  Malicious jamming launched by smart jammers can attack legitimate
transmissions, which has been regarded as one of the critical security
challenges in wireless communications. With this focus, this paper considers
the use of an 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 a smart jammer, an optimization problem for jointly
optimizing power allocation at the base station (BS), and reflecting
beamforming at the IRS is formulated while considering quality of service (QoS)
requirements of legitimate users. As the jamming model and jamming behavior are
dynamic and unknown, a fuzzy win or learn fast-policy hill-climbing (WoLFPHC)
learning approach is proposed to jointly optimize the anti-jamming power
allocation and reflecting beamforming strategy, where WoLFPHC is capable of
quickly achieving the optimal policy without the knowledge of the jamming
model, and fuzzy state aggregation can represent the uncertain environment
states as aggregate states. Simulation results demonstrate that the proposed
anti-jamming learning-based approach can efficiently improve both the
IRS-assisted system rate and transmission protection level compared with
existing solutions.
