“Jam Me If You Can:” Defeating Jammer With Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications

With conventional anti-jamming solutions like frequency hopping or spread spectrum, legitimate transceivers often tend to “escape” or “hide” themselves from jammers. These reactive anti-jamming approaches are constrained by the lack of timely knowledge of jamming attacks (especially from smart jammers). Bringing together the latest advances in neural network architectures and ambient backscattering communications, this work allows wireless nodes to effectively “face” the jammer (instead of escaping) by first learning its jamming strategy, then adapting the rate or transmitting information right on the jamming signals (i.e., backscattering modulated information on the jamming signals). Specifically, to deal with unknown jamming attacks (e.g., jamming strategies, jamming power levels, and jamming capability), existing work often relies on reinforcement learning algorithms, e.g., <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning. However, the <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning algorithm is notorious for its slow convergence to the optimal policy, especially when the system state and action spaces are large. This makes the <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning algorithm pragmatically inapplicable. To overcome this problem, we design a novel deep reinforcement learning algorithm using the recent dueling neural network architecture. Our proposed algorithm allows the transmitter to effectively learn about the jammer and attain the optimal countermeasures (e.g., adapt the transmission rate or backscatter or harvest energy or stay idle) thousand times faster than that of the conventional <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning algorithm. Through extensive simulation results, we show that our design (using ambient backscattering and the deep dueling neural network architecture) can improve the average throughput (under smart and reactive jamming attacks) by up to 426% and reduce the packet loss by 24%. By augmenting the ambient backscattering capability on devices and using our algorithm, it is interesting to observe that the (successful) transmission rate increases with the jamming power. Our proposed solution can find its applications in both civil (e.g., ultra-reliable and low-latency communications or URLLC) and military scenarios (to combat both inadvertent and deliberate jamming).