RIS-ADMM: A RIS and ADMM-Based Passive and Sparse Sensing Method With Interference Removal
Peng Chen, Zhimin Chen, Pu Miao, Yun Chen
TL;DR
This paper tackles passive sensing with RIS in ISAC, addressing interference from wireless APs while estimating target DOAs. It replaces computationally heavy SDP-based ANM solvers with a novel RIS-ADMM algorithm that yields closed-form iterative updates and incorporates a robust interference mitigation mechanism. The approach achieves improved DOA estimation accuracy at lower computational cost, demonstrated via simulations that show faster convergence and better performance than FFT, L1, OMP, and SDP baselines, with MUSIC used to extract the DOAs from the reconstructed sparse signal. The work offers practical implications for scalable, hardware-friendly RIS-enabled sensing and anticipates future hardware implementations to bridge theory and practice.
Abstract
Reconfigurable Intelligent Surfaces (RIS) emerge as promising technologies in future radar and wireless communication domains. This letter addresses the passive sensing issue utilizing wireless communication signals and RIS amidst interference from wireless access points (APs). We introduce an atomic norm minimization (ANM) approach to leverage spatial domain target sparsity and estimate the direction of arrival (DOA). However, the conventional semidefinite programming (SDP)-based solutions for the ANM problem are complex and lack efficient realization. Consequently, we propose a RIS-ADMM method, an innovative alternating direction method of multipliers (ADMM)-based iterative approach. This method yields closed-form expressions and effectively suppresses interference signals. Simulation outcomes affirm that our RIS-ADMM method surpasses existing techniques in DOA estimation accuracy while maintaining low computational complexity. The code for the proposed method is available online \url{https://github.com/chenpengseu/RIS-ADMM.git}.