Fast Reinforcement Learning for Anti-jamming Communications
This addresses the challenge of efficient anti-jamming for wireless devices, but it appears incremental as it builds on standard reinforcement learning techniques with modifications.
The paper tackles the problem of anti-jamming in wireless communications by proposing a fast reinforcement learning algorithm that combines previous action selection with ε-greedy exploration, achieving higher signal-to-interference-plus-noise ratio and faster convergence compared to existing methods like Q-learning and DQN variants.
This letter presents a fast reinforcement learning algorithm for anti-jamming communications which chooses previous action with probability $τ$ and applies $ε$-greedy with probability $(1-τ)$. A dynamic threshold based on the average value of previous several actions is designed and probability $τ$ is formulated as a Gaussian-like function to guide the wireless devices. As a concrete example, the proposed algorithm is implemented in a wireless communication system against multiple jammers. Experimental results demonstrate that the proposed algorithm exceeds Q-learing, deep Q-networks (DQN), double DQN (DDQN), and prioritized experience reply based DDQN (PDDQN), in terms of signal-to-interference-plus-noise ratio and convergence rate.