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Malicious jamming launched by smart jammers can attack legitimatetransmissions, which has been regarded as one of the critical securitychallenges in wireless communications. With this focus, this paper considersthe use of an intelligent reflecting surface (IRS) to enhance anti-jammingcommunication performance and mitigate jamming interference by adjusting thesurface reflecting elements at the IRS. Aiming to enhance the communicationperformance against a smart jammer, an optimization problem for jointlyoptimizing power allocation at the base station (BS), and reflectingbeamforming at the IRS is formulated while considering quality of service (QoS)requirements of legitimate users. As the jamming model and jamming behavior aredynamic and unknown, a fuzzy win or learn fast-policy hill-climbing (WoLFPHC)learning approach is proposed to jointly optimize the anti-jamming powerallocation and reflecting beamforming strategy, where WoLFPHC is capable ofquickly achieving the optimal policy without the knowledge of the jammingmodel, and fuzzy state aggregation can represent the uncertain environmentstates as aggregate states. Simulation results demonstrate that the proposedanti-jamming learning-based approach can efficiently improve both theIRS-assisted system rate and transmission protection level compared withexisting solutions.

Intelligent Reflecting Surface Assisted Anti-Jamming Communications: A Fast Reinforcement Learning Approach