Drone-Aided Network Coding for Secure Wireless Communications: A Reinforcement Learning Approach

This study investigates how base stations (BSs) apply network coding to protect the downlink data and how drones relay the coded packets to resist active eavesdropping that performs jamming to induce the BS to raise the transmit power and thus steal more data. We present a drone-aided network coding framework for secure downlink transmission, which incorporates a random linear network coding algorithm to encode the BS messages against active eavesdropping. This framework designs a model-based reinforcement learning to choose the BS network coding and transmission policy based on the jamming power sent by the active eavesdropper, the previous transmission performance, and the BS channel states without the prior knowledge of the drone-eavesdropper channel states. The learning parameters such as the Q-values are updated by the real experiences in the downlink transmission process besides the simulated experiences that are generated from the virtual model in the designed Dyna architecture. Simulation results show that our proposed scheme outperforms the benchmarks in terms of the intercept probability, the transmission performance, and the BS energy consumption.