Joint Optimization of Cooperative Beamforming and Power Allocation with Active RIS-assisted Integrated Sensing and Communication

Li Qitao; Wang Feng; Ding Ke

doi:10.12052/gdutxb.240122

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Joint Optimization of Cooperative Beamforming and Power Allocation with Active RIS-assisted Integrated Sensing and Communication

更新时间：2026-05-27

- Joint Optimization of Cooperative Beamforming and Power Allocation with Active RIS-assisted Integrated Sensing and Communication
- Vol. 42, Issue 6, Pages: 104-115(2025)
- 作者机构：
  
  1. 广东工业大学信息工程学院,广东,广州,510006
  2. 佛山显扬科技公司,广东,佛山,528299
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.240122
  CLC： TN929.5
- Published：2025
- 稿件说明：
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黎骑滔, 王丰, 丁克. 有源RIS辅助的通感一体化协作波束赋形与功率分配优化设计[J]. 广东工业大学学报, 2025, 42(6): 104–115.

Li Qitao, Wang Feng, Ding Ke. Joint Optimization of Cooperative Beamforming and Power Allocation with Active RIS-assisted Integrated Sensing and Communication[J]. 2025, 42(6): 104-115.
黎骑滔, 王丰, 丁克. 有源RIS辅助的通感一体化协作波束赋形与功率分配优化设计[J]. 广东工业大学学报, 2025, 42(6): 104–115. DOI： 10.12052/gdutxb.240122

Li Qitao, Wang Feng, Ding Ke. Joint Optimization of Cooperative Beamforming and Power Allocation with Active RIS-assisted Integrated Sensing and Communication[J]. 2025, 42(6): 104-115. DOI： 10.12052/gdutxb.240122.

摘要

多可重构智能表面(Reconfigurable Intelligent Surface

RIS)辅助的通信感知一体化系统(Integrated Sensing and Communication，ISAC)是未来6G网络的关键技术。本文研究协作式双有源RIS辅助的ISAC系统，利用双反射链路实现基站与处于非视距区域的多用户通信和多目标感知任务。以最大化最小感知波束图增益为准则，建模通信服务质量、感知均方互相关，以及基站和有源RIS发射功率等约束条件，构建基站的通信波束赋形与感知协方差矩阵，以及有源RIS的反射系数矩阵和放大功率分配等关键参数的联合优化问题。由于基站的发射波束赋形矢量及感知协方差矩阵与2个有源RIS的反射系数矩阵呈现强耦合特点，该联合优化问题属于一类非凸优化问题，求解复杂度高。为此，设计一种分布式迭代次优算法方案，将联合优化问题解耦为3个子优化问题，在基站侧优化通信波束赋形、感知协方差矩阵，以及有源RIS放大功率分配，并在2个有源RIS侧优化各自的反射系数矩阵。仿真结果表明，本文所提的分布式优化算法具有良好的收敛性能，且相比于其他方案，在系统功率预算和反射面单元数相同的条件下，通过优化2个有源RIS之间的放大功率分配能够提升约2 dB的波束图增益，实现更高的功率利用效率。

Abstract

The reconfigurable intelligent surface (RIS) -assisted integrated sensing and communication (ISAC) system is a key technology for future 6G networks. A cooperative dual-active RIS-assisted ISAC system is studied

utilizing dual-reflection links to achieve both communication between a base station and multiple users in non-line-of-sight areas and multi-target sensing tasks. To maximize the minimum sensing beampattern gain

the communication quality of service

sensing mean square cross-correlation

and power constraints for both the base station and active RIS were modeled

forming a joint optimization problem for key parameters including the communication beamforming and sensing covariance matrix at the base station

and the reflection coefficient matrix and amplification power allocation of the active RIS. Due to the strong coupling between the beamforming vectors

sensing covariance matrix at the base station

and the reflection coefficient matrices of the two active RISs

this joint optimization problem is non-convex and highly complex to solve. Therefore

a distributed iterative suboptimal algorithm was designed to decouple the joint optimization into three subproblems: optimizing the communication beamforming

sensing covariance matrix

and active RIS amplification power allocation at the base station

and optimizing the reflection coefficient matrices at both active RISs. The simulation results demonstrate that the proposed distributed optimization algorithm exhibits good convergence performance. Compared with other schemes

optimizing the amplification power allocation between the two active RIS units can improve the beampattern gain by approximately 2 dB under the same system power budget and number of RIS elements. This leads to higher power utilization efficiency.

关键词

Keywords

references

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