Intelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning

TL;DR

This paper proposes a reinforcement learning-based method to optimize IRS-assisted anti-jamming strategies, significantly improving wireless communication security and performance against smart jammers without prior knowledge of jamming behavior.

Contribution

It introduces a novel WoLF-PHC learning approach for joint power and beamforming optimization in IRS-assisted systems under dynamic jamming conditions.

Findings

Enhanced system rate and transmission security demonstrated in simulations.

Effective anti-jamming performance without prior jamming model knowledge.

Outperforms existing anti-jamming solutions.

Abstract

Malicious jamming launched by smart jammer, which attacks legitimate transmissions has been regarded as one of the critical security challenges in wireless communications. Thus, this paper exploits intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated. As the jamming model and jamming behavior are dynamic and unknown, a win or learn fast policy hill-climbing (WoLF-PHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy without the knowledge of the jamming model. Simulation results demonstrate that the proposed anti-jamming based-learning approach can efficiently improve both the IRS-assisted system rate and transmission protection level compared with existing solutions.