Defeating Reactive Jammers with Deep Dueling-based Deception Mechanism

Conventional anti-jamming solutions like frequency hopping and rate adaptation that are more suitable for proactive jammers are not effective in dealing with reactive jammers. These advanced jammers with recent advances in signal detection can discern the activities of legitimate radios then attack them as soon as the transmission is detected. To combat this type of jammer, we develop an intelligent deception strategy in which the transmitter generates "fake" transmissions to attract the jammer. After that, the transmitter can either harvest energy from the jamming signals or backscatter the jamming signals to transmit data. As such, we can leverage jamming signals to improve the average throughput and reduce the packet loss. To effectively learn from and adapt to the dynamic and uncertainty of jamming attacks, we develop a Markov decision process (MDP) that can dynamically construct two decision epochs in each time slot to capture the special properties of our proposed deception mechanism. The Q-learning algorithm then can be adopted to find the optimal deception strategy for the transmitter. Nevertheless, due to very-slow convergence rates, conventional Q-learning algorithms may not be effective in dealing with smart jamming attacks. We thus develop an advanced deep reinforcement learning model based on deep dueling architecture to quickly obtain the optimal defense policy. Simulation results show that the proposed framework can improve the system throughput up to 173% and reduce the packet loss by 42% compared with other anti-jamming strategies that are not equipped with the proposed deception mechanism