and the first-order Taylor expansion can also be used to approximate the non-convex function. The specific form is:

[0130]

[0131] Specifically, the gradient of the original objective function is:

[0132]

[0133] Furthermore, the non-convex problem It can be solved through a series of related convex problems. The specific form of constructing related convex problems is:

[0134]

[0135] st0≤M≤ε

[0136]

[0137] Among them, M i is the iteration point corresponding to the i-th iteration. As a convex problem, it can be solved directly using the standard convex optimization toolbox. * It can indicate the direction in which the original objective function decreases M * -M i .

[0138] Furthermore, it can be deduced that the iteration point of the i+1th iteration is:

[0139] M i+1 =M i +d(M * -M i )

[0140] The iteration step length d∈(0,1) can be calculated by backtracking straight line search method. Solution to the problem * They are all within the feasible domain, so the iteration point of the i+1th iteration must also satisfy the feasible domain.

[0141] The present invention designs a low-complexity single-mode design scheme for a directional pattern reconfigurable system based on zero-forcing precoding. The proposed scheme has lower complexity and faster convergence speed than the symbol-level precoding joint design scheme proposed in the prior art. By utilizing the signal-to-noise ratio under zero-forcing precoding and the directional pattern reconfigurable channel model, the present invention proposes the optimization problem of the single-mode design scheme for the directional pattern reconfigurable system, and clarifies the constraints and the source of performance gain.

[0142] The present invention adopts the SCA algorithm to solve the simplified non-convex optimization problem, and constructs the related convex problem through the first-order Taylor expansion to obtain the iteration direction. The iterative algorithm can achieve convergence, verifies the feasibility of the single-mode design of the directional pattern reconfigurable system, and promotes the application of directional pattern reconfigurable antennas in more practical MIMO scenarios.

[0143] The low-complexity MM algorithm further proposed in the present invention designs a more concise proxy function form, and under the same initial point conditions, has similar complexity performance in a single iteration and fewer iterative convergence times.

[0144] Example 2

[0145] Taking the SCA algorithm as an example, the reconfigurable antenna pattern design method based on zero-forcing precoding of the present invention is described:

[0146] S1. The transmitter obtains channel state information and constructs the physical subchannel set H of all users based on the physical subchannel of each user. path =[H 1 ,H 2 ,…,H K ] T ,

[0147] S2, randomly determine the iteration initial point M 0 In the SCA algorithm, the initial point must be within the feasible region, that is, it must satisfy the relationship: 0≤M 0 ≤ε and

[0148] S3, initialize the current number of iterations to i = 0, set the maximum residual norm when the iteration exits to τ, and the maximum number of iterations i max , the linear backtracking parameters are α and β.

[0149] S4, enter the iteration and solve the corresponding convex optimization problem:

[0150]

[0151] st0≤M≤ε

[0152]

[0153] Get the corresponding results Further, calculate the search direction

[0154] S5. Based on the search direction of the previous step, use the linear backtracking method to calculate the step size.

[0155] First calculate the maximum allowed step length s = s max :

[0156] s max =min{1,min{-m i / △m i |△m i <0}}

[0157] Then keep giving s max Multiply by parameter β until 0≤M i+1 ≤ε, and All of them hold true, where α∈[0.01,0.1], β∈[0.3,0.8], M i+1 =M i +s·△M.

[0158] S6, update iteration point: M i+1 =M i +s·△M, i=i+1. If ‖M i -M i-1 ‖ F <τ or i>i max , the iteration exits, otherwise repeat steps S4, S5, and S6.

[0159] S7, after the iteration exits, according to the final result M * Calculate the corresponding channel H.

[0160] Example 3

[0161] If the MM algorithm is considered, the reconfigurable antenna pattern design method based on zero-forcing precoding of the present invention includes the following steps:

[0162] S1. The transmitter obtains channel state information and constructs the physical subchannel set H of all users based on the physical subchannel of each user. path =[H 1 ,H2 ,…,H K ] T ,

[0163] S2, randomly determine the iteration initial point M 0 In the MM algorithm, the initial point must be within the feasible region, that is, it must satisfy the relationship: 0≤M 0 ≤ε and

[0164] S3, initialize the current number of iterations to i = 0, set the maximum residual norm when the iteration exits to τ, and the maximum number of iterations i max , the linear backtracking parameters are α and β.

[0165] S4, enter the iteration and solve the corresponding convex optimization problem:

[0166]

[0167] st0≤M≤ε

[0168]

[0169] Get the corresponding results Further, calculate the search direction

[0170] S5. Based on the search direction of the previous step, use the linear backtracking method to calculate the step size.

[0171] First calculate the maximum allowed step length s = s max :

[0172] s max =min{1,min{-m i / △m i |△m i <0}}

[0173] Then keep giving s max Multiply by parameter β until 0≤M i+1 ≤ε, and All of them hold true, where α∈[0.01,0.1], β∈[0.3,0.8], M i+1 =M i +s·△M.

[0174] S6, update iteration point: M i+1 =M i +s·△M, i=i+1. If ‖M i -M i-1 ‖ F <τ or i>i max, the iteration exits, otherwise repeat steps S4, S5, and S6.

[0175] S7, after the iteration exits, according to the final result M * Calculate the corresponding channel H.

[0176] The following simulation experiment further illustrates the reconfigurable antenna pattern design method based on zero-forcing precoding in the embodiment of the present invention:

[0177] The Monte Carlo simulation method is used to carry out simulation experiments on the proposed scheme.

[0178] The test conditions are:

[0179] In the multi-user MIMO downlink, the transmitter is equipped with N t There are K transmitting antennas and the receiving end is K single-antenna users. Assuming that the transmitting end can obtain perfect channel state information, the receiving vector r can be expressed as:

[0180] r=HWs+n

[0181] In the formula, s is the modulated transmit vector, W is the precoding matrix of zero-forcing precoding, H is the reconfigurable channel matrix, and n is the noise vector. Specifically, it is assumed that the noise is additive white Gaussian noise, and each element of n has a mean of 0 and a variance of σ 2 Gaussian distribution.

[0182] Furthermore, the relevant parameters of the linear backtracking method in the proposed iterative algorithm are initialized as: α = 0.01, β = 0.5, τ = 10 -3 ,i max = 200. The above parameters are applicable to both the SCA algorithm and the MM algorithm.

[0183] The simulation results are as follows Figures 1 to 5 As shown in the figure, the simulation adds a physical channel, that is, the MIMO system with traditional zero-forcing precoding without considering the reconfigurable antenna pattern, which can be regarded as the baseline solution of the solution of the present invention. In addition, the simulation adds a theoretical upper limit, that is, a MIMO system with no interference between multiple users, which can be regarded as the optimal result that the MIMO system channel can achieve in theory.

[0184] See also Figure 1 and Figure 2 , Figure 1The bit error rate curves of different modal design schemes are simulated. First, the two schemes that introduce reconfigurable directional patterns have reduced the bit error rate of the communication system compared to the physical channel without the introduction of reconfigurable directional patterns, effectively improving the reliability of the communication system. In the directional pattern design scheme, since the proxy function form designed by the MM algorithm is more concise, the corresponding error when the first-order Taylor expansion is used to find the corresponding convex problem is smaller. Therefore, the performance gain of the MM algorithm proposed in this invention is slightly better than that of the SCA algorithm. Figure 2 The average and rate curves of different modal design schemes were simulated. Figure 4 The results show that the reconfigurable designs of the MM algorithm and the SCA algorithm are both feasible, and both can achieve performance gains close to the theoretical upper bound.

[0185] See also Figures 3 to 5 , Figure 3 The average and rate curves of the modal design scheme under different multipath numbers are simulated. It can be seen that the performance gain of the modal design scheme gradually approaches the theoretical upper limit as the number of multipaths increases, further verifying the performance advantage of the directional pattern reconfigurable system over the traditional MIMO system. Figure 4 The average and rate curves of the modal design schemes under different numbers of users were simulated. First, the interference of the traditional physical channel increases with the number of users, and the performance is seriously degraded. However, the introduction of a communication system with a reconfigurable directional pattern can change the channel correlation between users to reduce interference, thereby improving the effectiveness of the communication system. Figure 5 The changes of some important parameters in the iteration process of the two single-mode design schemes proposed in the present invention, including the objective function value and the iteration residual, are simulated. It can be seen that under the same random initial point, as the iteration proceeds, the objective function value and the iteration residual of the two algorithms gradually decrease until convergence, but under the same accuracy, the convergence times of the MM algorithm are significantly less than those of the SCA algorithm, indicating that the complexity of the MM algorithm is much lower than that of the SCA algorithm, which further verifies the feasibility of the proposed design scheme in practice.

[0186] Another embodiment of the present invention further provides a reconfigurable antenna pattern design system based on zero-forcing precoding, comprising:

[0187] A non-convex optimization model building module is used to establish a non-convex optimization model that maximizes the user signal-to-noise ratio under a single mode by using the user receiving signal-to-noise ratio under zero-forcing precoding;

[0188] A convex optimization design module is used to solve the non-convex optimization model for maximizing the user signal-to-noise ratio under the single mode by using a continuous convex approximation SCA algorithm, and to iteratively solve the non-convex optimization model by using a first-order Taylor expansion to design related convex problems;

[0189] The non-convex optimization model simplification solution module is used to design a new proxy function using the mainization-minimization MM algorithm to simplify the solution process of the non-convex optimization model, and calculate the reconfigurable antenna radiation pattern design scheme based on zero-forcing precoding.

[0190] Another embodiment of the present invention further provides an electronic device, characterized in that it includes:

[0191] A memory storing at least one instruction; and a processor executing the instruction stored in the memory to implement the reconfigurable antenna pattern design method based on zero-forcing precoding.

[0192] Another embodiment of the present invention further proposes a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the reconfigurable antenna pattern design method based on zero-forcing precoding.

[0193] Exemplarily, the instructions stored in the memory may be divided into one or more modules / units, and the one or more modules / units are stored in a computer-readable storage medium and executed by the processor to complete the reconfigurable antenna pattern design method based on zero-forcing precoding of the present invention. The one or more modules / units may be a series of computer-readable instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the server.

[0194] The electronic device may be a computing device such as a smart phone, a notebook, a PDA, and a cloud server. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the electronic device may also include more or fewer components, or a combination of certain components, or different components, for example, the electronic device may also include an input / output device, a network access device, a bus, etc.

[0195] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0196] The memory may be an internal storage unit of the server, such as a hard disk or memory of the server. The memory may also be an external storage device of the server, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the server. Furthermore, the memory may include both an internal storage unit of the server and an external storage device. The memory is used to store the computer-readable instructions and other programs and data required by the server. The memory may also be used to temporarily store data that has been output or is to be output.

[0197] It should be noted that the information interaction, execution process and other contents between the above-mentioned module units are based on the same concept as the method embodiment. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0198] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0199] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0200] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0201] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for designing a reconfigurable antenna pattern based on zero-forcing precoding, characterized in that: include: Using the user received signal-to-noise ratio under zero-forcing precoding, a non-convex optimization model for maximizing the user signal-to-noise ratio under single mode is established. The non-convex optimization model for maximizing the user signal-to-noise ratio under the single mode is solved by a continuous convex approximation SCA algorithm, and the first-order Taylor expansion is used to design related convex problems to achieve iterative solution of the non-convex optimization model; A new proxy function is designed using the principalization-minimization MM algorithm to simplify the solution process of the non-convex optimization model, and the reconfigurable antenna pattern design scheme based on zero-forcing precoding is calculated.

2. The method for designing a reconfigurable antenna pattern based on zero-forcing precoding according to claim 1, characterized in that: The user receiving signal-to-noise ratio under the zero-forcing precoding is the same for all receiving users without considering the power allocation. The calculation expression is as follows: Where P is the total power of the transmitter; σ 2 is the power of additive white Gaussian noise; N t is the number of antennas equipped at the base station; H is the reconfigurable channel of the single-mode directional pattern, and the calculation expression is: H=MH path Where M represents the matrix set of all user pattern sampling vectors; H path is the set of all user physical subchannels; H path =[H1,H2,…,H k ] T , m k and H k are the directional pattern sampling vector and physical subchannel of the kth user respectively.

3. The method for designing a reconfigurable antenna pattern based on zero-forcing precoding according to claim 2, characterized in that: The non-convex optimization model expression for maximizing the user signal-to-noise ratio under the single mode is as follows: st0≤M≤ε In the formula, is a positive semidefinite matrix; ε is a real number representing the maximum gain of the reconfigurable pattern; Represents the energy constraint of all user subchannels.

4. The method for designing a reconfigurable antenna pattern based on zero-forcing precoding according to claim 3, characterized in that: In the step of solving the non-convex optimization model for maximizing the user signal-to-noise ratio under the single mode by the continuous convex approximation SCA algorithm, the objective function is g(M)=tr((MRW H ) -1 ), is a non-convex function under the sparse matrix argument M; The objective function is approximated by a first-order Taylor expansion for a non-convex function, and the expression is: In the formula, represents the gradient of the function g(·); The gradient of the objective function is: The specific expression of the design-related convex problem is as follows: st0≤M≤ε Where M i is the iteration point corresponding to the i-th iteration; the related convex problem is solved by the convex optimization toolbox; using the obtained result M * The direction in which the original objective function descends is M * -M i , the iterative point of the i+1th iteration is deduced as: M i+1 =M i +d(M * -M i ) Wherein, the iterative step size d∈(0,1) is calculated by the backtracking straight line search method.

5. The method for designing a reconfigurable antenna pattern based on zero-forcing precoding according to claim 4, characterized in that: Under the continuous convex approximation SCA algorithm, the step size of the iteration is calculated by the backtracking straight line search method including: The maximum allowable step length s is calculated as follows: max : s max =min{1,min{-m i / △m i |△m i <0}} Repeat for the maximum allowed step size s max Multiply by the parameter β until: and All of them hold true, where α∈[0.01,0.1], β∈[0.3,0.8], M i+1 =M i +s·△M.

6. The method for designing a reconfigurable antenna pattern based on zero-forcing precoding according to claim 3, characterized in that: The process of designing a new surrogate function by utilizing the principalization-minimization MM algorithm to simplify the solution of the non-convex optimization model includes: For the original objective function, using the properties of matrix inversion, it can be rewritten as follows: Where adj(·) represents the adjoint matrix of the matrix; According to the Hamilton-Cayley theorem, we get det(MRM H ) / tr(MRM H ) and det(MRM H ) / tr(adj(MRM H )) have the same monotonicity; According to the constraints Combined with the relationship between the total channel energy and the user sub-channel energy, we get: The simplified non-convex optimization model expression for maximizing the user signal-to-noise ratio under single mode is as follows: st0≤M≤ε The design scheme of the reconfigurable antenna pattern based on zero-forcing precoding is calculated and obtained including: Objective function f(M) = det(MRM H ) is a non-convex function under the sparse matrix independent variable M. The first-order Taylor expansion is used to approximate the non-convex function, and the expression is: The gradient of the objective function is: A related convex problem is constructed to solve the non-convex optimization model that maximizes the user signal-to-noise ratio under the simplified single mode. The specific expression of the related convex problem is as follows: st0≤M≤ε Where M i is the iteration point corresponding to the i-th iteration; the related convex problem is solved by the convex optimization toolbox; using the obtained result M * The direction in which the original objective function descends is M * -M i , the iterative point of the i+1th iteration is deduced as: M i+1 =M i +d(M * -M i ) Wherein, the iterative step size d∈(0,1) is calculated by the backtracking straight line search method.

7. The method for designing a reconfigurable antenna pattern based on zero-forcing precoding according to claim 6, characterized in that: Under the mainization-minimization MM algorithm, the step size of the iteration is calculated by the backtracking straight line search method including: The maximum allowable step length s is calculated as follows: max : s max =min{1,min{-m i / △m i |△m i <0}} Repeat for the maximum allowed step size s max Multiply by the parameter β until: 0≤M i+1 ≤ε, and All of them hold true, where α∈[0.01,0.1], β∈[0.3,0.8], M i+1 =M i +s·△M.

8. A reconfigurable antenna pattern design system based on zero-forcing precoding, characterized in that: include: A non-convex optimization model building module is used to establish a non-convex optimization model that maximizes the user signal-to-noise ratio under a single mode by using the user receiving signal-to-noise ratio under zero-forcing precoding; A convex optimization design module is used to solve the non-convex optimization model for maximizing the user signal-to-noise ratio under the single mode by using a continuous convex approximation SCA algorithm, and to iteratively solve the non-convex optimization model by using a first-order Taylor expansion to design related convex problems; The non-convex optimization model simplification solution module is used to design a new proxy function using the mainization-minimization MM algorithm to simplify the solution process of the non-convex optimization model, and calculate the reconfigurable antenna radiation pattern design scheme based on zero-forcing precoding.

9. An electronic device, characterized in that: include: A memory storing at least one instruction; and A processor executes instructions stored in the memory to implement the reconfigurable antenna pattern design method based on zero forcing precoding as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the reconfigurable antenna pattern design method based on zero-forcing precoding as described in any one of claims 1 to 7.

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