Adaptive Anti-Jamming Resource Allocation Scheme in Dynamic Jamming Environment

This paper proposes an adaptive anti-jamming resource allocation scheme to address both external malicious jamming and internal interference in dynamic multi-user communication environments. The scheme assesses jamming levels through a module based on average channel quality indicator (CQI) and adaptively selects the appropriate anti-jamming policies by utilizing the resouces of power-domain, frequency-domain, or a combination of power-frequency domain. This approach overcomes the limitations of fixed-domain policies in dynamic and complex jamming scenarios. To optimize resource allocation, a discrete soft actor-critic (DSAC) algorithm with an adjustable temperature coefficient is employed, addressing sample inefficiencies in traditional actor-critic methods and enhancing policy robustness. Simulation results show that the proposed scheme improves convergence speed under intelligent jamming (IJ) by 55%, 62%, 65%, 75%, and 60% compared to trusted region policy optimization (TRPO), deep deterministic policy gradient (DDPG), proximal policy optimization (PPO), deep reinforcement learning with dual action network (DRL-DAN) and soft actor critic with fixed temperature coefficient (SAC-FTC)-based schemes, respectively. The scheme achieves evasion rates of 99% against periodic comb jamming (PCJ), 98.7% against sweep jamming (SJ), 95% against combined jamming (CJ), and 90.8% against IJ. When the jammer alternates between regular jamming (RJ) and IJ, the proposed scheme responds quickly, demonstrating robustness in dynamic jamming environments.