CN-121984542-A - RIS-assisted RIS phase shift optimization method in honeycomb-free large-scale MIMO and ISAC system

Abstract

The invention discloses an RIS phase shift optimization method in a RIS-assisted honeycomb-free large-scale MIMO and ISAC system, which comprises the steps of establishing an optimization problem related to an RIS phase shift matrix by taking unit module constraint of RIS phase shift and a perceived signal to interference and noise ratio not lower than a preset threshold as constraints, respectively constructing a first auxiliary variable and a second auxiliary variable for the two constraints, decoupling the optimization problem into three sub-optimization problems which are related to each other, and solving the three sub-optimization problems in an alternating and iterative manner, enabling solutions of the RIS phase shift matrix and the first auxiliary variable and the second auxiliary variable to be consistent, and finally obtaining the optimized RIS phase shift configuration meeting the constraints. Compared with the prior art, the method and the device effectively improve the multi-user weighted communication rate while guaranteeing the perception performance by performing auxiliary variable decoupling and alternate iterative optimization on the constraint.

Inventors

• PENG ZHANGJIE
• Sheng Ningguo

Assignees

• 上海师范大学

Dates

Publication Date

20260505

Application Date

20260205

Claims (10)

1. 1. A method of RIS phase shift optimization in a RIS-assisted cellular-free massive MIMO and ISAC system, the system comprising a plurality of distributed access points controlled by a central processing unit, a smart surface, at least one user equipment and at least one target to be perceived, the method comprising: Establishing an optimization problem about the RIS phase shift matrix by taking the multi-user weighted communication rate and maximization as optimization targets and taking the unit modulus constraint of the RIS phase shift and the perceptual signal-to-interference-and-noise ratio not lower than a preset threshold as constraints; Respectively constructing a first auxiliary variable and a second auxiliary variable for the unit module value constraint and the perception signal-to-interference-and-noise ratio constraint, and decoupling the optimization problem into three mutually related sub-optimization problems which are respectively used for optimizing the RIS phase shift matrix, processing the unit module value constraint and the perception signal-to-interference-and-noise ratio constraint; and solving the three sub-optimization problems alternately and iteratively, and enabling the RIS phase shift matrix to be consistent with the solutions of the first auxiliary variable and the second auxiliary variable, so as to obtain an optimized RIS phase shift configuration meeting the unit modulus constraint and the perceived signal-to-interference-and-noise ratio constraint.
2. 2. The RIS phase shift optimization method in the RIS-assisted honeycomb-free massive MIMO and ISAC system according to claim 1, wherein the method is realized by alternately and iteratively solving three sub-optimization problems based on a penalty dual decomposition algorithm, and specifically comprises the steps of introducing Lagrangian multipliers and penalty coefficients, and constructing and solving an augmented Lagrangian sub-problem so that the RIS phase shift matrix is consistent with solutions of the first auxiliary variable and the second auxiliary variable.
3. 3. The RIS-aided cellular-free massive MIMO and ISAC system RIS phase shift optimization method according to claim 2, wherein the three sub-optimization problems specifically include: A first sub-problem of updating the RIS phase shift principal variable under the condition of fixing the first auxiliary variable, the second auxiliary variable and the Lagrangian multiplier, wherein the perceptual constraint is approximated as a linear constraint by a first-order Taylor expansion; A second sub-problem of updating the first auxiliary variable and satisfying the unit modulus constraint under the condition of fixing the RIS phase shift main variable and the Lagrangian multiplier; And a third sub-problem, namely updating the second auxiliary variable under the condition of fixing the RIS phase shift main variable and the Lagrangian multiplier, and meeting the perception constraint after being approximated by first-order Taylor expansion.
4. 4. A RIS-aided cellular-free massive MIMO and ISAC system RIS phase shift optimization method according to claim 3, wherein the objective function form of the second sub-problem is: , Wherein, the To shift the principal variables for vectorized RIS, For the first auxiliary variable, ρ is a penalty coefficient, Λ ̃ 1 is a lagrangian multiplier, and L is the total number of reflection units of the RIS.
5. 5. The method for optimizing RIS phase shift in a RIS-assisted honeycomb-free massive MIMO and ISAC system of claim 4, wherein the optimal solution to the second sub-problem is by a matrix Performing singular value decomposition to obtain the optimal solution, wherein the optimal solution is as follows: , Wherein U and V are respectively left and right singular vector matrixes obtained after singular value decomposition, and I L is an L-order identity matrix.
6. 6. A method for optimizing RIS phase shift in a non-cellular massive MIMO and ISAC system with the assistance of RIS according to claim 3, wherein, in the first and third sub-problems, after the non-convex perceived signal to interference plus noise ratio constraint is approximated to a linear convex constraint by first-order taylor expansion, a convex optimization solver is used to solve.
7. 7. The method for optimizing RIS phase shift in a cellular-free massive MIMO and ISAC system according to claim 1, further comprising, prior to decoupling the optimization problem into three interrelated sub-optimization problems, performing an equivalent transformation on the optimization objective using a split-plan algorithm, introducing an auxiliary variable related to the signal-to-interference-and-noise ratio, and transforming a weighted rate and expression comprising a signal-to-interference-and-noise ratio in the form of a split into a smoothing function with respect to the optimization variable.
8. 8. The method for optimizing RIS phase shift in a RIS-assisted cellular massive MIMO and ISAC system of claim 7, wherein said split-plan algorithm introduces auxiliary variables The smoothing function Expressed as: , Wherein, the For the weight coefficient of the user equipment i, For the total number of user devices, For the signal-to-interference-and-noise-ratio auxiliary variables introduced by the split-plan, For the equivalent communication channel vector of the i-th user, To transmit the precoding vector of the access point to the user equipment i, To transmit the precoding vector for the access point to sense the target, And For the power distribution coefficient(s), To receive noise power.
9. 9. The method for optimizing RIS phase shift in a RIS-assisted cellular-free massive MIMO and ISAC system of claim 8, wherein the equivalent communication channel vector of the ith user is calculated Phase shift matrix with RIS Relation of (2) Substitution into the smoothing function And ignoring the constant term, optimizing RIS phase shift principal variable in the first sub-problem Is equivalent to the following quadratic form: , Wherein, the For vectorized RIS phase shift main variable, A 1 is complex vector, A 2 is positive or semi-positive matrix, its concrete form is determined by equivalent channel, precoding vector, power distribution coefficient and auxiliary variable introduced by the described split-type planning algorithm.
10. 10. The RIS-aided cellular-free massive MIMO and ISAC system of claim 1, wherein the method is solved by alternating iterations, comprising in particular an outer loop and an inner loop: The outer loop is used for updating penalty coefficients and Lagrangian multipliers; And under the fixed punishment coefficient and Lagrangian multiplier, the internal circulation sequentially and alternately solves the three sub-optimization problems until the internal circulation converges.

Description

RIS-assisted RIS phase shift optimization method in honeycomb-free large-scale MIMO and ISAC system Technical Field The invention relates to the technical field of wireless communication, in particular to a RIS phase shift optimization method in a RIS-assisted honeycomb-free large-scale MIMO and ISAC system. Background To meet the urgent demand of the 6G era for higher performance wireless networks, intelligent super surface (RIS) is introduced into a non-cellular massive MIMO and communication awareness Integrated (ISAC) system to cooperatively enhance communication and awareness capabilities by dynamically regulating wireless channels. However, in such complex systems, how to efficiently optimize the RIS phase shift to maximize communication rate while preserving perceived performance remains a challenge. The existing RIS phase shift optimization method mainly has the following limitations that firstly, communication and perception functions are completely separated in modeling, noise interference among the communication and perception functions is not considered, or RIS only assists communication, the sensing function is not participated, ISAC capability is obviously insufficient, secondly, the special scene that targets are too far away from or isolated from an access point AP and RIS is needed to realize sensing is not considered, a key AP-RIS-target-RIS-AP link is lacking, thirdly, the existing optimization algorithm often does not fully consider multi-user weighting rate and equivalence indexes, and a high-efficiency and stable mechanism for processing non-convex unit mode constraint and complex perception constraint is lacking. Through retrieval, chinese patent publication No. CN116582208A discloses a method for optimizing a RIS-assisted spatial correlation de-cellular large-scale MIMO system, which optimizes access point transmit power and RIS phase shift by establishing a spatial correlation channel model and respectively adopting a water injection algorithm and a convex optimization solver so as to maximize users and speed, thereby improving the performance of the system under a correlation channel. However, the scheme is optimized for a pure communication scene, does not relate to integrated sensing and communication functions, and cannot realize sensing capability while guaranteeing communication performance. Therefore, how to jointly optimize the RIS phase shift to ensure the perceived performance and maximize the communication rate in the cellular-free massive MIMO and ISAC system with physical occlusion is a technical problem to be solved. Disclosure of Invention The invention aims to overcome the defects of the prior art and provide a RIS phase shift optimization method in a RIS-assisted honeycomb-free massive MIMO and ISAC system. The aim of the invention can be achieved by the following technical scheme: According to a first aspect of the present invention there is provided a method of RIS phase shift optimization in a RIS assisted cellular free massive MIMO and ISAC system comprising a plurality of distributed access points controlled by a central processing unit, a smart surface, at least one user equipment and at least one target to be perceived, the method comprising: Establishing an optimization problem about the RIS phase shift matrix by taking the multi-user weighted communication rate and maximization as optimization targets and taking the unit modulus constraint of the RIS phase shift and the perceptual signal-to-interference-and-noise ratio not lower than a preset threshold as constraints; Respectively constructing a first auxiliary variable and a second auxiliary variable for the unit module value constraint and the perception signal-to-interference-and-noise ratio constraint, and decoupling the optimization problem into three mutually related sub-optimization problems which are respectively used for optimizing the RIS phase shift matrix, processing the unit module value constraint and the perception signal-to-interference-and-noise ratio constraint; and solving the three sub-optimization problems alternately and iteratively, and enabling the RIS phase shift matrix to be consistent with the solutions of the first auxiliary variable and the second auxiliary variable, so as to obtain an optimized RIS phase shift configuration meeting the unit modulus constraint and the perceived signal-to-interference-and-noise ratio constraint. The method comprises the steps of introducing Lagrangian multipliers and penalty coefficients, and constructing and solving an augmented Lagrangian sub-problem so that the RIS phase shift matrix is consistent with solutions of the first auxiliary variable and the second auxiliary variable. As a preferred technical solution, the three sub-optimization problems specifically include: A first sub-problem of updating the RIS phase shift principal variable under the condition of fixing the first auxiliary variable, the second auxiliary variable and the Lagrangi