Active element selection method for hybrid RIS-assisted cellular MIMO system

Abstract

The invention provides an active element selection method for a hybrid RIS-assisted cellular-removal MIMO system, and the method comprises the steps: building a channel model, deducing an expression of a downlink reachable rate, building a joint optimization problem of an AP beam matrix, a hybrid RIS beam matrix and an active RIS element position, and carrying out the joint optimization of the positions of the AP beam matrix, the hybrid RIS beam matrix and the active RIS element. AP power constraint, active RIS power constraint and RIS unit mode constraint serve as constraint conditions of the joint optimization problem, and the joint optimization problem is converted into an AP beam matrix optimization problem and a hybrid RIS beam forming matrix-active element position optimization problem by utilizing Lagrange dual transformation and multi-dimensional complex quadratic transformation; a convex optimization solver is adopted to alternately solve until convergence, an optimization result is obtained, and a precoding matrix of an AP, phase shift of an RIS and power and position selection of an active element are adjusted according to the optimization result, so that the sum rate of a user of the RIS-assisted cellular-removal MIMO system is maximized, and the performance of the cellular-removal MIMO system is improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communications, and in particular to an active component selection method for a hybrid RIS-assisted decellularized MIMO system. Background Art

[0002] Decellularized networks have gained significant traction in the fifth-generation mobile communications (B5G) era, where cell boundaries are eliminated. They hold great potential in next-generation indoor and hotspot environments, such as shopping malls, train stations, hospitals, and subways. Furthermore, decellularized networks are particularly effective in high-mobility scenarios, such as vehicular networks, where switching costs are minimal. Despite these advantages, traditional decellularized networks, due to the large number of access points (APs) deployed, consume high transmission and hardware power, resulting in low energy efficiency (EE), a challenge that future networks must overcome.

[0003] The recently emerged reconfigurable smart surface (RIS) is seen as an energy-efficient solution to improve system capacity. Integrating RIS with access radio units in a cell-free framework to coordinate and serve all users is considered a promising, low-cost strategy for improving signal coverage compared to traditional cellular architectures.

[0004] However, due to the double fading effect, passive RIS can suffer significant performance losses in practical applications. To address this issue, the concept of active RIS has been introduced. By providing amplification gain to each reflector, active RIS can simultaneously amplify the signal amplitude and adjust its phase, effectively mitigating the double fading effect. However, the performance improvement of active RIS comes at the expense of increased energy consumption; as the number of RIS units increases, its energy efficiency decreases. Summary of the Invention

[0005] To address the above issues, the present invention proposes a method for selecting active components for a hybrid RIS-assisted decellularized MIMO system, and a decellularized MIMO system based thereon, to improve the system's transmission rate. The present invention provides the following technical solutions:

[0006] An active element selection method for a hybrid RIS-assisted decellularized MIMO system. The decellularized MIMO system includes B access points (APs), K users, and R reconfigurable smart surfaces (RISs). Each AP is equipped with M antennas, and each RIS has N elements, including N a active components and NN a passive components; the active component selection method includes the following steps:

[0007] Establishing a channel model for a RIS-assisted decellularized MIMO system, wherein the channel is divided into a direct channel and an indirect channel;

[0008] Based on the channel model, an expression for the downlink achievable rate of the RIS-assisted decellularized MIMO system is derived;

[0009] Based on the expression of the downlink achievable rate, a joint optimization problem of the AP beam matrix, the hybrid RIS beam matrix, and the active RIS element positions is established, and the AP power constraint, the active RIS power constraint, and the RIS unit mode constraint are used as constraints of the joint optimization problem;

[0010] By using Lagrangian dual transformation and multi-dimensional complex quadratic transformation, the joint optimization problem is transformed into an AP beam matrix optimization problem and a hybrid RIS beamforming matrix-active element position optimization problem;

[0011] A convex optimization solver is used to alternately solve the AP active beam matrix optimization problem and the RIS beamforming matrix-active element position optimization problem to obtain the optimization results.

[0012] The optimization results are adjusted to maximize the downlink rate of the RIS-assisted decellularized MIMO system.

[0013] Preferably, the expression of the channel model is:

[0014]

[0015] The superscript H represents the conjugate transpose, represents the total cascade channel from the bth access point AP to user k, represents the direct channel from the bth access point AP to user k, represents the indirect channel from the rth RIS to user k, represents the indirect channel from the rth RIS to the bth access point AP. Both the direct channel and the indirect channel obey the Ruili distribution. represents the beamforming matrix of the r-th RIS, It is a binary diagonal matrix. When the diagonal element is 1, it means the corresponding RIS element is active, and when it is 0, it means it is passive. and are active RIS and passive RIS diagonal beamforming matrices, I N Represents the N-dimensional identity matrix.

[0016] Preferably, the expression of the downlink achievable rate is:

[0017]

[0018]

[0019] in and are the mean square error of the active noise of active RIS and the additive white Gaussian noise at user k, is the precoding matrix of AP for user k.

[0020] Preferably, by designing the AP beam matrix W and the hybrid RIS beam matrix and active RIS component locations To maximize the number of users and the rate, the joint optimization problem is expressed as:

[0021]

[0022] in and are the maximum transmission power of active RIS and AP respectively, is the maximum amplification factor of the active RIS element, and Tr represents the trace of the matrix.

[0023] Preferably, the Lagrange dual transformation is used to separate the logarithms in the objective function and the auxiliary variable is introduced :

[0024] .

[0025] Given ,right Optimize: Find the objective function Partial derivative, the optimal solution is ;

[0026] Use multidimensional complex quadratic transformation to solve the non-convexity problem of high-dimensional matrix scores and introduce auxiliary variables :

[0027]

[0028] Given ,right Optimize: find the objective function Partial derivative, the optimal solution is:

[0029] Where R represents the real part.

[0030] Preferably, the expression of the AP active beam matrix optimization problem is:

[0031]

[0032] The AP beam matrix , I K represents the K-dimensional identity matrix, I M represents the M-dimensional identity matrix, is a B-dimensional unit column vector, , represents the Kronecker product.

[0033] Preferably, the expression of the RIS beamforming matrix-active element position optimization problem is:

[0034]

[0035] in, represents a column vector of all 1s, sum represents the sum of elements, and diag represents the diagonal elements.

[0036] Compared with the prior art, the present invention achieves the following beneficial effects: by establishing a channel model for a RIS-assisted decellularized MIMO system, the channel is divided into direct channels and indirect channels. Based on the channel model, an expression for the downlink achievable rate of the RIS-assisted decellularized MIMO system is derived. Based on the expression for the downlink achievable rate, a joint optimization problem for access point (AP) and hybrid RIS beamforming, as well as active RIS element location selection, is established. The access point (AP) transmit power constraint, hybrid RIS modulus constraint, and the number of active RIS elements are used as constraints for the joint optimization problem. The joint optimization problem is transformed into an access point (AP) active beam matrix optimization problem and a RIS beam matrix and active element location joint optimization problem using Lagrangian dual transformation (LDR) and multidimensional complex quadratic transformation (MCQT). A convex optimization solver is used to alternately solve the problem until convergence to obtain an optimization result. The AP precoding matrix, RIS phase shift, and active element power and location selection are adjusted based on the optimization result to maximize the user sum rate of the RIS-assisted decellularized MIMO system, thereby improving the performance of the decellularized MIMO system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0038] Figure 1 Schematic diagram of the structure of the hybrid RIS-assisted decellularized MIMO system of the present invention;

[0039] Figure 2 This is a graph showing the relationship between the downlink user achievable rates for different numbers of active RIS elements of the present invention;

[0040] Figure 3 This is a relationship diagram of the achievable downlink user rates at different distances between the RIS and the user according to the present invention. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] In order to make the above-mentioned objects, features and effects of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] Example 1: A method for selecting active components in a hybrid RIS-assisted decellularized MIMO system, which is used in a hybrid RIS-assisted decellularized MIMO system. The decellularized MIMO system includes: B access points (APs), K users, and R reconfigurable smart surfaces (RISs). Each AP is equipped with M antennas, and each RIS has N components, including N a active components and NN a passive components. In the communication stage, N a Components equipped with N a One power amplifier acts as an active RIS, while the others act as conventional passive RIS.

[0044] The active component selection method includes the following steps:

[0045] Establishing a channel model for a RIS-assisted decellularized MIMO system, wherein the channel is divided into a direct channel and an indirect channel;

[0046] Based on the channel model, an expression for the downlink achievable rate of the RIS-assisted decellularized MIMO system is derived;

[0047] Based on the expression of the downlink achievable rate, a joint optimization problem of the AP beam matrix, the hybrid RIS beam matrix, and the active RIS element positions is established, and the AP power constraint, the active RIS power constraint, and the RIS unit mode constraint are used as constraints of the joint optimization problem;

[0048] By using Lagrangian dual transformation and multi-dimensional complex quadratic transformation, the joint optimization problem is transformed into an AP beam matrix optimization problem and a hybrid RIS beamforming matrix-active element position optimization problem;

[0049] A convex optimization solver is used to alternately solve the AP active beam matrix optimization problem and the RIS beamforming matrix-active element position optimization problem to obtain the optimization results.

[0050] The AP precoding matrix, RIS phase shift, and active element positions are adjusted based on the optimization results to achieve RIS-assisted decellularized MIMO system to maximize the user sum rate.

[0051] In this embodiment, the channel model is expressed as:

[0052]

[0053] Where, the superscript H represents the conjugate transpose, represents the total cascade channel from the bth access point AP to user k, represents the direct channel from the bth access point AP to user k, represents the indirect channel from the rth RIS to user k, represents the indirect channel from the rth RIS to the bth access point AP. Both the direct channel and the indirect channel obey the Ruili distribution. represents the beamforming matrix of the r-th RIS, It is a binary diagonal (AES) matrix. When the diagonal element is 1, it means that the corresponding RIS element is active, and when it is 0, it means it is passive. and are active RIS and passive RIS diagonal beamforming matrices, I N Represents the N-dimensional identity matrix.

[0054] In this embodiment, the precoded symbol sent by the bth AP is for: ,in is the beamforming vector corresponding to the b-th AP, is the normalized energy transmission symbol of user k. Each AP satisfies the power constraint ,in, is the maximum transmit power of the bth AP.

[0055] The signal received by user k can be expressed as: ,in , is the dynamic noise affected by active RIS components, is the additive white Gaussian noise at user k.

[0056] From this we can get the signal to interference and noise ratio SINR of user k k And the downlink achievable rate R is:

[0057]

[0058]

[0059] in and are the mean square error of the active noise of active RIS and the additive white Gaussian noise at user k, is the precoding matrix of AP for user k.

[0060] The transmission power of the rth RIS is expressed as:

[0061]

[0062] Each RIS meets the power constraints ,in, is the maximum transmission power of RIS.

[0063] In this embodiment, by jointly designing the AP beam matrix W and the hybrid RIS beam matrix and active RIS component locations To maximize the number of users and the rate, a joint optimization problem of access point AP, hybrid RIS beamforming, and active RIS element location selection is established. The AP power constraint, active RIS power constraint, and RIS unit mode constraint are used as constraints for the joint optimization problem. The expression of the joint optimization problem is:

[0064]

[0065] in and are the maximum transmission power of active RIS and AP respectively, is the maximum amplification factor of the active RIS element, and Tr represents the trace of the matrix.

[0066] To facilitate the solution, rewrite the expression as , we get the expression:

[0067] .

[0068] In this embodiment, the Lagrange dual transformation (LDR) is used to separate the logarithms in the objective function and an auxiliary variable is introduced. :

[0069] .

[0070] Given , first Optimize. Partial derivative, the optimal solution is To solve the non-convexity problem of high-dimensional matrix scores, the multidimensional complex quadratic transform (MCQT) is used to rewrite it:

[0071]

[0072] in, is an auxiliary variable, and R represents the real part.

[0073] Similarly, given ,right Optimize. First find the objective function The partial derivative of The optimal solution is: .

[0074] Therefore, given , optimize the AP beam matrix W, and the objective function is rewritten as:

[0075]

[0076] The AP beam matrix , I K represents the K-dimensional identity matrix, I M represents the M-dimensional identity matrix, is a B-dimensional unit column vector, This is a convex optimization problem, and the convex optimization solver cvx can be used to find the optimal solution W. opt .

[0077] Give again , for the hybrid RIS beam matrix and active RIS component locations For optimization, the objective function is rewritten as:

[0078]

[0079] in, It represents a column vector of all 1s, and sum represents the sum of elements. It can be seen that this is a convex optimization problem, and the optimal solution can be obtained by using the convex optimization solver MOSEK. .

[0080] The hybrid RIS-assisted active component selection algorithm for a decellularized MIMO system establishes a channel model for the RIS-assisted decellularized MIMO system, dividing the channel into direct and indirect channels. Based on the channel model, an expression for the achievable downlink rate of the RIS-assisted decellularized MIMO system is derived. Based on this expression for the downlink achievable rate, a joint optimization problem for access point (AP) and hybrid RIS beamforming, as well as active RIS component location selection, is formulated. The AP transmit power constraint, hybrid RIS modulus constraint, and number of active RIS components are used as constraints in the joint optimization problem. Using the Lagrangian duality transformation (LDR) and multidimensional complex quadratic transformation (MCQT), the joint optimization problem is transformed into a joint optimization problem for the AP active beam matrix and the RIS beam matrix and active component locations. A convex optimization solver is used to alternately solve the problem until convergence, obtaining the optimal result. Based on the optimal result, the AP precoding matrix, RIS phase shift, and active component power and location are adjusted to maximize the user sum rate in the RIS-assisted decellularized MIMO system. This maximizes the downlink sum rate and improves the performance of the decellularized MIMO system.

[0081] Example 2: The active component selection method of the hybrid RIS-assisted decellularized MIMO system of the present application is further described in conjunction with simulation experiments.

[0082] like Figure 2 The downlink user and rate graphs for different active element numbers provide a clear reflection of system performance. Simulation parameters are M=4, K=4, B=4, R=2, and N=64. Results show that the proposed dynamic hybrid RIS scheme consistently outperforms the fixed hybrid RIS scheme. This performance gap represents the active element selection gain, while the performance gap between the fixed hybrid RIS scheme and the no-RIS scheme represents the RIS beamforming gain.

[0083] Figure 3 Figure 2 shows the sum rate performance for different distances between the user and the RIS, where M = 4, K = 4, B = 4, R = 4, and N = 64. The results show that the proposed dynamic hybrid RIS scheme consistently outperforms the fixed hybrid RIS scheme, and the active element selection gain decreases as the distance increases.

[0084] Example 3:

[0085] The computer-readable storage medium of this embodiment stores a computer program thereon. When the program is executed by a processor, the steps of the active component selection method of a hybrid RIS-assisted decellularized MIMO system in embodiment 1 are implemented.

[0086] The computer-readable storage medium of this embodiment may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal; the computer-readable storage medium of this embodiment may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, etc. equipped on the terminal; further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device.

[0087] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or is to be output.

[0088] Example 4:

[0089] The computer device of this embodiment includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the active component selection method for a hybrid RIS-assisted decellularized MIMO system in embodiment 1 are implemented.

[0090] In this embodiment, the processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The memory can include read-only memory and random access memory, and provide instructions and data to the processor. A part of the memory can also include non-volatile random access memory. For example, the memory can also store information about the device type.

[0091] Those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above-mentioned technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in various embodiments or certain portions of the embodiments.

[0092] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for selecting active components in a hybrid RIS-assisted decellularized MIMO system, characterized in that: The decellularized MIMO system includes: B access points AP, K users and R reconfigurable smart surfaces RIS, each AP is equipped with M antennas, and each RIS has N elements, including N a active components and NN a passive components; the active component selection method includes the following steps: Establishing a channel model for a RIS-assisted decellularized MIMO system, wherein the channel is divided into a direct channel and an indirect channel; Based on the channel model, an expression for the downlink achievable rate of the RIS-assisted decellularized MIMO system is derived; Based on the expression of the downlink achievable rate, a joint optimization problem of the AP beam matrix, the hybrid RIS beam matrix, and the active RIS element positions is established, and the AP power constraint, the active RIS power constraint, and the RIS unit mode constraint are used as constraints of the joint optimization problem; By using Lagrangian dual transformation and multi-dimensional complex quadratic transformation, the joint optimization problem is transformed into an AP beam matrix optimization problem and a hybrid RIS beamforming matrix-active element position optimization problem; A convex optimization solver is used to alternately solve the AP active beam matrix optimization problem and the RIS beamforming matrix-active element position optimization problem to obtain the optimization results. The optimization results are adjusted to maximize the downlink rate of the RIS-assisted decellularized MIMO system.

2. The method for selecting active components of a hybrid RIS-assisted decellularized MIMO system according to claim 1, wherein: The expression of the channel model is: ； The superscript H represents the conjugate transpose, represents the total cascade channel from the bth access point AP to user k, represents the direct channel from the bth access point AP to user k, represents the indirect channel from the rth RIS to user k, represents the indirect channel from the rth RIS to the bth access point AP. Both the direct channel and the indirect channel obey the Ruili distribution. represents the beamforming matrix of the r-th RIS, It is a binary diagonal matrix. When the diagonal element is 1, it means the corresponding RIS element is active, and when it is 0, it means it is passive. and are active RIS and passive RIS diagonal beamforming matrices, I N Represents the N-dimensional identity matrix.

3. The method for selecting active components of a hybrid RIS-assisted decellularized MIMO system according to claim 2, wherein: The expression of the downlink achievable rate is: ； ； in and are the mean square error of the active noise of active RIS and the additive white Gaussian noise at user k, is the precoding matrix of AP for user k.

4. The method for selecting active components of a hybrid RIS-assisted decellularized MIMO system according to claim 3, wherein: By designing the AP beam matrix W and the hybrid RIS beam matrix and active RIS component locations To maximize the number of users and the rate, the joint optimization problem is expressed as: ； in and are the maximum transmission power of active RIS and AP respectively, is the maximum amplification factor of the active RIS element, and Tr represents the trace of the matrix.

5. The method for selecting active components of a hybrid RIS-assisted decellularized MIMO system according to claim 4, wherein: Use Lagrange dual transformation to separate the logarithms in the objective function and introduce auxiliary variables : ； Given ,right Optimize: Find the objective function Partial derivative, the optimal solution is ; Use multidimensional complex quadratic transformation to solve the non-convexity problem of high-dimensional matrix scores and introduce auxiliary variables : ； Given ,right Optimize: find the objective function Partial derivative, the optimal solution is: ; Where R represents the real part.

6. The method for selecting active components of a hybrid RIS-assisted decellularized MIMO system according to claim 5, wherein: The expression of AP active beam matrix optimization problem is: ； The AP beam matrix , I K represents the K-dimensional identity matrix, I M represents the M-dimensional identity matrix, is a B-dimensional unit column vector, , represents the Kronecker product.

7. The method for selecting active components of a hybrid RIS-assisted decellularized MIMO system according to claim 5, wherein: The expression of the RIS beamforming matrix-active element position optimization problem is: ； in, represents a column vector of all 1s, sum represents the sum of elements, and diag represents the diagonal elements.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the active component selection method for a hybrid RIS-assisted decellularized MIMO system are implemented.

9. A computer device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the active component selection method for a hybrid RIS-assisted decellularized MIMO system according to any one of claims 1 to 7 are implemented.

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