Anti-Jamming Optimization for EM-Compliant Active RIS via Decoupling Architecture

TL;DR

This paper introduces an EM-compliant active RIS model that explicitly accounts for electromagnetic effects like mutual coupling, and proposes an efficient optimization algorithm to enhance anti-jamming performance in wireless systems.

Contribution

It develops a realistic EM-compliant RIS model incorporating mutual coupling and impedance mismatches, and proposes a decoupling architecture-based optimization algorithm for anti-jamming.

Findings

The decoupling architecture effectively eliminates mutual coupling effects.

The AO algorithm significantly reduces optimization complexity.

Numerical results show improved anti-jamming performance.

Abstract

Wireless communication systems are increasingly vulnerable to sophisticated jamming attacks with the rapid evolution of jamming technologies and advanced signal processing techniques. While traditional anti-jamming techniques offer limited performance gains, active reconfigurable intelligent surfaces (RISs) have emerged as a promising channel-domain solution for improving resilience against jamming. Nonetheless, existing studies often rely on simplified electromagnetic (EM) models that do not fully capture mutual coupling (MC) and impedance mismatches in RIS hardware. In this paper, we propose an EM-compliant active (EMC-Active) RIS model for anti-jamming systems, explicitly incorporating the EM and physical properties at active RIS, such as MC effects, channel correlation, and discrete phase. To evaluate the anti-jamming performance of the proposed EMC-Active RIS, we develop a low-complexity alternating optimization (AO) algorithm based on the decoupling architecture (DA) to maximize the ergodic achievable rate. By leveraging the DA to explicitly eliminate MC effects among REs, the original coupled system is transformed into a tractable and scalable uncoupled representation. Numerical results demonstrate that the DA-based AO algorithm can significantly reduce the modeling and optimization complexity and efficiently solve the problem in an alternating manner with substantially reduced iteration overhead.