Intelligent reflecting surface rate optimization method based on intelligent reflecting surface selection

Abstract

The invention provides an intelligent reflecting surface rate optimization method based on intelligent reflecting surface selection. The method comprises the following steps: performing parameter initialization on an intelligent reflector IRS auxiliary communication system to obtain channel state information, establishing an optimization problem aiming at maximizing a receiving signal-to-noise ratio of a user IRS reflection link, and decomposing the optimization problem into two sub-problems of IRS selection and joint beam forming for solving; according to the channel state information, the IRS information and the user information, an IRS unit selection algorithm is used for selection and distribution, and user and IRS selection relation information is obtained; and grouping the users and the IRS according to the selection relationship between the users and the IRS to form a plurality of subsystems, and carrying out joint beam forming on each subsystem by using SDR according to the channel state information to obtain a joint beam forming matrix of the base station and the IRS. According to the invention, the calculation complexity of the system can be reduced, the downlink transmission rate is improved, and the transmission rate is improved by about 50% compared with the IRS-free auxiliary system; compared with a random phase IRS, the transmission rate is improved by about 30%.

Description

Technical Field

[0001] The present invention relates to the field of computer application technology, and in particular to a smart reflective surface rate optimization method based on smart reflective surface selection. Background Art

[0002] Intelligent reflecting surface (IRS) technology is one of the important technologies for the next generation of wireless communication systems. Once proposed, it has attracted widespread attention in the industry. IRS is composed of a large number of low-cost passive components, each of which can independently adjust the amplitude and phase of the reflected signal, thereby reshaping the wireless electromagnetic environment between the base station and the user. By combining IRS with millimeter wave, massive multiple-input multiple-output and other technologies, the energy efficiency and spectrum efficiency of the system can be greatly improved.

[0003] Compared with the traditional relay mode, the IRS-assisted communication system has many advantages. IRS can adjust the phase shift of the reflected signal according to system requirements, and does not require additional RF link assistance, which can effectively reduce the energy consumption of the system and improve the communication performance of the system. When there is a line-of-sight link between the base station and the user, by jointly optimizing the beamforming at the base station and the phase shift at the IRS, the two transmission links can be coherently superimposed to optimize the system transmission performance. IRS-assisted communication systems have been widely studied in terms of maximizing energy efficiency, maximizing data rate, maximizing confidentiality rate, maximizing received power and minimizing transmit power. There are existing schemes that use semi-positive relaxation algorithms and alternating optimization algorithms to jointly optimize the system to minimize the system's transmission energy consumption. There are also schemes that analyze the theoretical lower limit of the power of the IRS-assisted system and compare it with the results of the alternating optimization algorithm. There are also schemes that use different algorithms to optimize the system. There are also schemes that use successive convex approximation algorithms and semi-positive relaxation algorithms to jointly optimize hybrid beamforming and the phase shift at the IRS to maximize the user weighted sum rate. Another solution proposes an alternating optimization algorithm for antenna selection and phase shift design, which can effectively reduce the computational complexity of the system when the number of antennas and reflector units is large.

[0004] With the development of communication networks, the number of users in the network continues to increase; at the same time, users will use more reflection units to obtain better transmission performance. With the increase in the number of users and reflection units, the channel information that the system needs to obtain increases, and the amount of calculation required to obtain the optimal phase shift at the IRS increases significantly, greatly increasing the operating cost of the system. In response to the above problems, the current mainstream solution is to apply grouping technology to classify and merge users and IRS units to reduce the computational complexity of the system. Since this solution reduces the number of IRS units, it reduces the degree of freedom of the system to a certain extent, causing the system performance to deteriorate.

[0005] At present, some scholars have studied the grouping of IRS-assisted communication systems in non-orthogonal multiple access systems and orthogonal multiple access systems. This technology divides the original system into several subsystems, and IRS uses the subsystem as the smallest unit to serve multiple users at the same time. In non-orthogonal multiple access systems, there is a scheme to study the IRS-assisted single-carrier non-orthogonal multiple access system, deploying an IRS to serve two user groups. The fixed power allocation strategy only optimizes the phase shift matrix, and the system performance cannot be maximized. There is also a scheme to study the competitive clustering algorithm of multi-user systems assisted by multiple IRSs, which divides users into different clusters. Through the selection of different IRSs by each cluster, the scale of the system is reduced, so that the IRS and users are matched with a smaller number. The grouping of the above literature is mainly based on classification aggregation. By treating multiple users as a whole and jointly performing phase shift design to reduce the complexity of the system, it is still insufficient in the flexibility of system design and cannot be applied to conventional IRS communication systems.

[0006] In orthogonal multiple access systems, user grouping is more based on the frequency selective fading of the channel. One scheme introduces the working principle of non-orthogonal multiple access systems, and another scheme studies the IRS grouping problem of frequency division multiple access systems. This grouping scheme groups adjacent IRS units into a group so that they have the same reflection coefficient and serve a specific frequency according to the frequency selective fading of the channel. By reducing the number of IRS units that need to be calculated, the computational complexity of the system is reduced. Another scheme studies the IRS grouping problem of multi-user systems. In systems with a large number of service users and a limited number of reflection units, the unit grouping of this strategy is better than dedicated unit allocation, but it still does not achieve direct free selection between users and IRSs, and cannot maximize system performance. Summary of the invention

[0007] The embodiment of the present invention provides an intelligent reflection surface rate optimization method based on intelligent reflection surface selection, so as to effectively improve the downlink transmission rate of the IRS auxiliary communication system.

[0008] In order to achieve the above object, the present invention adopts the following technical scheme.

[0009] A smart reflector rate optimization method based on smart reflector selection, comprising:

[0010] Initialize the parameters of the intelligent reflector IRS auxiliary communication system to obtain channel status information;

[0011] An optimization problem is established according to the channel state information with the goal of maximizing the received signal-to-noise ratio of the user IRS reflection link, and the optimization problem is decomposed into two sub-problems of IRS selection and joint beamforming for solution;

[0012] Use the IRS unit selection algorithm to select and allocate according to the channel state information, IRS information and user information, and obtain the user and IRS selection relationship information;

[0013] The users and IRSs are grouped according to their selection relationship to form multiple subsystems. The semi-positive definite relaxation algorithm SDR is used to perform joint beamforming on each subsystem according to the channel state information to obtain the joint beamforming matrix of the base station and IRS.

[0014] Preferably, the step of initializing parameters of the IRS auxiliary communication system to obtain channel state information includes:

[0015] The IRS-assisted communication system is set to consist of a base station with M antennas, a large-scale IRS with N reflection units, and K users. The system uses K subchannels and each user independently occupies a subchannel, and the subchannel bandwidth is B.

[0016] The transmission path of the downlink signal from the base station to the user in the system is divided into a direct path and a path reflected by the IRS. represents the channel from the base station to the user side. The reflection path consists of two parts. One part is the path from the base station to the IRS. represents the channel from the base station to the IRS side, and the other part is the path from the IRS to the user. represents the channel from IRS to the user side. The signal is reflected in the IRS unit. Let Θ represent the phase shift matrix at the IRS. Where β∈[0,1], θ∈[0,2π], so the signal received by user k is expressed as:

[0017]

[0018] in represents the additive Gaussian white noise at the user receiving end, with a mean of 0 and a variance of s k represents the information of user k sent by the base station, satisfying E[|s k | 2 ]=1;w k represents the normalized beamforming vector of user k, X k represents the work unit selection matrix of user k, X k =diag(x k,i ), i∈(1,N), where the selected x k,i =1, otherwise x k,i =0;

[0019] Assume that the reflection of the selected working unit has no attenuation, that is, |β sel |=1; the unselected working unit has no reflection, that is, |β non|=0, for user k, its channel transmission matrix is ​​recorded as The optimal IRS working unit for user k is the maximum signal-to-noise ratio unit of the non-line-of-sight link. The received signal-to-noise ratio at user k is:

[0020]

[0021] The bandwidth of user k is B, and the information transmission rate at user k is:

[0022]

[0023] Preferably, the optimization problem is established according to the channel state information with the goal of maximizing the received signal-to-noise ratio of the user IRS reflection link, and the optimization problem is decomposed into two sub-problems of IRS unit selection and joint beamforming for solution, including:

[0024] An optimization problem is established based on the channel state information with the goal of maximizing the received signal-to-noise ratio of the user IRS reflection link. The expression of the optimization problem is:

[0025]

[0026] in represents the total throughput of the system; C1 and C2 represent the phase shift and amplitude constraints of the signal at the IRS respectively; C3 is the received signal-to-noise ratio constraint at the IRS, SINR RIS represents the received signal-to-noise ratio of the user through the IRS reflection path; C4 is the transmission power constraint, w0 2 is the maximum transmit power at the base station; C5-C7 represent the working unit selection constraints of the IRS, each IRS unit can only work for one user, and each user has at least one IRS unit working for it;

[0027] The optimization problem shown in equation (4) is decomposed into two sub-problems: IRS unit selection and joint beamforming.

[0028] Preferably, the selecting and allocating using an IRS unit selection algorithm according to channel state information, IRS information and user information to obtain user and IRS selection relationship information includes:

[0029] Initialize the IRS unit set selected by each user to be an empty set, and the set selected by the kth user to be N k , each user matches the IRS unit with the best channel condition. If multiple users select the same unit, the unit is selected by the user with the best channel condition, and other users repeat this step;

[0030] The method for selecting the user with the best channel condition is: setting the minimum signal-to-noise ratio of the user receiving the non-line-of-sight path, calculating the difference between the non-line-of-sight path receiving signal-to-noise ratio of each user and the minimum signal-to-noise ratio, and determining the user with the largest signal-to-noise ratio difference as the user with the best channel condition.

[0031] Preferably, the method of grouping users and IRSs according to the selection relationship between users and IRSs to form multiple subsystems, and performing joint beamforming on each subsystem using a semi-positive definite relaxation algorithm SDR according to channel state information to obtain a joint beamforming matrix of the base station and the IRS includes:

[0032] Assuming that the reflected signal at the IRS has no attenuation, that is, β = 1, the joint beamforming subproblem is simplified to:

[0033]

[0034] K is the number of users, k is user k, B is the channel bandwidth, h r is the reflection channel, θ is the phase shift at the reflection unit, X k The matrix G is selected for the reflection unit as the channel from the base station to the IRS, h d is the direct channel, w k is the beamforming matrix at the base station, for noise;

[0035] For any given θ, the maximum ratio transmission method is used to find the optimal beamforming matrix of the system, that is, Where P represents the transmission power at the base station, the optimization problem (5) is transformed into:

[0036]

[0037] When the normalized beamforming vector takes the maximum value, the system information transmission rate takes the maximum value, that is, when ||w k || 2 =w0 2 When the transmission power P = w0 2 , transform the optimization problem (6) into:

[0038]

[0039] Problem (7) is transformed into the information transmission rate of user k. The information transmission rates of different users are summed. When the internal function, i.e., the received signal-to-noise ratio of user k, reaches the maximum value, the original formula reaches the maximum value, and the optimization problem (7) is transformed into:

[0040]

[0041] make in The original phase shift constraint C1 is transformed into a complex plane unit circle constraint, that is make in Convert Equation 8 to:

[0042]

[0043] Expand the above formula to:

[0044]

[0045] Introduce auxiliary variable t, t2 =1, and the SDR algorithm is used to transform formula (10) into:

[0046]

[0047] in

[0048] is a scalar, treated as a 1×1 matrix, whose trace is equal to itself, make Satisfying rank(Γ)=1 and being a semi-positive definite matrix, that is, Γ≥0, equation (11) is transformed into:

[0049]

[0050] The SDR algorithm is used to solve problem (12) through the CVX toolkit to obtain the optimal solution of the system, which includes: IRS selection matrix, base station beamforming matrix, phase shift matrix at IRS and maximized system performance through matrix design.

[0051] It can be seen from the technical solutions provided by the above-mentioned embodiments of the present invention that the method of the embodiments of the present invention can greatly reduce the computational complexity of the system and improve the downlink transmission rate. Compared with the system without IRS assistance, the transmission rate is increased by about 50%; compared with the random phase IRS, the transmission rate is increased by about 30%.

[0052] Additional aspects and advantages of the present invention will be given in part in the following description, which will become obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0054] Figure 1A model diagram of an IRS auxiliary communication system provided by an embodiment of the present invention;

[0055] Figure 2 A processing flow chart of a smart reflector rate optimization method based on smart reflector selection provided by an embodiment of the present invention;

[0056] Figure 3 A flow chart of an IRS unit selection algorithm provided by an embodiment of the present invention;

[0057] Figure 4 A schematic diagram of a curve showing a change in system throughput versus station transmit power for different parameters provided by an embodiment of the present invention;

[0058] Figure 5 A schematic diagram of comparing the receiving rates of each user and the system throughput provided by an embodiment of the present invention.

[0059] Figure 6 A schematic diagram of system throughput comparison with different numbers of reflection units provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0061] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0062] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0063] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0064] Aiming at the problem that the computational complexity in IRS-assisted multi-user systems increases rapidly with the number of users, the present invention proposes for the first time a reflection unit selection algorithm that maximizes the total system signal-to-noise ratio under the premise of ensuring the minimum received signal-to-noise ratio of users. Under the constraints of transmission power, phase shift, and reflection unit selection, the problem of maximizing the system transmission rate is established by jointly optimizing the base station beamforming, reflection unit selection, and phase shift. Since the selection of the reflection unit is highly coupled to the phase shift setting problem, and both the selection matrix and the phase shift matrix are high-dimensional matrices, the problem of maximizing the total system transmission rate is a non-convex problem and cannot be solved directly.

[0065] The present invention decomposes the original optimization problem into two sub-problems for solution. First, a reflection unit selection algorithm of IRS based on the received signal-to-noise ratio is proposed to select the IRS unit used by the user. By selecting the reflection unit with the best channel condition, it can be effectively guaranteed that the transmission rate of the system will not drop significantly after the user selects the reflection unit. Then the base station beamforming matrix and the IRS phase shift matrix are jointly optimized, and the problem is still a non-convex problem. The problem is converted into a convex problem using a semi-positive definite relaxation algorithm for solution. Due to the selection of the IRS reflection unit, the number of reflection units used by a single user decreases, and the computational complexity of the system is greatly reduced. The simulation results show that the algorithm proposed in the present invention can effectively select the reflection unit according to the channel quality, and can effectively improve the transmission rate of the system compared with random phase and no IRS assistance. The reliability and effectiveness of the proposed theory and algorithm are verified.

[0066] An IRS auxiliary communication system model provided by an embodiment of the present invention is shown in FIG. Figure 1As shown, the IRS-assisted multi-user system model consists of a base station with M antennas, a large-scale IRS with N reflection units, and K users. Assume that in this model, the system adopts orthogonal frequency division multiple access technology, uses a total of K sub-channels and each user independently occupies a sub-channel, and the sub-channel bandwidth is B. In order to optimize the algorithm design, it is assumed that the base station can obtain the ideal downlink channel information through the existing channel estimation method. The signal will produce single or multiple reflections after the IRS, and the multiple reflections will cause double fading effect and large power loss. The present invention ignores the influence of multiple reflections and only considers the signal after a single reflection. The system selects the corresponding IRS unit for a specific user according to the channel conditions. Figure 1 Different colors are used to distinguish the IRS units and reflected signals corresponding to different users.

[0067] The processing flow chart of a processing flow chart of a smart reflector rate optimization method based on smart reflector selection provided by an embodiment of the present invention is as follows: Figure 2 As shown, the process includes the following steps:

[0068] Step S10: Initialize the parameters of the IRS auxiliary communication system, obtain channel state information and output it.

[0069] Step S20: establishing an optimization problem with the goal of maximizing the received signal-to-noise ratio of the user IRS reflection link according to the channel state information, and decomposing the optimization problem into two sub-problems of IRS selection and joint beamforming for solution.

[0070] Step S30: Use the IRS unit selection algorithm to select and allocate according to the channel state information, IRS information and user information, and obtain user and IRS selection relationship information.

[0071] Step S40: Group users and IRSs according to the selection relationship between users and IRSs to form multiple subsystems, and perform joint beamforming on each subsystem using SDR (semidefinite relaxation) according to channel state information to obtain a joint beamforming matrix of the base station and the IRS. Joint beamforming is performed on multiple subsystems.

[0072] Specifically, the above step S10 includes: Figure 1 As shown in the figure, the transmission path of the downlink signal from the base station to the user in the system can be divided into a direct path and a path reflected by the IRS. represents the channel from the base station to the user side. The reflection path consists of two parts, one of which is the path from the base station to the IRS. represents the channel from the base station to the IRS side, and the other part is the path from the IRS to the user. represents the channel from IRS to the user side. The signal is reflected in the IRS unit, let Θ represent the phase shift matrix at the IRS, Where β∈[0,1], θ∈[0,2π]. Therefore, the signal received by user k can be expressed as:

[0073]

[0074] in represents the additive Gaussian white noise at the user receiving end, with a mean of 0 and a variance of s k represents the information of user k sent by the base station, satisfying E[|s k | 2 ]=1;w k represents the normalized beamforming vector of user k. k represents the work unit selection matrix of user k, X k =diag(x k,i ), i∈(1,N), where the selected x k,i =1, otherwise x k,i =0.

[0075] In the actual working process, the reflection channels of different IRS units are affected by the spatial position and the surrounding environment, and there are certain performance differences in the reflection channels for different users. In the present invention, according to the specific channel conditions of different users, the working IRS units serving them are selected in real time. The present invention assumes that the reflection of the selected working unit has no attenuation, that is, |β sel |=1; the unselected working unit has no reflection, that is, |β non |=0. For user k, its channel transmission matrix is ​​recorded as The optimal IRS working unit for user k is the unit with the maximum signal-to-noise ratio of the non-line-of-sight link. The received signal-to-noise ratio at user k is:

[0076]

[0077] The bandwidth of user k is B, and the information transmission rate at user k is:

[0078]

[0079] Specifically, the above step S20 includes: in order to maximize the transmission rate of the system and effectively reduce the system complexity when there are multiple users, the base station transmit signal energy is jointly optimized, and the optimization problem with the goal of maximizing the user IRS reflection link receiving signal-to-noise ratio can be modeled as follows:

[0080]

[0081] in represents the total throughput of the system; (C1) and (C2) represent the phase shift and amplitude constraints of the signal at the IRS, respectively; (C3) is the received signal-to-noise ratio constraint at the IRS, SINR RIS represents the received signal-to-noise ratio of the user through the IRS reflection path; (C4) is the transmission power constraint, w0 2 is the maximum transmission power at the base station; (C5-C7) represents the working unit selection constraints of the IRS. Each IRS unit can only work for one user, and each user has at least one IRS unit working for it.

[0082] Optimization problem decomposition and algorithm design: In the original optimization problem, the reflection unit selection matrix and the reflection unit phase shift matrix are coupled with each other, and the user transmission rate is nonlinearly related to the user received signal-to-noise ratio, which cannot be optimized. Therefore, the original optimization problem is decomposed into two sub-problems: reflection unit selection and joint beamforming, and the reflection unit selection and phase shift are decoupled. Assuming that the system is set with the optimal phase shift, only the reflection unit selection is considered, and the user information transmission rate is maximized while ensuring the user received signal-to-noise ratio; then the subsystem is generated according to the reflection unit selection. At this time, the problem is a quadratically constrained quadratic program (QCQP) problem. The semi-positive relaxation algorithm is used for joint beamforming optimization to obtain the approximate solution of the system.

[0083] Specifically, the above step S30 includes: the present invention proposes a low-complexity multi-user IRS reflection unit selection algorithm, which performs IRS reflection unit selection based on user rate requirements and channel conditions.

[0084] In existing research on maximizing the system transmission rate, IRS can only serve a single user at a time, does not consider the correlation between different users, and cannot dynamically allocate IRS reflection units. At the same time, due to the different channel qualities of different users, the user information transmission rate varies greatly, resulting in a waste of system communication performance; as the number of users and reflection units increases, the computational complexity of the system increases significantly. Figure 3 A flow chart of an IRS unit selection algorithm provided by an embodiment of the present invention. The present invention proposes a low-complexity reflection unit selection algorithm that maximizes the user rate, and maximizes the user IRS reflection link reception signal-to-noise ratio to select the reflection unit under the constraint of ensuring the minimum reception signal-to-noise ratio of each user. The specific algorithm is as follows:

[0085] First, initialize the IRS unit set selected by each user to an empty set, and the set selected by the kth user to N k; Each user is matched with the IRS unit with the best channel condition. If multiple users select the same unit, this unit is selected by the user with the best channel condition, and other users repeat this step; Set the minimum signal-to-noise ratio of the user receiving the non-line-of-sight path; Calculate and compare the difference between the user's non-line-of-sight path receiving signal-to-noise ratio and the minimum signal-to-noise ratio, and give priority to selecting the IRS unit for the user with the largest signal-to-noise ratio difference, until all users reach the minimum receiving signal-to-noise ratio; Select the IRS unit with the best channel condition from the remaining units.

[0086] Specifically, the above step S40 includes:

[0087] Joint optimization algorithm based on SDR algorithm: Assuming that the reflected signal at the IRS has no attenuation, that is, β = 1, the joint beamforming subproblem can be simplified as follows:

[0088]

[0089] For any given θ, the optimal beamforming matrix of the system can be obtained using the maximum-radio transmission (MRT) method, that is, Where P represents the transmission power at the base station. The optimization problem (5) can be transformed into:

[0090]

[0091] When the normalized beamforming vector reaches its maximum value, the system information transmission rate reaches its maximum value. k || 2 =w0 2 When the transmission power P = w0 2 The optimization problem (6) can be transformed into:

[0092]

[0093] At this time, there is only variable Θ in the optimization problem, and different users correspond to ΘX k are independent of each other, so problem (7) can be transformed into finding the information transmission rate of user k, and then summing the information transmission rates of different users; and because the external function is a convex function, when the internal function, that is, the received signal-to-noise ratio of user k, takes the maximum value, the original formula takes the maximum value, and the optimization problem (7) can be transformed into:

[0094]

[0095] make in The original phase shift constraint (C1) can be transformed into a complex plane unit circle constraint, namely make in Formula (8) can be transformed into:

[0096]

[0097] Expand the above formula to:

[0098]

[0099] The optimization problem is a non-convex quadratic constrained quadratic programming problem, which is difficult to solve directly. The SDR algorithm can be used for optimization. Introducing auxiliary variables t, t 2 =1, transforming formula (10) into:

[0100]

[0101] in

[0102] because is a scalar, which can be regarded as a 1×1 matrix, whose trace is equal to itself. Since the properties of the trace satisfy the commutative law, make It satisfies rank(Γ)=1 and is a semi-positive definite matrix, that is, Γ≥0. The original formula can be written as:

[0103]

[0104] The optimization problem is converted into a semi-definite programming (SDP) problem, which can be solved by the CVX toolkit to find the optimal solution of the system. The optimal solution includes: IRS selection matrix, base station beamforming matrix, phase shift matrix at IRS, and maximized system performance through matrix design.

[0105] The algorithm complexity mainly consists of two parts: the IRS selection algorithm, solving the SDP problem through the CVX toolkit, and the Gaussian randomization process. The complexity of the IRS selection algorithm for user k is The number of variables in the SDP problem is (N k +1) 2 , the interior point method requires iterations, and in each iteration the result needs to be obtained times of calculation, the algorithm complexity is The complexity of eigenvalue decomposition during Gaussian randomization is

[0106] The computational complexity of user K in the algorithm of the present invention is Where N k The average value of is (N / K). When the RIS reflection unit is not selected, the computational complexity is Therefore, the algorithm of the present invention can significantly reduce the computational complexity of the system.

[0107] The scheme of the present invention is simulated, D represents the distance between the base station and the IRS, users are randomly distributed in a circular area centered on the IRS, and R represents the radius of the circular area. The parameter settings are shown in Table 1.

[0108] Table 1 Related parameters

[0109]

[0110]

[0111] like Figure 3 As shown in the figure, the system throughput varies with the base station transmission power when the number of users and reflection units is different. As the base station transmission increases, the system throughput also increases. Figure 3 It can be seen that when the number of users is constant, as the number of reflection units N increases, the system throughput gradually increases, and the system computational complexity also increases accordingly; when the number of reflection units is constant, as the number of users K increases, the system throughput increases significantly, and the system computational complexity decreases.

[0112] Figure 4 A schematic diagram of a curve showing a change in system throughput versus station transmit power for different parameters provided by an embodiment of the present invention, such as Figure 4 As shown in the figure, the comparison of the receiving rate of each user under different parameters is different from the total system throughput. Different colors represent the transmission rate of different users in the system. Figure 4 is the sum of the transmission rates of the same group of elements (system throughput), from Figure 4 It can be seen from the figure that the rate of each user is greater than that of the random phase and non-IRS assisted system; Figure 4 It can be observed that the algorithm of the present invention is significantly better than the random phase IRS and the system without IRS assistance. The performance is improved by about 50% compared with the system without IRS assistance and by about 30% compared with the random phase IRS.

[0113] Figure 5 A schematic diagram of a comparison of the receiving rates of each user and the system throughput provided by an embodiment of the present invention. Figure 5 As shown in the figure, the system throughput changes with the number of reflector units. The system transmit power is fixed at 100w. As can be seen from the figure, the system throughput increases with the increase in the number of reflector units. The three throughputs are from large to small for the number of users K=10, K=8, and K=6. As the number of users in the system increases, the number of optional channels increases, the quality of the selected channels is better, the system throughput increases, and the computational complexity of the system tends to decrease.

[0114] Figure 6A schematic diagram of system throughput comparison for different numbers of reflection units provided in an embodiment of the present invention. The present invention is aimed at an IRS-assisted multi-user wireless communication system, studies the IRS rate maximization problem based on the selection of multi-user reflection units, and proposes an algorithm for jointly optimizing the IRS unit selection, base station beamforming and reflection phase shift at the IRS to maximize the system downlink rate. Simulation results show that the algorithm of the present invention can greatly improve the downlink transmission rate. Compared with the system without IRS assistance, the transmission rate is increased by about 50%; compared with the random phase IRS, the transmission rate is increased by about 30%. From mathematical analysis, it can be seen that the algorithm of the present invention can effectively reduce the system calculation complexity in large-scale IRS application scenarios with multiple users. In future research, the impact of various communication systems such as non-orthogonal frequency division multiple access systems on the selection of IRS reflection units will be considered.

[0115] In summary, the implementation method of the present invention can significantly reduce the computational complexity of the system and improve the downlink transmission rate. Compared with the system without IRS assistance, the transmission rate is increased by about 50%; compared with the random phase IRS, the transmission rate is increased by about 30%.

[0116] The present invention realizes for the first time the selection of users and reflection units based on channel state information, matches the user with the reflection unit with the best channel quality under the current channel environment, and uses a semi-positive relaxation algorithm to optimize the joint beamforming of the reflection unit and the base station. By using this algorithm, the computational complexity of the system can be greatly reduced, while the downlink transmission rate can be improved.

[0117] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0118] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0119] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0120] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A smart reflector rate optimization method based on smart reflector selection, characterized in that: include: Initialize the parameters of the intelligent reflector IRS auxiliary communication system to obtain channel status information; An optimization problem is established according to the channel state information with the goal of maximizing the received signal-to-noise ratio of the user IRS reflection link, and the optimization problem is decomposed into two sub-problems of IRS unit selection and joint beamforming for solution; Use the IRS unit selection algorithm to select and allocate according to the channel state information, IRS information and user information, and obtain the user and IRS selection relationship information; The users and IRS are grouped according to their selection relationship to form multiple subsystems. The semi-positive definite relaxation algorithm SDR is used to perform joint beamforming on each subsystem according to the channel state information to obtain the joint beamforming matrix of the base station and IRS.

2. The method according to claim 1, characterized in that: The step of initializing parameters of the IRS auxiliary communication system to obtain channel state information includes: The IRS-assisted communication system is set to consist of a base station with M antennas, a large-scale IRS with N reflection units, and K users. The system uses K subchannels and each user independently occupies a subchannel, and the subchannel bandwidth is B. The transmission path of the downlink signal from the base station to the user in the system is divided into a direct path and a path reflected by the IRS. represents the channel from the base station to the user side. The reflection path consists of two parts. One part is the path from the base station to the IRS. represents the channel from the base station to the IRS side, and the other part is the path from the IRS to the user. represents the channel from IRS to the user side. The signal is reflected in the IRS unit. Let Θ represent the phase shift matrix at the IRS. Where β∈[0,1], θ∈[0,2π], so the signal received by user k is expressed as: in represents the additive Gaussian white noise at the user receiving end, with a mean of 0 and a variance of s k represents the information of user k sent by the base station, satisfying E[|s k | 2 ]=1;w k represents the normalized beamforming vector of user k, X k represents the work unit selection matrix of user k, X k =diag(x k,i ), i∈(1,N) , where the selected x k,i =1, otherwise x k,i =0; Assume that the reflection of the selected working unit has no attenuation, that is, |β sel |=1; the unselected working unit has no reflection, that is, |β non |=0, for user k, its channel transmission matrix is ​​recorded as The optimal IRS working unit for user k is the maximum signal-to-noise ratio unit of the non-line-of-sight link. The received signal-to-noise ratio at user k is: The bandwidth of user k is B, and the information transmission rate at user k is:

3. The method according to claim 2, characterized in that The optimization problem is established according to the channel state information with the goal of maximizing the received signal-to-noise ratio of the user IRS reflection link, and the optimization problem is decomposed into two sub-problems of IRS unit selection and joint beamforming for solving, including: An optimization problem is established based on the channel state information with the goal of maximizing the received signal-to-noise ratio of the user IRS reflection link. The expression of the optimization problem is: in represents the total throughput of the system; C1 and C2 represent the phase shift and amplitude constraints of the signal at the IRS respectively; C3 is the received signal-to-noise ratio constraint at the IRS, SINR RIS represents the received signal-to-noise ratio of the user through the IRS reflection path; C4 is the transmission power constraint, w0 2 is the maximum transmit power at the base station; C5-C7 represent the working unit selection constraints of the IRS, each IRS unit can only work for one user, and each user has at least one IRS unit working for it; The optimization problem shown in equation (4) is decomposed into two sub-problems: IRS unit selection and joint beamforming.

4. The method according to claim 3, characterized in that The method of selecting and allocating using an IRS unit selection algorithm according to channel state information, IRS information and user information to obtain user and IRS selection relationship information includes: Initialize the IRS unit set selected by each user to be an empty set, and the set selected by the kth user to be N k , each user matches the IRS unit with the best channel condition. If multiple users select the same unit, the unit is selected by the user with the best channel condition, and other users repeat this step; The method for selecting the user with the best channel condition is: setting the minimum signal-to-noise ratio of the user receiving the non-line-of-sight path, calculating the difference between the non-line-of-sight path receiving signal-to-noise ratio of each user and the minimum signal-to-noise ratio, and determining the user with the largest signal-to-noise ratio difference as the user with the best channel condition.

5. The method according to claim 4, characterized in that The method of grouping users and IRSs according to the selection relationship between users and IRSs to form multiple subsystems, and performing joint beamforming on each subsystem using a semi-positive definite relaxation algorithm SDR according to channel state information to obtain a joint beamforming matrix of a base station and an IRS includes: Assuming that the reflected signal at the IRS has no attenuation, that is, β = 1, the joint beamforming subproblem is simplified to: K is the number of users, k is user k, B is the channel bandwidth, h r is the reflection channel, θ is the phase shift at the reflection unit, X k The matrix G is selected for the reflection unit as the channel from the base station to the IRS, h d is the direct channel, w k is the beamforming matrix at the base station, for noise; For any given θ, the maximum ratio transmission method is used to find the optimal beamforming matrix of the system, that is, Where P represents the transmission power at the base station, the optimization problem (5) is transformed into: When the normalized beamforming vector takes the maximum value, the system information transmission rate takes the maximum value, that is, when ||w k || 2 =w0 2 When the transmission power P = w0 2 , transform the optimization problem (6) into: Problem (7) is transformed into the information transmission rate of user k. The information transmission rates of different users are summed. When the internal function, i.e., the received signal-to-noise ratio of user k, reaches the maximum value, the original formula reaches the maximum value, and the optimization problem (7) is transformed into: make in The original phase shift constraint C1 is transformed into a complex plane unit circle constraint, that is make in Convert Equation 8 to: Expand the above formula to: Introduce auxiliary variable t, t2 =1, and the SDR algorithm is used to transform formula (10) into: in is a scalar, treated as a 1×1 matrix, whose trace is equal to itself, make Satisfying rank(Γ)=1 and being a semi-positive definite matrix, that is, Γ≥0, equation (11) is transformed into: The SDR algorithm is used to solve problem (12) through the CVX toolkit to obtain the optimal solution of the system, which includes: IRS selection matrix, base station beamforming matrix, phase shift matrix at IRS and maximized system performance through matrix design.

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