Method and apparatus for adaptive anti-jamming communications based on deep double-q reinforcement learning

Abstract

In order to avoid various jamming attacks from intelligent jammers in modern complex wireless environments, a system and method is presented for a user radio to generate and implement an adaptive anti-jamming communication strategy. The said adaptive anti-jamming communication strategy is obtained via the training process for a specific neural network using Deep Double-Q Reinforcement learning algorithm in the strategy generation phase. The objective of this process is to discover a strategy to select the optimal radio action including transmission channel and transmission power for the user radio, which is changed adaptively to different jamming patterns to maximize the successful transmission rate ('jamming-free') while retaining the power consumption of user radio as low as possible. In the strategy implementation phase, the user radio chooses an appropriate radio action based on output of trained neural network after the training process; thus, achieves robust and efficient communications against diverse complex jamming scenarios.