Two-dimensional Anti-jamming Mobile Communication Based on Reinforcement Learning
This addresses security and reliability issues for mobile devices in dynamic jamming environments, representing an incremental advancement in anti-jamming techniques.
The paper tackles the problem of anti-jamming in mobile communication by proposing a two-dimensional scheme where devices avoid jammed frequencies or areas, using reinforcement learning to achieve an optimal policy without prior knowledge of jamming or channel models. Simulation results show improvements in signal-to-interference-plus-noise ratio and utility against cooperative jamming compared to benchmarks.
By using smart radio devices, a jammer can dynamically change its jamming policy based on opposing security mechanisms; it can even induce the mobile device to enter a specific communication mode and then launch the jamming policy accordingly. On the other hand, mobile devices can exploit spread spectrum and user mobility to address both jamming and interference. In this paper, a two-dimensional anti-jamming mobile communication scheme is proposed in which a mobile device leaves a heavily jammed/interfered-with frequency or area. It is shown that, by applying reinforcement learning techniques, a mobile device can achieve an optimal communication policy without the need to know the jamming and interference model and the radio channel model in a dynamic game framework. More specifically, a hotbooting deep Q-network based two-dimensional mobile communication scheme is proposed that exploits experiences in similar scenarios to reduce the exploration time at the beginning of the game, and applies deep convolutional neural network and macro-action techniques to accelerate the learning speed in dynamic situations. Several real-world scenarios are simulated to evaluate the proposed method. These simulation results show that our proposed scheme can improve both the signal-to-interference-plus-noise ratio of the signals and the utility of the mobile devices against cooperative jamming compared with benchmark schemes.