Hybrid precoding method, apparatus, device, medium and program product

Abstract

The present invention relates to the field of communications technology and provides a hybrid precoding method, apparatus, device, medium, and program product. The method comprises: establishing a first problem model of a digital-analog hybrid precoding structure based on an intelligent metasurface (RIS); performing an equivalent transformation on the first problem model to obtain a second problem model; updating matrices and parameters in the second problem model to determine a digital-analog hybrid precoding matrix; and performing hybrid precoding on a transmitted signal based on the digital-analog hybrid precoding matrix. The present invention replaces traditional antennas and phase shifters with low-power, low-complexity RIS to achieve low-complexity, energy-efficient coding, thereby reducing power consumption and hardware costs.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a hybrid precoding method, apparatus, device, medium and program product. Background Art

[0002] 6G operates at a higher frequency than 5G and requires higher communication speeds. Therefore, 6G communication systems require more antennas at the base station to improve the system's diversity gain and beam directivity. However, as the number of antennas increases, the traditional analog precoding architecture requires a large number of phase shifters when the base station performs hybrid digital-analog precoding on the transmitted signal, resulting in high system power consumption. Summary of the Invention

[0003] The present invention provides a hybrid precoding method, apparatus, device, medium and program product to solve the defects of the analog precoding structure in the prior art, such as a large number of phase shifters required and high system power consumption, thereby reducing system power consumption.

[0004] The present invention provides a hybrid precoding method, comprising:

[0005] Establish the first problem model of the digital-analog hybrid precoding structure based on the intelligent metasurface RIS;

[0006] Performing equivalent transformation on the first problem model to obtain a second problem model;

[0007] Updating the matrix and parameters in the second problem model to determine a digital-analog hybrid precoding matrix;

[0008] Perform hybrid precoding on the transmission signal according to the digital-analog hybrid precoding matrix.

[0009] According to a hybrid precoding method provided by the present invention, performing equivalent transformation on the first problem model to obtain a second problem model includes:

[0010] Introducing auxiliary parameter variables and using a preset iterative optimization algorithm to perform equivalent transformation on the first problem model to obtain a first target problem model;

[0011] According to a set of linear combiners and a set of weighting matrices introduced by the weighted minimum mean square error (WMMSE) criterion, the first target problem model is equivalently transformed to obtain a second target problem model;

[0012] According to the penalty coefficient introduced by the penalty coefficient dual decomposition PDD algorithm and a set of auxiliary precoding matrices, the second target problem model is equivalently transformed to obtain the second problem model.

[0013] According to a hybrid precoding method provided by the present invention, updating the matrix and parameters in the second problem model includes:

[0014] Using a partial derivative method, updating the linear combiner and the weighting matrix in the second problem model;

[0015] Updating the auxiliary precoding matrix in the second problem model using a Lagrange multiplier method;

[0016] Using a preset penalty coefficient term, updating the digital precoding matrix and the RIS matrix in the second problem model;

[0017] The preset iterative optimization algorithm is adopted to update the auxiliary parameter variables in the second problem model.

[0018] According to a hybrid precoding method provided by the present invention, determining a digital-analog hybrid precoding matrix includes: after n iterative updates, determining a first function value obtained by the first target problem model in the current iterative update and a second function value obtained by the previous iterative update;

[0019] If the difference between the first function value and the second function value is less than a set threshold, the iteration is exited to obtain the digital-analog hybrid precoding matrix.

[0020] According to a hybrid precoding method provided by the present invention, the first problem model of establishing a digital-analog hybrid precoding structure based on an intelligent metasurface RIS comprises:

[0021] Determine the total power of the base station's transmitter and the system's spectral efficiency;

[0022] According to the total power of the transmitter and the spectrum efficiency of the system, a first problem model of a digital-analog hybrid precoding structure based on RIS is established.

[0023] According to a hybrid precoding method provided by the present invention, the digital-analog hybrid precoding matrix includes a target digital precoding matrix and a target RIS matrix obtained by iterative updating;

[0024] The performing hybrid precoding on the transmit signal according to the digital-analog hybrid precoding matrix includes:

[0025] Encoding the transmit signal according to the target digital precoding matrix to obtain a first coded signal;

[0026] The first coded signal is encoded according to the target RIS matrix to obtain a second coded signal.

[0027] The present invention also provides a hybrid precoding device, comprising the following modules:

[0028] A problem model establishment module is used to establish a first problem model of a digital-analog hybrid precoding structure based on an intelligent metasurface RIS; an equivalent transformation module is used to perform an equivalent transformation on the first problem model to obtain a second problem model;

[0029] An updating module is used to update the matrix and parameters in the second problem model to determine the digital-analog hybrid precoding matrix; and a coding module is used to perform hybrid precoding on the transmitted signal according to the digital-analog hybrid precoding matrix.

[0030] The present invention also 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, any of the hybrid precoding methods described above is implemented.

[0031] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which implements any of the hybrid precoding methods described above when executed by a processor.

[0032] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above hybrid precoding methods.

[0033] The hybrid precoding method, apparatus, device, medium, and program product provided by the present invention establish a first problem model of a digital-analog hybrid precoding structure based on an intelligent metasurface (RIS); perform an equivalent transformation on the first problem model to obtain a second problem model; update the matrix and parameters in the second problem model to determine the digital-analog hybrid precoding matrix; and perform hybrid precoding on the transmitted signal based on the digital-analog hybrid precoding matrix. By introducing a low-power, low-complexity RIS to replace traditional antennas and phase shifters, the present invention achieves low-complexity, energy-efficient coding, thereby reducing power consumption and hardware costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to 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 any creative work.

[0035] Figure 1 This is one of the flow charts of the hybrid precoding method provided by the present invention.

[0036] Figure 2 It is a schematic diagram of a communication system based on a digital-analog hybrid precoding structure of RIS provided by the present invention.

[0037] Figure 3 This is the second flow chart of the hybrid precoding method provided by the present invention.

[0038] Figure 4 Schematic diagram comparing the technical advantages provided by the present invention.

[0039] Figure 5 It is a schematic diagram of the convergence times of the algorithm provided by the present invention.

[0040] Figure 6 It is a structural diagram of the hybrid precoding device provided by the present invention.

[0041] Figure 7 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0042] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0043] In terms of base station structure, the base station side adopts a traditional digital-analog hybrid precoding structure. Specifically, the baseband signal first passes through the baseband processor to complete digital precoding. After completing digital precoding, the signal passes through the RF link and the analog precoding structure based on the phase shifter to complete analog precoding. Under this structure, the power consumption of a 1-bit phase shifter is 10mW, the power consumption of a 2-bit phase shifter is 40mW, and the power consumption of a 4-bit phase shifter exceeds 50mW. When the base station deploys large-scale antennas (for example, the number of transmitting antennas is 1024), the number of supporting phase shifters is the product of the number of antennas (1024) and the number of RF (Radio Frequency) links, resulting in high base station power consumption and hardware complexity.

[0044] To reduce the high power consumption associated with bulky phase shifters, the present invention introduces a low-power, low-complexity RIS (Reconfigurable Intelligent Surface) to replace traditional antennas and phase shifters. Due to the adjustable phase characteristics of its surface RIS units, RIS can replace traditional base station phase shifters and antennas. RIS is a novel device with low cost and low power consumption. Each RIS unit is phase-adjustable, and the RIS can function as an antenna. Therefore, RIS can replace the analog precoding and antenna components of traditional digital-analog hybrid precoding architectures.

[0045] The following combination Figure 1-Figure 7 The hybrid precoding method, apparatus, device, medium and program product of the present invention are described.

[0046] Figure 1 This is one of the flow charts of the hybrid precoding method provided by the present invention, such as Figure 1 As shown, the method includes the following:

[0047] Step 101: Establish a first problem model of a digital-analog hybrid precoding structure based on an intelligent metasurface RIS.

[0048] The total power of the base station transmitter and the spectrum efficiency of the system are determined; and a first problem model of a digital-analog hybrid precoding structure based on RIS is established according to the total power of the transmitter and the spectrum efficiency of the system.

[0049] like Figure 2 As shown, the base station communicates with U multi-antenna users, and RIS replaces the traditional analog precoding structure based on phase shifters. The base station is equipped with N t transmit antennas and N RF The user end is equipped with N r The fully digital structure of the antenna. On the base station side, the signal is transmitted By UN s data streams, where C M×N Represents a complex matrix with M rows and N columns, that is Representing a UN S The transmitted signal is first digitally precoded, and the digital precoding matrix is ​​expressed as Then, the digital pre-coded signal is Channel matrix between the RF link and the RIS-based analog precoding structure Analog precoding consists of t The RIS of the RIS units is completed, and the RIS matrix is ​​expressed as Based on this, the energy efficiency maximization problem can be modeled as follows, that is, the first problem model:

[0050]

[0051]

[0052]

[0053] Where R is the spectrum efficiency of the system, is the total power of the transmitter, P RF is the power consumption of each RF link, P RIS is the power consumption of each RIS unit, P BB is the power consumption of the baseband processor, F BB The u-th submatrix of S +1: uN S ), the colon before the comma means taking out all row elements of the matrix, and the content after the comma means taking out (u-1)N S +1 to uN S Column elements are taken out, (:, (u-1)N S +1: uN S ) overall means that all rows of the matrix (u-1)N S +1 to uN S The column elements are taken out. Formulas (1b) and (1c) are constraints. (1b) is the system's transmission power constraint, that is, the system's transmission power is less than the predetermined value P max , P max is the maximum transmit power limit of the base station; (1c) is the constant modulus limit of the RIS unit.

[0054] Step 102: Perform equivalent transformation on the first problem model to obtain a second problem model.

[0055] Auxiliary parameter variables are introduced, and a preset iterative optimization algorithm is used to perform an equivalent transformation on the first problem model to obtain a first target problem model; wherein the iterative optimization algorithm can be Dinkelbach's algorithm, which is an iterative algorithm for solving nonlinear programming problems and is used to solve optimization problems with fractional objective functions. According to a set of linear combiners and a set of weighting matrices introduced according to the weighted minimum mean square error (WMMSE) criterion, the first target problem model is equivalently transformed to obtain a second target problem model; according to the penalty coefficients introduced by the penalty coefficient dual decomposition (PDD) algorithm and a set of auxiliary precoding matrices, the second target problem model is equivalently transformed to obtain a second problem model.

[0056] For example, we first use Dinkelbach's algorithm to perform an equivalent transformation on problem (1) and obtain:

[0057]

[0058] st(1b),(1c); (2b)

[0059] Among them, the collection η is an auxiliary parameter variable introduced. Problem (1) is a problem consisting of the objective function (1a) and the constraints (1b) and (1c), namely the first problem model.

[0060] Then, a set of linear combiners are introduced based on the WMMSE criterion and a set of weight matrices Performing an equivalent transformation on problem (2), we can obtain:

[0061]

[0062] st(1b),(1c); (3b)

[0063] Among them, problem (2) refers to the problem consisting of the objective function (2a) and the constraint condition (2b), that is, the first objective problem model. Tr(·) is the trace function of the matrix, N S ×N S The identity matrix of dimension E u is the MSE variance matrix, expressed as:

[0064]

[0065] Among them, H u is the channel matrix between RIS and the u-th user, is the noise and interference covariance matrix, σ 2 is the power of the system noise, (·) H To find the conjugate transpose of a matrix, N r ×N r The identity matrix of dimension .

[0066] Furthermore, based on the PDD algorithm, a penalty coefficient ρ and a set of auxiliary precoding matrices are introduced For approximate representation Performing an equivalent transformation on problem (3), we obtain:

[0067]

[0068]

[0069]

[0070] in,

[0071]

[0072] and F j The sum is an intermediate variable. Problem (3) is a problem consisting of the objective function (3a) and the constraint condition (3b), which is the second objective problem model.

[0073] Step 103: Update the matrix and parameters in the second problem model to determine the digital-analog hybrid precoding matrix.

[0074] The linear combiner and weighting matrix in the second problem model are updated by using the partial derivative method; the auxiliary precoding matrix in the second problem model is updated by using the Lagrange multiplier method; the digital precoding matrix and RIS matrix in the second problem model are updated by using the preset penalty coefficient term; and the auxiliary parameter variables in the second problem model are updated by using the preset iterative optimization algorithm.

[0075] For example, update the introduced auxiliary linear combiner and weight matrix. Use the partial derivative method to update the linear combiner and weighted matrix get:

[0076]

[0077]

[0078] Update the auxiliary precoding matrix introduced. Given all other variables, the auxiliary precoding matrix is ​​updated using the Lagrange multiplier method. Specifically, ignoring irrelevant items, problem (5) is transformed into:

[0079]

[0080] Where λ is the introduced Lagrange multiplier. Problem (5) is composed of the objective function (5a) and the constraints (5b) and (5c), i.e., the second problem model.

[0081] The closed-form solution of problem (8) can be expressed as:

[0082]

[0083] in, P u =H u M u W u , Q u and P u The mean and are intermediate variables.

[0084] Through binary search, by making Solve for λ.

[0085] Update the digital precoding matrix and RIS. By optimizing the penalty coefficient term in the objective function (5a) Update digital precoding matrix And RIS matrix Φ. Using the least squares method, for the u-th user, we can solve F BB，uThe closed-form solution is:

[0086]

[0087] in, To find the pseudo inverse of the matrix. Transform question (5) into:

[0088]

[0089]

[0090] in, and is an intermediate variable, by using the transformation equation Where vec(·) means vectorizing the matrix columns, that is, connecting the first to the last columns of a matrix in sequence to form a vector, and transforming problem (11) into:

[0091]

[0092]

[0093] Where D is defined as the set of non-zero elements in vec(Φ). Problem (11) is a problem consisting of the objective function (11a) and the constraints (11b).

[0094] Problem (12) is a problem consisting of the objective function (12a) and the constraints (12b). Problem (12) can be further transformed into:

[0095]

[0096]

[0097] in, is an intermediate variable, the matrix

[0098] Problem (13) is a problem consisting of the objective function (13a) and the constraints (13b). Problem (13) can be further expressed as:

[0099]

[0100]

[0101] in, To extract the real part of a complex number.

[0102] Problem (14) has a classic form that can be solved by the MM algorithm. Therefore, the MM algorithm is used to solve problem (14) and obtain the optimal solution of φ opt Among them, problem (14) refers to the problem consisting of the objective function (14a) and the constraint condition (14b). Then, according to the mapping approximation principle, φ opt The elements in are mapped to the corresponding discrete phase set, expressed as:

[0103]

[0104] Among them, ∠(·) is the phase extraction operation, is the suboptimal solution after mapping of the i-th RIS unit, and the phase set Q RIS Contains the phase range [0, 2π) through B RIS All phases after bit quantization, ψ is Q RIS All discrete phases in. Using the relationship between φ and Φ, that is, φ is the vector representation of all diagonal elements of Φ, the RIS matrix in each iteration can be obtained

[0105] Update the auxiliary parameter variables introduced according to Dinkelbach's algorithm. Given all other variables, calculate the system's spectral efficiency R and system power consumption P. According to Dinkelbach's algorithm, calculate the auxiliary parameter variable η introduced in each iteration:

[0106]

[0107] After n iterative updates, the first function value of the first target problem model obtained in the current iterative update and the second function value obtained in the previous iterative update are determined; if the difference between the first function value and the second function value is less than a set threshold, the iteration is exited to obtain a digital-analog hybrid precoding matrix.

[0108] For example, the algorithm convergence is judged. If the convergence conditions are met, the final digital-analog hybrid precoding matrix is ​​obtained; if the convergence conditions are not met, the steps of updating the matrix and parameters of the problem model are repeated.

[0109] The present invention defines that after the nth iteration, the objective function of problem (2) is expressed as By comparison The relationship between and threshold ξ is used to determine whether the algorithm converges. Specifically, if Then repeat the steps of updating the matrix and parameters of the problem model. Then exit the iteration and get the final digital precoding matrix and RIS matrix Φ, namely the target digital precoding matrix and the target RIS matrix.

[0110] Step 104: Perform hybrid precoding on the transmit signal according to the digital-analog hybrid precoding matrix.

[0111] The transmit signal is encoded according to a target digital precoding matrix to obtain a first coded signal, and the first coded signal is encoded according to a target RIS matrix to obtain a second coded signal. The digital-analog hybrid precoding matrix includes the target digital precoding matrix and the target RIS matrix obtained by iterative updating.

[0112] For example, the transmitted signal first passes through the target digital precoding matrix. After the digital precoding, the first coded signal passes through the channel matrix between the RF link and the RIS-based analog precoding structure. The signal output by the channel matrix passes through the target RIS matrix and outputs the second coded signal. The base station sends the second coded signal to the user equipment.

[0113] The hybrid precoding method provided by the present invention establishes a first problem model of a digital-analog hybrid precoding structure based on an intelligent metasurface (RIS); performs an equivalent transformation on the first problem model to obtain a second problem model; updates the matrix and parameters in the second problem model to determine the digital-analog hybrid precoding matrix; and performs hybrid precoding on the transmitted signal based on the digital-analog hybrid precoding matrix. By introducing a low-power, low-complexity RIS to replace traditional antennas and phase shifters, the present invention achieves low-complexity, energy-efficient coding, thereby reducing power consumption and hardware costs.

[0114] In order to further analyze and explain the hybrid precoding method proposed in the present invention, refer to Figure 3-Figure 5 And the following examples.

[0115] This paper considers the downlink of a MU-MIMO (Multi-input Multi-output) communication system, where both the base station and the user are equipped with multiple antennas, and the base station adopts a RIS-based digital-analog hybrid precoding structure. The present invention proposes a low-complexity, high-energy-efficiency precoding method based on an intelligent metasurface digital-analog hybrid structure. This method can also be understood as a low-complexity, energy-efficient digital-analog hybrid precoding method, namely, an EE-BCD hybrid precoding method, which maximizes the energy efficiency of the downlink of the communication system based on the RIS-based digital-analog hybrid precoding structure.

[0116] The present invention utilizes Dinkelbach's algorithm to process the fractional energy efficiency expression and uses the WMMSE criterion to equivalently convert the energy efficiency maximization problem into a WMMSE minimization problem. Then, the PDD (Penalty Dual Decomposition) algorithm is used to introduce a set of auxiliary precoding matrices for approximating the digital-analog hybrid precoding matrix. In the EE-BCD hybrid precoding algorithm, all introduced intermediate variables and the hybrid precoding matrix to be solved are updated in an iterative manner. By using the Lagrange multiplier method, a closed-form solution of the auxiliary precoding matrix is ​​derived in each iteration of the EE-BCD hybrid precoding algorithm. In each iteration of the EE-BCD hybrid precoding algorithm, the least squares digital precoding matrix is ​​used, and the RIS-based analog precoding matrix is ​​updated using the MM (Majorization-minimization) algorithm.

[0117] A RIS unit with a 1-bit phase shift capability consumes 5mW, a 2-bit phase shift capability consumes 10mW, and a 4-bit phase shift capability consumes less than 15mW. Compared to a digital-analog hybrid precoding structure using a phase shifter, when the number of transmit antennas is 1024, a base station using a 1024×1024 RIS can significantly reduce system power consumption and improve system energy efficiency.

[0118] The present invention proposes a low-complexity, high-energy-efficiency precoding method based on an intelligent metasurface digital-analog hybrid structure, which can achieve the following goals:

[0119] First, it avoids the problem that as the number of antennas increases, when the base station performs mixed digital-analog precoding on the transmitted signal, the traditional analog precoding structure requires a large number of phase shifters, resulting in excessive system power consumption and hardware costs.

[0120] Second, a low-complexity digital-analog hybrid precoding algorithm design is proposed with the goal of maximizing energy efficiency, maximizing the energy efficiency of the communication system based on the RIS digital-analog hybrid structure.

[0121] Third, compared with the traditional communication system based on the phase shifter digital-analog hybrid structure, the communication system of the present invention combined with the digital-analog hybrid structure based on RIS has a higher energy efficiency advantage.

[0122] refer to Figure 3 The present invention proposes a low-complexity, high-energy-efficiency precoding method based on an intelligent metasurface digital-analog hybrid structure, comprising the following steps:

[0123] Step 1: Establish a problem model of the digital-analog hybrid precoding structure based on RIS;

[0124] Step 2: Perform equivalent transformation on the problem model of the RIS-based digital-analog hybrid precoding structure;

[0125] Step 3: Update the introduced auxiliary linear combiner and weighting matrix;

[0126] Step 4: Update the introduced auxiliary precoding matrix;

[0127] Step 5: Update the digital precoding matrix and RIS;

[0128] Step 6: Update the introduced auxiliary parameter variables according to Dinkelbach's algorithm;

[0129] Step 7: Determine the convergence of the algorithm. If the convergence condition is met, the final digital-analog hybrid precoding matrix is ​​obtained. If the convergence condition is not met, repeat step 3.

[0130] To reduce the complexity of simulation, consider the typical application scenario: the number of base station antennas N t =8×8=64, the number of radio frequency links N RF =8, the number of users U=4, and the number of user antennas is N r =2×2=4, the number of data streams per user N s =1, convergence threshold ξ=10 -5 , noise power δ 2 =1, penalty coefficient To verify the hybrid digital-analog precoding, a typical millimeter wave channel with a carrier frequency of f c =300GHz, the number of millimeter wave channels between the base station and each user is 4. Using MATLAB software simulation, considering the energy efficiency comparison between the proposed technical solution and the existing technical solution, 4-bit, 2-bit, and 1-bit phase shifters and RIS with corresponding phase shift capabilities are set respectively. The comparison results are shown in Figure 2. Figure 4 As shown in the figure, the proposed energy efficiency maximization algorithm outperforms the digital-analog hybrid precoding communication system based on the phase shifter structure in the communication system based on the RIS structure when the system has 4-bit, 2-bit, and 1-bit phase shift capabilities.

[0131] according to Figure 5 As shown in the figure, the proposed energy efficiency maximization algorithm can reach convergence in 20 iterations in a communication system based on a RIS-based digital-analog hybrid precoding structure under the conditions of 4-bit, 2-bit, and 1-bit phase shift capabilities, and has low complexity. From a mathematical perspective, the complexity of the algorithm is analyzed. For the design of F, the main complexity comes from the matrix inversion operation for all users. Its complexity is Among them O(C λ ) is the complexity of calculating the Lagrange multiplier λ. Calculate F BBThe complexity is O(UN t N RF N s ). The complexity of the MM algorithm used to calculate Φ is Where T MM It is the number of iterations required for the MM algorithm to converge. The number of times the algorithm converges in this invention is defined as T max , when T max The algorithm converges when it reaches 20. The complexity of the entire algorithm is With N t It is proportional to the cube of , and has low complexity.

[0132] For a communication system with a phase shifter-based digital-analog hybrid precoding structure, the power consumption of a 1-bit phase shifter is 10mW, the power consumption of a 2-bit phase shifter is 40mW, and the power consumption of a 4-bit phase shifter exceeds 50mW. When a base station deploys large-scale antennas (such as when the number of transmitting antennas is 1024), the number of phase shifters required is the product of the number of antennas (1024) and the number of RF chains, resulting in high power consumption and high hardware complexity in the base station. However, for the communication system with a RIS-based digital-analog hybrid precoding structure adopted by the present invention, the power consumption of a RIS unit with a 1-bit phase shift capability is 5mW, the power consumption of a RIS unit with a 2-bit phase shift capability is 10mW, and the power consumption of a RIS unit with a 4-bit phase shift capability is less than 15mW. Compared to a digital-analog hybrid precoding structure using a phase shifter structure, when the number of transmitting antennas is 1024, the base station using a 1024×1024 scale RIS can significantly reduce the power consumption of the system and improve the energy efficiency of the system. In addition, in the cases of 4-bit, 2-bit and 1-bit phase shift capabilities of the system, the proposed energy efficiency maximization algorithm combined with the communication system based on RIS digital-analog hybrid precoding structure outperforms the communication system based on phase shifter structure digital-analog hybrid precoding.

[0133] This invention replaces traditional antennas and phase shifters with low-power, low-complexity RISs, achieving low-complexity, energy-efficient coding, thereby reducing power consumption and hardware costs. Furthermore, a hybrid precoding scheme with maximum energy efficiency is achieved in a communication system based on a RIS-based digital-analog hybrid precoding structure. This provides important guidance for achieving low power consumption and low complexity in base stations and communication systems, significantly reducing the deployment costs of 6G communication systems.

[0134] The hybrid precoding device provided by the present invention is described below. The hybrid precoding device described below and the hybrid precoding method described above can be referenced to each other.

[0135] refer to Figure 6 The hybrid precoding device provided by the present invention includes a problem model building module 601, an equivalent conversion module 602, an updating module 603 and a coding module 604.

[0136] A problem model building module 601 is used to build a first problem model of a digital-analog hybrid precoding structure based on an intelligent metasurface RIS;

[0137] An equivalent transformation module 602 is configured to perform equivalent transformation on the first problem model to obtain a second problem model;

[0138] An updating module 603 is configured to update the matrix and parameters in the second problem model to determine a digital-analog hybrid precoding matrix;

[0139] The coding module 604 is configured to perform hybrid precoding on the transmit signal according to the digital-analog hybrid precoding matrix.

[0140] The hybrid precoding device provided by the present invention establishes a first problem model of a digital-analog hybrid precoding structure based on an intelligent metasurface (RIS); performs an equivalent transformation on the first problem model to obtain a second problem model; updates the matrix and parameters in the second problem model to determine the digital-analog hybrid precoding matrix; and performs hybrid precoding on the transmitted signal based on the digital-analog hybrid precoding matrix. By introducing a low-power, low-complexity RIS to replace traditional antennas and phase shifters, the present invention achieves low-complexity, energy-efficient coding, thereby reducing power consumption and hardware costs.

[0141] In one embodiment, the equivalent conversion module 602 is further configured to:

[0142] Introducing auxiliary parameter variables and using a preset iterative optimization algorithm to perform equivalent transformation on the first problem model to obtain a first target problem model;

[0143] According to a set of linear combiners and a set of weighting matrices introduced by the weighted minimum mean square error (WMMSE) criterion, the first target problem model is equivalently transformed to obtain a second target problem model;

[0144] According to the penalty coefficient introduced by the penalty coefficient dual decomposition PDD algorithm and a set of auxiliary precoding matrices, the second target problem model is equivalently transformed to obtain the second problem model.

[0145] In one embodiment, the update module 603 is further configured to:

[0146] Using a partial derivative method, updating the linear combiner and the weighting matrix in the second problem model;

[0147] Updating the auxiliary precoding matrix in the second problem model using a Lagrange multiplier method;

[0148] Using a preset penalty coefficient term, updating the digital precoding matrix and the RIS matrix in the second problem model;

[0149] The preset iterative optimization algorithm is adopted to update the auxiliary parameter variables in the second problem model.

[0150] In one embodiment, the update module 603 is further configured to:

[0151] After n iterative updates, determining a first function value of the first target problem model obtained in the current iterative update and a second function value obtained in the previous iterative update;

[0152] If the difference between the first function value and the second function value is less than a set threshold, the iteration is exited to obtain the digital-analog hybrid precoding matrix.

[0153] In one embodiment, the question model building module 601 is further configured to:

[0154] Determine the total power of the base station's transmitter and the system's spectral efficiency;

[0155] According to the total power of the transmitter and the spectrum efficiency of the system, a first problem model of a digital-analog hybrid precoding structure based on RIS is established.

[0156] In one embodiment, the encoding module 604 is further configured to:

[0157] Encoding the transmit signal according to the target digital precoding matrix to obtain a first coded signal;

[0158] The first coded signal is encoded according to the target RIS matrix to obtain a second coded signal.

[0159] Figure 7 An example of a physical structure diagram of an electronic device is shown below. Figure 7 As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730, and a communication bus 740, wherein the processor 710, the communication interface 720, and the memory 730 communicate with each other via the communication bus 740. The processor 710 may call the logic instructions in the memory 730 to execute the hybrid precoding method, which includes: establishing a first problem model of a digital-analog hybrid precoding structure based on the intelligent metasurface RIS; performing an equivalent transformation on the first problem model to obtain a second problem model; updating the matrix and parameters in the second problem model to determine the digital-analog hybrid precoding matrix; and performing hybrid precoding on the transmitted signal according to the digital-analog hybrid precoding matrix.

[0160] In addition, the logic instructions in the above-mentioned memory 730 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, 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. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or 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: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0161] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the hybrid precoding method provided by the above methods, the method including: establishing a first problem model of a digital-analog hybrid precoding structure based on an intelligent metasurface RIS; performing an equivalent transformation on the first problem model to obtain a second problem model; updating the matrix and parameters in the second problem model to determine the digital-analog hybrid precoding matrix; and performing hybrid precoding on the transmitted signal according to the digital-analog hybrid precoding matrix.

[0162] 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 hybrid precoding method provided by the above-mentioned methods, the method comprising: establishing a first problem model of a digital-analog hybrid precoding structure based on an intelligent metasurface RIS; performing an equivalent transformation on the first problem model to obtain a second problem model; updating the matrix and parameters in the second problem model to determine the digital-analog hybrid precoding matrix; and performing hybrid precoding on the transmitted signal according to the digital-analog hybrid precoding matrix.

[0163] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0164] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable 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 each embodiment or certain parts of the embodiments.

[0165] 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 various embodiments of the present invention.

Claims

1. A hybrid precoding method, characterized in that: include: Establish the first problem model of the digital-analog hybrid precoding structure based on the intelligent metasurface RIS; Performing equivalent transformation on the first problem model to obtain a second problem model; Updating the matrix and parameters in the second problem model to determine a digital-analog hybrid precoding matrix; Performing hybrid precoding on the transmit signal according to the digital-analog hybrid precoding matrix; The equivalent transformation of the first problem model to obtain a second problem model includes: Introducing auxiliary parameter variables and using a preset iterative optimization algorithm to perform equivalent transformation on the first problem model to obtain a first target problem model; According to a set of linear combiners and a set of weighting matrices introduced by the weighted minimum mean square error (WMMSE) criterion, the first target problem model is equivalently transformed to obtain a second target problem model; Performing an equivalent transformation on the second target problem model according to the penalty coefficient introduced by the penalty coefficient dual decomposition PDD algorithm and a set of auxiliary precoding matrices to obtain the second problem model; The updating of the matrix and parameters in the second problem model includes: Using a partial derivative method, updating the linear combiner and the weighting matrix in the second problem model; Updating the auxiliary precoding matrix in the second problem model using a Lagrange multiplier method; Using a preset penalty coefficient term, updating the digital precoding matrix and the RIS matrix in the second problem model; The preset iterative optimization algorithm is adopted to update the auxiliary parameter variables in the second problem model.

2. The hybrid precoding method according to claim 1, wherein: The determining of the digital-analog hybrid precoding matrix includes: After n iterative updates, determining a first function value of the first target problem model obtained in the current iterative update and a second function value obtained in the previous iterative update; If the difference between the first function value and the second function value is less than a set threshold, the iteration is exited to obtain the digital-analog hybrid precoding matrix.

3. The hybrid precoding method according to claim 1, wherein: The first problem model of establishing a digital-analog hybrid precoding structure based on the intelligent metasurface RIS includes: Determine the total power of the base station's transmitter and the system's spectral efficiency; According to the total power of the transmitter and the spectrum efficiency of the system, a first problem model of a digital-analog hybrid precoding structure based on RIS is established.

4. The hybrid precoding method according to claim 1, wherein: The digital-analog hybrid precoding matrix includes a target digital precoding matrix and a target RIS matrix obtained by iterative updating; The performing hybrid precoding on the transmit signal according to the digital-analog hybrid precoding matrix includes: Encoding the transmit signal according to the target digital precoding matrix to obtain a first coded signal; The first coded signal is encoded according to the target RIS matrix to obtain a second coded signal.

5. A hybrid precoding device, characterized in that: include: A problem model building module, used to build a first problem model of a digital-analog hybrid precoding structure based on the intelligent metasurface RIS; An equivalent transformation module, configured to perform equivalent transformation on the first problem model to obtain a second problem model; An updating module, configured to update the matrix and parameters in the second problem model to determine a digital-analog hybrid precoding matrix; A coding module, configured to perform hybrid precoding on a transmit signal according to the digital-analog hybrid precoding matrix; The equivalent transformation module is further configured to introduce auxiliary parameter variables and use a preset iterative optimization algorithm to perform an equivalent transformation on the first problem model to obtain a first target problem model; and perform an equivalent transformation on the first target problem model using a set of linear combiners and a set of weighting matrices introduced according to a weighted minimum mean square error (WMMSE) criterion to obtain a second target problem model; Performing an equivalent transformation on the second target problem model according to the penalty coefficient introduced by the penalty coefficient dual decomposition PDD algorithm and a set of auxiliary precoding matrices to obtain the second problem model; The updating module is further configured to update the linear combiner and the weighting matrix in the second problem model using a partial derivative method; and update the auxiliary precoding matrix in the second problem model using a Lagrange multiplier method; The digital precoding matrix and the RIS matrix in the second problem model are updated by using a preset penalty coefficient term; and the auxiliary parameter variables in the second problem model are updated by using the preset iterative optimization algorithm.

6. 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 hybrid precoding method according to any one of claims 1 to 4 is implemented.

7. 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 hybrid precoding method according to any one of claims 1 to 4 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the hybrid precoding method according to any one of claims 1 to 4 is implemented.

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