Collaborative UFH-based anti-jamming broadcast with learning

Uncoordinated frequency hopping (UFH) technique has been developed to address smart jamming attacks in wireless networks, in which the receiver does not need to know the pre-shared physical-layer secret keys such as the frequency hopping pattern with the transmitter and thus cannot be efficiently blocked by smart jammers that eavesdrop the public control channel of the network. However, collaborative UFH-based broadcast (CUB) that exploits both the spatial and frequency diversity still suffers from a low communication efficiency, because the probability that a receiver happens to use the same channel with a transmitter with a random channel selection is very low. In this paper, we propose a collaborative UFH-based broadcast scheme based on reinforcement learning to further improve the communication efficiency against smart jamming. More specifically, by applying Q-learning algorithm, a radio node can achieve the optimal transmit strategy via trials in the repeated game without being aware of the jamming model and the network model. Simulation results show that the Q-learning based anti-jamming broadcast can significantly decrease the broadcast delay and reduce the total energy cost, compared with the CUB scheme.