Cell-free communication system, rate optimization method, device, equipment, medium and product

Abstract

The invention provides a cell-free communication system, a rate optimization method, a rate optimization device, rate optimization equipment, a medium and a product. The method comprises the following steps: constructing a channel model of a cell-free communication system based on block RIS assistance; solving a sum rate optimization problem in the channel model to obtain an optimal base station beam matrix, an optimal reflection phase shift coefficient matrix of the block RIS, and an optimal block matrix representing a distribution relation between the block RIS and a reflection element; and determining the maximum sum rate of the cell-free communication system based on the optimal base station beam matrix, the optimal reflection phase shift coefficient matrix and the optimal block matrix. According to the cell-free communication system and the rate optimization method provided by the invention, the active beam forming at the base station side, the passive beam forming at the RIS side and the block matrix design are jointly optimized, and the system performance is jointly improved and the sum rate of multi-user access is guaranteed by taking the system sum rate maximization as the target.

Description

Technical Field

[0001] The present invention relates to the field of wireless communications, and in particular to a cell-free communication system and a rate optimization method, device, equipment, medium and product. Background Art

[0002] With the advent of the 6G era, in order to meet the needs of massive data transmission and high-speed services of terminal devices, cell-free communication technology introduces the idea of ​​"user-centricity", suppresses interference between multiple cells through joint collaboration of multiple base stations, and improves system performance. Although cell-free communication technology has significant advantages, large-scale base stations bring huge power consumption and deployment costs. As one of the core technologies of 6G, Reconfigurable Intelligent Surface (RIS) has received widespread attention due to its advantages such as low cost and low power consumption. Therefore, introducing RIS into the cell-free communication system to improve system performance has received widespread attention.

[0003] Since RIS needs to precisely adjust a large number of reflection elements to optimize signal transmission, the complexity of its hardware and control algorithms increases the difficulty of calculation and scheduling of the system, resulting in insufficient real-time performance, which in turn affects the rate of the network. In addition, the lack of control flexibility makes it impossible for the system to quickly adapt to user needs and channel changes in a dynamic environment, further limiting the transmission efficiency and rate improvement of the system. Therefore, the current RIS-assisted cell-free communication system has problems such as high complexity and insufficient control flexibility, which directly affects the rate performance of the RIS-assisted cell-free communication system. Summary of the invention

[0004] The present invention provides a cell-free communication system and a rate optimization method, device, equipment, medium and product, which are used to solve the problems of high complexity and insufficient control flexibility of the cell-free communication system assisted by RIS in the prior art.

[0005] In a first aspect, the present invention provides a cell-free communication system and a rate optimization method, comprising: Construct a channel model for a cell-free communication system assisted by a block intelligent reflector RIS; Solving the sum rate optimization problem in the channel model, obtaining an optimal base station beam matrix, an optimal reflection phase shift coefficient matrix of the block RIS, and an optimal block matrix representing the distribution relationship between the block RIS and the reflection elements; Based on the optimal base station beam matrix, the optimal reflection phase shift coefficient matrix and the optimal blocking matrix, a maximum sum rate of the cell-free communication system is determined.

[0006] In one embodiment, solving the sum rate optimization problem in the channel model to obtain an optimal base station beam matrix, an optimal reflection phase shift coefficient matrix of a block intelligent reflection surface RIS, and an optimal block matrix representing the allocation relationship between the block RIS and the reflection elements includes: The sum rate optimization problem in the channel model is decomposed into a first optimization sub-problem, a second optimization sub-problem and a third optimization sub-problem; the first optimization sub-problem is used to optimize the base station beam matrix under the condition of a given reflection phase shift coefficient matrix and a block matrix to maximize the sum rate; the second optimization sub-problem is used to optimize the reflection phase shift coefficient matrix under the condition of a given base station beam matrix and a block matrix to maximize the sum rate; the third optimization sub-problem is used to optimize the block matrix under the condition of a given base station beam matrix and a reflection phase shift coefficient matrix to maximize the sum rate; Solving the first optimization sub-problem based on a preset reflection phase shift coefficient matrix and a preset block matrix to obtain an optimal base station beam matrix; Solving the second optimization sub-problem based on the optimal base station beam matrix and the preset block matrix to obtain an optimal reflection phase shift coefficient matrix; Based on the optimal base station beam matrix and the optimal reflection phase shift coefficient matrix, the third optimization sub-problem is solved to obtain an optimal block matrix.

[0007] In one embodiment, solving the first optimization sub-problem based on a preset reflection phase shift coefficient matrix and a preset block matrix to obtain an optimal base station beam matrix includes: Acquire a first channel matrix, a second channel matrix, a third channel matrix, a preset reflection phase shift coefficient matrix and a preset block matrix in the cell-free communication system; the first channel matrix represents a channel from a wireless access point to a user equipment; the second channel matrix represents a channel from a wireless access point to a block RIS; the third channel matrix represents a channel from a block RIS to a user equipment; Determine the first channel matrix, the second channel matrix, the third channel matrix, the preset reflection phase shift coefficient matrix and the preset block matrix as first input data of the channel model; Under the first constraint condition, a fractional programming algorithm is used to solve the first optimization subproblem according to the first input data to obtain an optimal base station beam matrix; the first constraint condition is used to constrain power transmission on the base station side.

[0008] In one embodiment, solving the second optimization sub-problem based on the optimal base station beam matrix and the preset block matrix to obtain an optimal reflection phase shift coefficient matrix includes: Determine the first channel matrix, the second channel matrix, the third channel matrix, the optimal base station beam matrix and the preset block matrix as second input data of the channel model; Under the second constraint condition, the second optimization subproblem is solved according to the second input data using a continuous convex approximation algorithm to obtain an optimal reflection phase shift coefficient matrix; the second constraint condition is used to constrain the phase shift coefficient of the RIS reflection element.

[0009] In one embodiment, solving the third optimization sub-problem based on the optimal base station beam matrix and the optimal reflection phase shift coefficient matrix to obtain the optimal block matrix includes: Determine the first channel matrix, the second channel matrix, the third channel matrix, the optimal base station beam matrix and the optimal reflection phase shift coefficient matrix as third input data of the channel model; Under the conditions of satisfying the third, fourth and fifth constraints, the swap-matching algorithm is used to solve the third optimization subproblem according to the third input data to obtain the optimal block matrix; the third constraint is used to constrain the number of reflection elements in each block RIS; the fourth constraint is used to constrain each reflection element to be allocated to only one block RIS; the fifth constraint is used to constrain the binary variables related to the pairing of block RIS and reflection elements.

[0010] In one embodiment, the maximum sum rate is determined by the following formula: .

[0011] in, Indicates l A base station beam vector of a wireless access point to the kth user equipment; represents the reflection phase shift coefficient vector of the rth RIS; represents the allocation relationship between the block RIS and the reflection element in the rth RIS; represents the signal-to-noise ratio at the kth user equipment.

[0012] In a second aspect, the present invention further provides a cell-free communication system and a rate optimization device, comprising: A model building module, used to build a channel model of a cell-free communication system assisted by a block intelligent reflector RIS; An optimization problem solving module is used to solve the sum rate optimization problem in the channel model to obtain an optimal base station beam matrix, an optimal reflection phase shift coefficient matrix of the block RIS, and an optimal block matrix representing the distribution relationship between the block RIS and the reflection elements; The maximum sum rate module is used to determine the maximum sum rate of the cell-free communication system based on the optimal base station beam matrix, the optimal reflection phase shift coefficient matrix and the optimal block matrix.

[0013] In a third aspect, the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the cell-free communication system and rate optimization method as described above are implemented.

[0014] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the cell-free communication system and rate optimization method as described in any one of the above are implemented.

[0015] In a fifth aspect, the present invention also provides a computer program product, comprising a computer program, which can be stored on a non-transitory computer-readable storage medium, and when the computer program is executed by the processor, implements the steps of any of the above-mentioned cell-free communication systems and rate optimization methods.

[0016] The cell-free communication system and rate optimization method, device, equipment, medium and product provided by the present invention construct a channel model of a downlink cell-free communication system assisted by block RIS. Based on the model, an optimization method is adopted to solve the sum rate optimization problem, and an optimal base station beam matrix, an optimal reflection phase shift coefficient matrix and an optimal block matrix are obtained, and the sum rate is further maximized. By jointly optimizing active beamforming on the base station side, passive beamforming on the RIS side and block matrix design, the system sum rate is maximized as the goal, the system performance is jointly improved, and the sum rate of multi-user access is guaranteed, thereby reducing the complexity of the RIS-assisted cell-free communication system and improving the control flexibility of the cell-free communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of a cell-free communication system and a rate optimization method provided by the present invention.

[0019] Figure 2 It is a schematic diagram of a model of a cell-free communication system based on block RIS assistance provided by the present invention.

[0020] Figure 3 It is a structural schematic diagram of a cell-free communication system and a rate optimization device provided by the present invention.

[0021] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] The terms "first", "second", etc. in the present invention are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention can be implemented in an order other than those illustrated or described herein.

[0024] Combine the following Figure 1-Figure 4 The present invention describes a cell-free communication system and a rate optimization method, device, equipment, medium and product.

[0025] It should be noted that the cell-free communication system and rate optimization method provided in the embodiment of the present invention are implemented based on the cell-free communication system and rate optimization device. The cell-free communication system and rate optimization method provided by the present invention construct a channel model of a downlink cell-free communication system assisted by block RIS, and based on the model, by jointly optimizing the active beamforming on the base station side, the passive beamforming on the RIS side and the block matrix design, the system and rate are maximized under the constraints of the base station power, RIS phase shift and block matrix, and the system performance is jointly improved. In response to this problem, a method based on a low-complexity alternating optimization framework is proposed to solve the optimization target, decouple the optimization target, and solve the base station side beamforming, RIS side passive beamforming and block matrix respectively through distributed planning, continuous convex approximation and exchange matching methods, so as to maximize the system and rate.

[0026] The embodiment of the present invention describes the no-cell communication system and the rate optimization method by taking the no-cell communication system and the rate optimization device in the no-cell communication system and the rate optimization device as the execution subjects as an example.

[0027] Combination Figure 1-Figure 2 , Figure 1 is a flow chart of a cell-free communication system and a rate optimization method provided by the present invention, Figure 2 It is a schematic diagram of a model of a cell-free communication system based on block RIS assistance provided by the present invention.

[0028] like Figure 1 As shown, the method includes the following: Step 101, constructing a channel model of a cell-free communication system assisted by a block intelligent reflector RIS; Step 102: Solve the sum rate optimization problem in the channel model to obtain an optimal base station beam matrix, an optimal reflection phase shift coefficient matrix of the block RIS, and an optimal block matrix representing the allocation relationship between the block RIS and the reflection elements; Step 103: Determine the maximum sum rate of the cell-free communication system based on the optimal base station beam matrix, the optimal reflection phase shift coefficient matrix and the optimal block matrix.

[0029] Specifically, Figure 2 As shown, Figure 2 The present invention shows a downlink cell-free communication system based on block RIS assistance, which uses multiple distributed wireless access points (APs) and block RISs to simultaneously serve user equipment (UE) for communication. There are L APs, K UEs and R block RISs in the cell-free communication system. Assume that the AP is configured with M antennas, the UE is configured with a single antenna, and each block RIS has N reflection elements. Define the set , , , , respectively represent the reflection element set, AP set, UE set and RIS set of the block RIS. Thus, a channel model of a cell-free communication system based on the assistance of block RIS can be constructed.

[0030] Before describing the block RIS assisted scenario, the traditional RIS assisted scenario is described first, and then the block RIS assisted scenario is inferred. For the traditional RIS assisted scenario, the downlink channel includes the direct channel from AP to UE and the cascade channel from AP-RIS-UE. The direct channel is the propagation path of the signal from the transmitter to the receiver directly without passing through the intermediate device; the cascade channel is the path where the signal is processed by the intermediate device (such as RIS) and then transmitted to the receiver, which is usually used for signal enhancement, optimization or coverage expansion. Indicates AP to Direct connection channel for UEs, Indicates AP to RIS channels, Indicates RIS to UE channels, Indicates The reflection coefficient matrix of the RIS is, therefore, AP to The equivalent channel of a UE can be expressed as: .

[0031] Based on formula (1), we consider the trade-off between complexity and performance and study the scenario of block RIS. RIS are divided into blocks, each containing reflection elements, and share the same reflection phase shift coefficient Therefore, it corresponds to The first The RIS reflection phase shift coefficient vector can be expressed as To characterize the distribution relationship between block RIS and reflection elements, a binary matrix is ​​defined ,in Indicates The first of the RIS The reflection elements are assigned to The equivalent channel matrix between RIS-UE is ,in , Indicates The first of the RIS reflection element to the The channel coefficient of each UE, Indicates AP to The first of the RIS The channel vector of reflection elements is Channel No. 1. AP to The equivalent channel of a UE can be expressed as: .

[0032] Assume that the base station AP sends The symbol of a UE is , No. AP to The beam vector of a UE is , so the The transmitted signal of an AP is expressed as: .

[0033] From the above analysis, we can see that The received signal at a UE can be expressed as: ; Here, σ is the standard deviation of the noise.

[0034] It can be deduced from formula (4) that The SINR at a UE can be expressed as: .

[0035] Based on the channel model of the cell-free communication system, the following cell-free communication system and rate maximization problem can be constructed. The system performance can be improved by jointly optimizing the base station side beamforming, RIS side beamforming and block matrix design, as shown below: ; ; ; ; ; ; Among them, (6a) is the objective function of the sum rate optimization problem; (6b) is the first constraint condition, which is used to constrain the power transmission at the base station side; (6c) is the second constraint condition, which is used to constrain the phase shift coefficient of the RIS reflection element; (6d) is the third constraint condition, which is used to constrain the third constraint condition. Each block in RIS The number of reflected elements in is ; (6e) is the fourth constraint, which is used to constrain each reflection element in RIS to be assigned to only one block (6f) is the fifth constraint, which is used to constrain the block in RIS Binary variables that are correlated when paired with reflective elements.

[0036] By solving the sum rate optimization problem in the channel model , the optimal base station beam matrix, the optimal reflection phase shift coefficient matrix of the block RIS, and the optimal block matrix representing the allocation relationship between the block RIS and the reflection elements are obtained.

[0037] After solving the optimal base station beam matrix, the optimal reflection phase shift coefficient matrix and the optimal block matrix, they are used as known data to calculate through formula (6a) to obtain the maximum sum rate of the cell-free communication system, thereby maximizing the sum rate of the cell-free communication system.

[0038] The present invention provides a cell-free communication system and rate optimization method, constructs a channel model of a downlink cell-free communication system assisted by a block RIS, and based on the model, adopts an optimization method to solve the sum rate optimization problem, obtains an optimal base station beam matrix, an optimal reflection phase shift coefficient matrix and an optimal block matrix, further maximizes the sum rate, and realizes the goal of maximizing the system sum rate by jointly optimizing active beamforming on the base station side, passive beamforming on the RIS side and block matrix design, jointly improving system performance, and ensuring the sum rate of multi-user access, thereby reducing the complexity of the RIS-assisted cell-free communication system and improving the control flexibility of the cell-free communication system.

[0039] In some embodiments, based on step 102, the sum rate optimization problem in the channel model is solved to obtain an optimal base station beam matrix, an optimal reflection phase shift coefficient matrix of a block smart reflection surface RIS, and an optimal block matrix characterizing the allocation relationship between the block RIS and the reflection element, including: The sum rate optimization problem in the channel model is decomposed into a first optimization sub-problem, a second optimization sub-problem and a third optimization sub-problem; the first optimization sub-problem is used to optimize the base station beam matrix under the condition of a given reflection phase shift coefficient matrix and a block matrix to maximize the sum rate; the second optimization sub-problem is used to optimize the reflection phase shift coefficient matrix under the condition of a given base station beam matrix and a block matrix to maximize the sum rate; the third optimization sub-problem is used to optimize the block matrix under the condition of a given base station beam matrix and a reflection phase shift coefficient matrix to maximize the sum rate; Solving the first optimization sub-problem based on a preset reflection phase shift coefficient matrix and a preset block matrix to obtain an optimal base station beam matrix; Solving the second optimization sub-problem based on the optimal base station beam matrix and the preset block matrix to obtain an optimal reflection phase shift coefficient matrix; Based on the optimal base station beam matrix and the optimal reflection phase shift coefficient matrix, the third optimization sub-problem is solved to obtain an optimal block matrix.

[0040] It should be noted that the coupling between multiple constraints will make it impossible to directly deal with the problem. , is an NP-hard non-convex problem. To solve the optimization problem , a low-complexity iterative solution algorithm is proposed to obtain a suboptimal solution to the sum rate maximization problem. First, the original problem The objective function is decomposed into multiple optimization sub-problems, namely, base station side beamforming design, RIS side beamforming design and block matrix design. Then, the optimization sub-problems are solved by fractional programming (FP), successive convex approximation (SCA) and swap-matching algorithms. Then, multiple optimization sub-problems are solved in an iterative form based on the alternating optimization algorithm.

[0041] Specifically, the sum rate optimization problem in the channel model is decomposed into a first optimization sub-problem, a second optimization sub-problem and a third optimization sub-problem, wherein the first optimization sub-problem is used to optimize the base station beam matrix under the condition of a given reflection phase shift coefficient matrix and a block matrix, so as to realize the base station side beamforming design to maximize the sum rate; the second optimization sub-problem is used to optimize the reflection phase shift coefficient matrix under the condition of a given base station beam matrix and a block matrix, so as to realize the RIS side beamforming design to maximize the sum rate; the third optimization sub-problem is used to optimize the block matrix under the condition of a given base station beam matrix and a reflection phase shift coefficient matrix, so as to realize the block matrix design to maximize the sum rate; After decomposing the three optimization sub-problems, the first optimization sub-problem is solved based on the preset reflection phase shift coefficient matrix and the preset block matrix to obtain the optimal base station beam matrix. Furthermore, the second optimization sub-problem is solved based on the optimal base station beam matrix and the preset block matrix to obtain the optimal reflection phase shift coefficient matrix. Finally, the third optimization sub-problem is solved based on the optimal base station beam matrix and the optimal reflection phase shift coefficient matrix to obtain the optimal block matrix.

[0042] The embodiment of the present invention decomposes the sum rate optimization problem into multiple optimization sub-problems, which can simplify the problem and decouple multiple constraints. Further, through a step-by-step solution approach, it not only simplifies the complex optimization problem but also gradually approaches the global optimal solution, thereby maximizing the sum rate of the system and improving communication performance.

[0043] According to the above content, solving the first optimization sub-problem based on the preset reflection phase shift coefficient matrix and the preset block matrix to obtain the optimal base station beam matrix includes: Acquire a first channel matrix, a second channel matrix, a third channel matrix, a preset reflection phase shift coefficient matrix and a preset block matrix in the cell-free communication system; the first channel matrix represents a channel from a wireless access point to a user equipment; the second channel matrix represents a channel from a wireless access point to a block RIS; the third channel matrix represents a channel from a block RIS to a user equipment; Determine the first channel matrix, the second channel matrix, the third channel matrix, the preset reflection phase shift coefficient matrix and the preset block matrix as first input data of the channel model; Under the first constraint condition, a fractional programming algorithm is used to solve the first optimization subproblem according to the first input data to obtain an optimal base station beam matrix; the first constraint condition is used to constrain power transmission on the base station side.

[0044] Specifically, given the RIS side beamforming , block matrix Under the premise, the problem can be converted to: ; .

[0045] Obviously, constraint (7c) is a convex constraint, so the optimization objective is converted into a convex problem. Here, the FP method is used for conversion. First, the auxiliary variable is introduced. , according to the Lagrange dual transformation, the sum rate maximization problem can be equivalently expressed as: .

[0046] Pair of top pairs By taking partial derivatives and setting them to 0, we can get the maximum for: .

[0047] It can be seen that formula (8) is still a non-convex problem, so a new auxiliary variable is introduced on this basis Transforming the fractional part in formula (8) yields: .

[0048] Abandon and The irrelevant terms can be obtained as follows: For formula (11) Find the partial derivative and set it to 0 to get the maximum for: Here, to further simplify the representation, we define , and define , based on the above definition formula (11) can be further converted to: definition , , , , Therefore, the optimization problem can be transformed into the following form: ; .

[0049] Note that the above problem is a QCQP problem and can be solved directly using the CVX toolbox.

[0050] Before solving the first optimization sub-problem, it is necessary to obtain the direct connection channel from AP to UE in the cell-free communication system. , the channel from AP to block RIS , block RIS to user equipment channel , respectively recorded as the first channel matrix, the second channel matrix, and the third channel matrix. At the same time, it is also necessary to obtain a preset reflection phase shift coefficient matrix and a preset block matrix. These matrices are used as the first input data of the model to solve the first optimization sub-problem.

[0051] The base station beamforming algorithm based on distributed planning solves the first optimization sub-problem: Input: Channel matrix , RIS preset reflection phase shift coefficient matrix and preset block matrix ; Cyclic process (sum rate not converged): a. Update according to formula (9) ; b. Update according to formula (12) ; c. According to the current update , And the first input data, by solving the convex optimization problem (14), update the base station beam matrix ; d. Iterate the above steps until the sum rate converges; Output: Base station beam matrix .

[0052] The embodiment of the present invention uses a fractional programming algorithm to solve the first optimization subproblem while satisfying the first constraint condition, combined with a preset reflection phase shift coefficient matrix, a preset block matrix, a first channel matrix, a second channel matrix, and a third channel matrix, to achieve decoupling of the constraint conditions, greatly reduce the complexity of the solution algorithm, and can gradually approach the global optimal solution, thereby maximizing the system sum rate and improving communication performance.

[0053] According to the above content, solving the second optimization sub-problem based on the optimal base station beam matrix and the preset block matrix to obtain the optimal reflection phase shift coefficient matrix includes: Determine the first channel matrix, the second channel matrix, the third channel matrix, the optimal base station beam matrix and the preset block matrix as second input data of the channel model; Under the second constraint condition, the second optimization subproblem is solved according to the second input data using a continuous convex approximation algorithm to obtain an optimal reflection phase shift coefficient matrix; the second constraint condition is used to constrain the phase shift coefficient of the RIS reflection element.

[0054] Specifically, at a given base station side beamforming , block matrix Under the premise, the problem can be converted to: According to the above similar FP method, the objective function can be transformed into the following form: Similarly, the optimal for: definition , Further, some equations in formula (16) are transformed into equivalent equations as follows: definition . Substituting it into formula (16), it can be transformed into: Therefore, the objective function can be equivalently transformed into: in, Therefore, the optimization problem can be transformed into the following form: The SCA algorithm is used to solve the problem and the objective function is transformed into the following form: The first-order Taylor expansion of the objective function (23) is as follows: in Indicates the RIS phase of the last iteration.

[0055] Before solving the second optimization subproblem, it is necessary to obtain the first channel matrix , the second channel matrix , the third channel matrix At the same time, it is also necessary to obtain the optimal base station beam matrix solved by the previous iteration and the pre-set preset block matrix. These matrices are used as the second input data of the model to solve the second optimization sub-problem.

[0056] The RIS beamforming algorithm based on continuous convex approximation solves the second optimization subproblem: Input: Channel matrix , RIS's optimal base station beam matrix and preset block matrix ; Cyclic process (sum rate not converged): a. Update according to formula (9) ; b. Update according to formula (17) ; c. According to the current update , And the second input data, by solving the convex optimization problem (23), update the reflection phase shift coefficient matrix of the block RIS ; d. Iterate the above steps until the sum rate converges; Output: Reflection phase shift coefficient matrix of block RIS .

[0057] The embodiment of the present invention uses a continuous convex approximation algorithm to solve the second optimization subproblem while satisfying the second constraint condition, combining the optimal base station beam matrix, the preset block matrix, the first channel matrix, the second channel matrix, and the third channel matrix, thereby realizing the decoupling of the constraint conditions, greatly reducing the complexity of the solution algorithm, and being able to gradually approach the global optimal solution, thereby maximizing the system sum rate and improving communication performance.

[0058] According to the above content, solving the third optimization sub-problem based on the optimal base station beam matrix and the optimal reflection phase shift coefficient matrix to obtain the optimal block matrix includes: Determine the first channel matrix, the second channel matrix, the third channel matrix, the optimal base station beam matrix and the optimal reflection phase shift coefficient matrix as third input data of the channel model; Under the conditions of satisfying the third, fourth and fifth constraints, the swap-matching algorithm is used to solve the third optimization subproblem according to the third input data to obtain the optimal block matrix; the third constraint is used to constrain the number of reflection elements in each block RIS; the fourth constraint is used to constrain each reflection element to be allocated to only one block RIS; the fifth constraint is used to constrain the binary variables related to the pairing of block RIS and reflection elements.

[0059] Specifically, at a given base station side beamforming , RIS side beamforming Under the premise, the problem can be converted to: You can see the above problems This is an integer programming problem, which can be modeled as a many-to-one matching problem. Consider using the swap-matching algorithm to solve it. and reflective elements The matching relationship between them is as follows: Definition 1 (matching relationship): Among them, 1) represents each reflection element Can only belong to one block , and must be assigned to a unique block 2) indicates each block The number of reflection elements in cannot exceed , ensuring that each block Will not exceed its capacity limit; 3) represents each reflection element With chunking The relationship between them is one-to-one.

[0060] In order to solve the many-to-one matching problem, the following definition 2 is proposed.

[0061] Definition 2 (Exchange operation): In the given matching relationship and In the exchange operation, the second elements of the two matching pairs are swapped to obtain a new matching pair, denoted as .

[0062] How to judge whether the exchange operation in Definition 2 needs to be performed? The following Definition 3 is defined.

[0063] Definition 3: (Exchange-blocking pair) For a given matching relationship and , if the following conditions are met, and Constitute a swap-blocking pair: ; in, Represents a reflective element or number of blocks The index of It represents the weighted sum rate (WSR) under the number of reflection elements or blocks. In Definition 3, 1) it ensures that the exchange will not reduce the transmission rate of the user terminal, avoiding negative impact; 2) it ensures that the transmission rate of at least one user device is improved after the exchange matching, so that the exchange is improved.

[0064] Before solving the third optimization sub-problem, it is necessary to obtain the first channel matrix , the second channel matrix , the third channel matrix At the same time, it is also necessary to obtain the optimal base station beam matrix and the optimal reflection phase shift coefficient matrix solved by the previous iteration. These matrices are used as the third input data of the model to solve the third optimization sub-problem.

[0065] Based on the block matrix algorithm based on exchange matching, solve the third optimization subproblem: Input: Channel matrix , optimal base station beam matrix and the optimal base station beam matrix of RIS ; Cyclic process (and rate not converged): a. For , , proceed to the next step; b. ,in is an open position and does the following: i. If is a swap-blocking pair and satisfies the conditions in Definition 3, then the swap operation is performed ii. Jump out of the current loop until there is no exchange blocking pair in the current match. Convert to segment matrix ; c. Iterate the above steps until the sum rate converges; Output: Block matrix .

[0066] The embodiment of the present invention uses a swap-matching algorithm to solve the third optimization subproblem while satisfying the third, fourth and fifth constraints, combined with the optimal base station beam matrix, the optimal reflection phase shift coefficient matrix, the first channel matrix, the second channel matrix and the third channel matrix, to achieve decoupling of the constraints, greatly reduce the complexity of the solution algorithm, and gradually approach the global optimal solution, thereby maximizing the system sum rate and improving communication performance.

[0067] The non-cell communication system and rate optimization device provided by the present invention are described below. The non-cell communication system and rate optimization device described below and the non-cell communication system and rate optimization method described above can be referred to each other.

[0068] Reference Figure 3 , Figure 3 It is a structural schematic diagram of a cell-free communication system and a rate optimization device provided by the present invention.

[0069] The cell-free communication system and rate optimization device include: The model building module 310 is used to build a channel model of a cell-free communication system assisted by a block intelligent reflector (RIS).

[0070] The optimization problem solving module 320 is used to solve the sum rate optimization problem in the channel model to obtain the optimal base station beam matrix, the optimal reflection phase shift coefficient matrix of the block RIS, and the optimal block matrix representing the allocation relationship between the block RIS and the reflection elements.

[0071] The maximum sum rate module 330 is used to determine the maximum sum rate of the cell-free communication system based on the optimal base station beam matrix, the optimal reflection phase shift coefficient matrix and the optimal blocking matrix.

[0072] The cell-free communication system and rate optimization device provided by the present invention construct a channel model of a downlink cell-free communication system assisted by a block RIS. Based on the model, an optimization method is adopted to solve the sum rate optimization problem, and an optimal base station beam matrix, an optimal reflection phase shift coefficient matrix and an optimal block matrix are obtained, and the sum rate is further maximized. By jointly optimizing active beamforming on the base station side, passive beamforming on the RIS side and block matrix design, the system sum rate is maximized, the system performance is jointly improved, and the sum rate of multi-user access is guaranteed, thereby reducing the complexity of the RIS-assisted cell-free communication system and improving the control flexibility of the cell-free communication system.

[0073] Furthermore, the optimization problem solving module 320 is also used for: The sum rate optimization problem in the channel model is decomposed into a first optimization sub-problem, a second optimization sub-problem and a third optimization sub-problem; the first optimization sub-problem is used to optimize the base station beam matrix under the condition of a given reflection phase shift coefficient matrix and a block matrix to maximize the sum rate; the second optimization sub-problem is used to optimize the reflection phase shift coefficient matrix under the condition of a given base station beam matrix and a block matrix to maximize the sum rate; the third optimization sub-problem is used to optimize the block matrix under the condition of a given base station beam matrix and a reflection phase shift coefficient matrix to maximize the sum rate; Solving the first optimization sub-problem based on a preset reflection phase shift coefficient matrix and a preset block matrix to obtain an optimal base station beam matrix; Solving the second optimization sub-problem based on the optimal base station beam matrix and the preset block matrix to obtain an optimal reflection phase shift coefficient matrix; Based on the optimal base station beam matrix and the optimal reflection phase shift coefficient matrix, the third optimization sub-problem is solved to obtain an optimal block matrix.

[0074] Furthermore, the optimization problem solving module 320 is also used for: Acquire a first channel matrix, a second channel matrix, a third channel matrix, a preset reflection phase shift coefficient matrix and a preset block matrix in the cell-free communication system; the first channel matrix represents a channel from a wireless access point to a user equipment; the second channel matrix represents a channel from a wireless access point to a block RIS; the third channel matrix represents a channel from a block RIS to a user equipment; Determine the first channel matrix, the second channel matrix, the third channel matrix, the preset reflection phase shift coefficient matrix and the preset block matrix as first input data of the channel model; Under the first constraint condition, a fractional programming algorithm is used to solve the first optimization subproblem according to the first input data to obtain an optimal base station beam matrix; the first constraint condition is used to constrain power transmission on the base station side.

[0075] Furthermore, the optimization problem solving module 320 is also used for: Determine the first channel matrix, the second channel matrix, the third channel matrix, the optimal base station beam matrix and the preset block matrix as second input data of the channel model; Under the second constraint condition, the second optimization subproblem is solved according to the second input data using a continuous convex approximation algorithm to obtain an optimal reflection phase shift coefficient matrix; the second constraint condition is used to constrain the phase shift coefficient of the RIS reflection element.

[0076] Furthermore, the optimization problem solving module 320 is also used for: Determine the first channel matrix, the second channel matrix, the third channel matrix, the optimal base station beam matrix and the optimal reflection phase shift coefficient matrix as third input data of the channel model; Under the conditions of satisfying the third, fourth and fifth constraints, the swap-matching algorithm is used to solve the third optimization subproblem according to the third input data to obtain the optimal block matrix; the third constraint is used to constrain the number of reflection elements in each block RIS; the fourth constraint is used to constrain each reflection element to be allocated to only one block RIS; the fifth constraint is used to constrain the binary variables related to the pairing of block RIS and reflection elements.

[0077] It should be noted that the cell-free communication system and rate optimization device provided by the present invention can execute the cell-free communication system and rate optimization method described in any of the above embodiments during specific operation, which will not be described in detail in this embodiment.

[0078] Figure 4 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute a method for optimizing the sum rate of a cell-free communication system, the method comprising: constructing a channel model of a cell-free communication system assisted by a block intelligent reflection surface RIS; solving the sum rate optimization problem in the channel model to obtain an optimal base station beam matrix, an optimal reflection phase shift coefficient matrix of the block RIS, and an optimal block matrix representing the allocation relationship between the block RIS and the reflection element; based on the optimal base station beam matrix, the optimal reflection phase shift coefficient matrix and the optimal block matrix, determining the maximum sum rate of the cell-free communication system.

[0079] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0080] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the cell-free communication system and rate optimization method provided in the above-mentioned embodiments, and the method includes: constructing a channel model of a cell-free communication system assisted by a block intelligent reflection surface RIS; solving the sum rate optimization problem in the channel model to obtain an optimal base station beam matrix, an optimal reflection phase shift coefficient matrix of the block RIS, and an optimal block matrix characterizing the distribution relationship between the block RIS and the reflection elements; based on the optimal base station beam matrix, the optimal reflection phase shift coefficient matrix and the optimal block matrix, determining the maximum sum rate of the cell-free communication system.

[0081] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the cell-free communication system and rate optimization method provided in the above-mentioned embodiments, the method comprising: constructing a channel model of a cell-free communication system assisted by a block intelligent reflection surface RIS; solving the sum rate optimization problem in the channel model to obtain an optimal base station beam matrix, an optimal reflection phase shift coefficient matrix of the block RIS, and an optimal block matrix characterizing the distribution relationship between the block RIS and the reflection elements; based on the optimal base station beam matrix, the optimal reflection phase shift coefficient matrix and the optimal block matrix, determining the maximum sum rate of the cell-free communication system.

[0082] The system embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. Those of ordinary skill in the art may understand and implement the present embodiment without creative effort.

[0083] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these 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 invention.

Claims

1. A cell-free communication system and rate optimization method, characterized in that: include: Construct a channel model for a cell-free communication system assisted by a block intelligent reflector RIS; Solving the sum rate optimization problem in the channel model, obtaining an optimal base station beam matrix, an optimal reflection phase shift coefficient matrix of the block RIS, and an optimal block matrix representing the distribution relationship between the block RIS and the reflection elements; Based on the optimal base station beam matrix, the optimal reflection phase shift coefficient matrix and the optimal blocking matrix, a maximum sum rate of the cell-free communication system is determined.

2. The cell-free communication system and rate optimization method according to claim 1, characterized in that: The method of solving the sum rate optimization problem in the channel model to obtain the optimal base station beam matrix, the optimal reflection phase shift coefficient matrix of the block intelligent reflection surface RIS, and the optimal block matrix representing the distribution relationship between the block RIS and the reflection elements includes: The sum rate optimization problem in the channel model is decomposed into a first optimization sub-problem, a second optimization sub-problem and a third optimization sub-problem; the first optimization sub-problem is used to optimize the base station beam matrix under the condition of a given reflection phase shift coefficient matrix and a block matrix to maximize the sum rate; the second optimization sub-problem is used to optimize the reflection phase shift coefficient matrix under the condition of a given base station beam matrix and a block matrix to maximize the sum rate; the third optimization sub-problem is used to optimize the block matrix under the condition of a given base station beam matrix and a reflection phase shift coefficient matrix to maximize the sum rate; Solving the first optimization sub-problem based on a preset reflection phase shift coefficient matrix and a preset block matrix to obtain an optimal base station beam matrix; Solving the second optimization sub-problem based on the optimal base station beam matrix and the preset block matrix to obtain an optimal reflection phase shift coefficient matrix; Based on the optimal base station beam matrix and the optimal reflection phase shift coefficient matrix, the third optimization sub-problem is solved to obtain an optimal block matrix.

3. The cell-free communication system and rate optimization method according to claim 2, characterized in that: The step of solving the first optimization sub-problem based on a preset reflection phase shift coefficient matrix and a preset block matrix to obtain an optimal base station beam matrix includes: Acquire a first channel matrix, a second channel matrix, a third channel matrix, a preset reflection phase shift coefficient matrix and a preset block matrix in the cell-free communication system; the first channel matrix represents a channel from a wireless access point to a user equipment; the second channel matrix represents a channel from a wireless access point to a block RIS; the third channel matrix represents a channel from a block RIS to a user equipment; Determine the first channel matrix, the second channel matrix, the third channel matrix, the preset reflection phase shift coefficient matrix and the preset block matrix as first input data of the channel model; Under the first constraint condition, a fractional programming algorithm is used to solve the first optimization subproblem according to the first input data to obtain an optimal base station beam matrix; the first constraint condition is used to constrain power transmission on the base station side.

4. The cell-free communication system and rate optimization method according to claim 3, characterized in that: The step of solving the second optimization sub-problem based on the optimal base station beam matrix and the preset block matrix to obtain an optimal reflection phase shift coefficient matrix includes: Determine the first channel matrix, the second channel matrix, the third channel matrix, the optimal base station beam matrix and the preset block matrix as second input data of the channel model; Under the second constraint condition, the second optimization subproblem is solved according to the second input data using a continuous convex approximation algorithm to obtain an optimal reflection phase shift coefficient matrix; the second constraint condition is used to constrain the phase shift coefficient of the RIS reflection element.

5. The cell-free communication system and rate optimization method according to claim 4, characterized in that: The step of solving the third optimization sub-problem based on the optimal base station beam matrix and the optimal reflection phase shift coefficient matrix to obtain an optimal block matrix includes: Determine the first channel matrix, the second channel matrix, the third channel matrix, the optimal base station beam matrix and the optimal reflection phase shift coefficient matrix as third input data of the channel model; Under the conditions of satisfying the third, fourth and fifth constraints, the swap-matching algorithm is used to solve the third optimization subproblem according to the third input data to obtain the optimal block matrix; the third constraint is used to constrain the number of reflection elements in each block RIS; the fourth constraint is used to constrain each reflection element to be allocated to only one block RIS; the fifth constraint is used to constrain the binary variables related to the pairing of block RIS and reflection elements.

6. The cell-free communication system and rate optimization method according to claim 5, characterized in that: The maximum sum rate is determined by the following formula: 。 in, Indicates l A base station beam vector of a wireless access point to the kth user equipment; represents the reflection phase shift coefficient vector of the rth RIS; represents the allocation relationship between the block RIS and the reflection element in the rth RIS; represents the signal-to-noise ratio at the kth user equipment.

7. A cell-free communication system and rate optimization device, characterized in that: include: A model building module, used to build a channel model of a cell-free communication system assisted by a block intelligent reflector RIS; An optimization problem solving module is used to solve the sum rate optimization problem in the channel model to obtain an optimal base station beam matrix, an optimal reflection phase shift coefficient matrix of the block RIS, and an optimal block matrix representing the distribution relationship between the block RIS and the reflection elements; The maximum sum rate module is used to determine the maximum sum rate of the cell-free communication system based on the optimal base station beam matrix, the optimal reflection phase shift coefficient matrix and the optimal block matrix.

8. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the cell-free communication system and rate optimization method according to any one of claims 1 to 6 are implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the cell-free communication system and rate optimization method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that: When the computer program is executed by a processor, the steps of the cell-free communication system and rate optimization method according to any one of claims 1 to 6 are implemented.

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