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CN-121984548-A - RIS-assisted honeycomb-free large-scale MIMO and ISAC system precoding optimization method

CN121984548ACN 121984548 ACN121984548 ACN 121984548ACN-121984548-A

Abstract

The invention discloses a precoding optimization method in RIS-assisted honeycomb-free large-scale MIMO and ISAC systems, which comprises the steps of establishing a joint optimization problem about precoding vectors by taking multiuser weighted communication rate and maximization as optimization targets and taking constraint of power of each access point and a perception signal to interference and noise ratio not lower than a preset threshold as constraint, converting the joint optimization problem through a split-type planning FP algorithm, decoupling the joint optimization problem into a communication precoding sub-problem and a perception precoding sub-problem based on an alternative optimization framework, and updating and outputting the optimized communication precoding vector and the perception precoding vector to each access point by alternatively and iteratively solving the two sub-problems. Compared with the prior art, the method combines the communication and the perception pre-coding through the method of combining the split planning and the alternative optimization, and improves the multi-user communication rate on the premise of ensuring the perception performance and the power 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 precoding 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 a joint optimization problem about a precoding vector by taking multi-user weighted communication rate and maximization as optimization targets and taking each access point transmission power constraint and a perceived signal to interference and noise ratio not lower than a preset threshold as constraints; Converting the objective function of the joint optimization problem through a split planning FP algorithm to obtain a smoothed objective function; Based on the smoothed objective function and the constraint, an alternating optimization framework is adopted, the transformed joint optimization problem is decoupled into a communication precoding sub-problem and a sensing precoding sub-problem, and the communication precoding sub-problem is solved in an alternating iteration mode, wherein in each iteration, a sensing precoding vector is fixed to solve the communication precoding sub-problem, and a communication precoding vector is fixed to solve the sensing precoding sub-problem; updating and outputting the optimized communication precoding vector and the optimized sensing precoding vector, and configuring the communication precoding vector and the sensing precoding vector to each access point.
  2. 2. The method for optimizing precoding in a RIS-aided non-cellular massive MIMO and ISAC system according to claim 1, wherein the optimization objective is: , Wherein, the For the weight coefficient of the i-th user equipment, For the communication signal-to-interference-and-noise ratio of the ith user equipment, To transmit the precoding vector of the access point to the user equipment i, Precoding vectors for transmitting access point-to-target sensing.
  3. 3. The method of precoding optimization in RIS-assisted cellular-free massive MIMO and ISAC systems of claim 1, wherein the access point transmit power constraint is such that for a kth transmitting access point its total transmit power does not exceed a preset maximum transmit power threshold 。
  4. 4. The method for optimizing precoding in RIS-assisted honeycomb-free massive MIMO and ISAC system according to claim 1, wherein the objective function of the joint optimization problem is transformed by a split-plan FP algorithm, and specifically comprising: Introducing a first auxiliary variable And a second auxiliary variable Converting the optimization target into a smoothed objective function : , Wherein, the For the weight coefficient of the user equipment i, For the equivalent communication channel vector of the i-th user, For the total number of user devices, 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), Is the noise power.
  5. 5. The method of precoding optimization in a RIS-assisted non-cellular massive MIMO and ISAC system of claim 4, wherein in each iteration the first auxiliary variable And a second auxiliary variable Updating according to the precoding vector of the current iteration, specifically: , , Wherein, the For the weight coefficient of the i-th user equipment, For the communication signal-to-interference-and-noise ratio of the ith user equipment, 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, For the equivalent communication channel vector of the i-th user, And For the power distribution coefficient(s), Is the noise power.
  6. 6. The method for optimizing precoding in RIS-assisted honeycomb-free massive MIMO and ISAC systems according to claim 4, wherein solving the perceptual precoding sub-problem comprises: Based on the smoothed objective function under the condition of fixed communication precoding vector Constructing a sensing precoding sub-problem which aims at minimizing sensing signal interference items received by each user equipment and meets the constraint of transmitting power and sensing signal-to-interference-and-noise ratio; Converting the non-convex constraint in the perceptual pre-coding problem into a convex constraint by using first-order Taylor expansion; And solving the converted perceptual precoding sub-problem by adopting a convex optimization solver to obtain an updated perceptual precoding vector.
  7. 7. The method for optimizing precoding in a RIS-aided non-cellular massive MIMO and ISAC system according to claim 4, wherein solving the communication precoding sub-problem specifically comprises: Under the condition of fixed perception precoding vectors, combining the communication precoding vectors of all user equipment into a communication precoding matrix; based on the smoothed objective function Constructing a communication precoding problem aiming at maximizing the difference between a linear term and a quadratic term and meeting the constraint of transmitting power and the constraint of perceived signal-to-interference-and-noise ratio, And solving the communication precoding sub-problem by adopting a convex optimization solver to obtain an updated communication precoding matrix, and recovering the communication precoding vector of each user equipment from the updated communication precoding matrix.
  8. 8. The method for optimizing precoding in RIS-assisted honeycomb-free massive MIMO and ISAC system as recited in claim 7, wherein the objective function form of the second sub-optimization problem is , wherein, For the communication of the pre-coding matrix, And Is a matrix related to the channel and auxiliary variables.
  9. 9. The method for optimizing precoding in RIS-assisted cellular massive MIMO and ISAC systems according to claim 1, wherein the iteration termination condition of the alternating optimization is that the difference between objective function values obtained by two consecutive iterations is smaller than a preset tolerance or the number of iterations reaches a preset maximum number of iterations.
  10. 10. The method of claim 1, further comprising initializing the perceptual precoding vector and a communication precoding vector prior to starting the alternate iterative optimization, wherein the perceptual precoding vector is initialized to a beamforming vector pointing in a direction of a target to be perceived, and the communication precoding vector is initialized to a maximum ratio transmission precoding vector based on channel state information of each user.

Description

RIS-assisted honeycomb-free large-scale MIMO and ISAC system precoding optimization method Technical Field The invention relates to the technical field of wireless communication, in particular to a precoding optimization method in a RIS-assisted honeycomb-free large-scale MIMO and ISAC system. Background In order to meet the urgent demand of the 6G era for higher-performance wireless networks, intelligent super surface (RIS) is introduced into a cellular-free large-scale MIMO and communication perception Integrated (ISAC) system, and in a complex deployment environment, a direct propagation path between a base station and a target, a user is often blocked physically by obstacles such as buildings, topography and the like, so that a perception and communication link is interrupted. The RIS can intelligently construct a reflection path through dynamically regulating and controlling a wireless channel, so that shielding and cooperative enhancement of communication and perception capability are overcome. In this architecture, precoding design is the core that affects overall system performance, requiring co-scheduling of communication and perceived waveforms within a unified framework to address challenges presented by distributed nodes, reconfigurable channels, and shared interference. The existing precoding optimization for RIS-assisted honeycomb-free large-scale MIMO and ISAC systems mainly has the following limitations that firstly, communication and perception functions are completely separated in modeling, interference of perception signals to communication users and influence of communication signals to perception echoes are not fully considered, so that mismatching between a model and an actual propagation environment is caused, secondly, the balance of communication perception overall efficiency cannot be achieved by taking system weighted sum rate as a center by adopting a simplified maximum-minimum fairness criterion or a single performance index, thirdly, because an objective function is not convex and constraint coupled, the existing algorithm is high in complexity and poor in convergence, and real-time configuration and large-scale deployment are difficult to support. Through retrieval, chinese patent publication No. CN119921816A discloses a method for optimizing a fluid antenna position and a radar communication precoding matrix in a URLLC scene, by establishing an optimization model with maximized radar perception signal-to-noise ratio as a core and total power of a base station, URLLC time delay and the fluid antenna position as constraints, and adopting an alternative optimization algorithm to jointly solve the antenna position, the radar precoding matrix and the communication precoding matrix, the collaborative design of communication and perception functions in a single base station scene is realized. However, the precoding optimization design of the method is tightly coupled to the mechanical position adjustment of the fluid antenna, engineering realization is complex and dynamic is limited, the method takes the perception performance as a single center target, fairness and efficiency of multi-user communication rate are difficult to consider, the whole alternating optimization process of the method relates to multiple types of strong coupling variables and complex constraint processing, and calculation cost is high. Therefore, how to optimize communication and sensing precoding vectors in a cellular-free large-scale MIMO and ISAC system with physical shielding to maximize communication rate while guaranteeing sensing performance is a technical problem to be solved. Disclosure of Invention The invention aims to overcome the defects of the prior art and provide a precoding 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 precoding 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 a joint optimization problem about a precoding vector by taking multi-user weighted communication rate and maximization as optimization targets and taking each access point transmission power constraint and a perceived signal to interference and noise ratio not lower than a preset threshold as constraints; Converting the objective function of the joint optimization problem through a split planning FP algorithm to obtain a smoothed objective function; Based on the smoothed objective function and the constraint, an alternating optimization framework is adopted, the transformed joint optimization problem is decoupled into a communication precoding sub-problem and a sensing precoding