Abstract
Intelligent reflective surfaces (IRSs) as low energy consumption and easy to attach devices have been widely applied in the field of anti-jamming recently. In particular, the combination of IRS and unmanned aerial vehicle (UAV), as IRS-UAV, further expands the scope of IRS services. In this paper, the joint IRS selection and beamforming optimization problem has been investigated in multiple IRS-UAV assisted anti-jamming D2D networks. To solve the above optimization problem, a distributed matching-based selection and Q-learning-based beamforming optimization algorithm (DMQ) was proposed. In detail, the optimization problem is decomposed into two subproblems, namely, the IRS selection subproblem is formulated as a non-commutative many-to-many matching game model to describe peer effects and uncertainty selection quotas, and the passive beamforming optimization subproblem is solved by a reinforcement algorithm to satisfy the complex environment. Numerical simulations confirm the convergence and near-optimal performance of the proposed scheme with lower latency and greater robustness.
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