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This paper investigates cache-enabled physical-layer secure communication in a no-orthogonal multiple access (NOMA) network with two users, where an intelligent unmanned aerial vehicle (UAV) is equipped with attack module which can perform as multiple attack modes. We present a power allocation strategy to enhance the transmission security. To this end, we propose an algorithm which can adaptively control the power allocation factor for the source station in NOMA network based on reinforcement learning. The interaction between the source station and UAV is regarded as a dynamic game. In the process of the game, the source station adjusts the power allocation factor appropriately according to the current work mode of the attack module on UAV. To maximize the benefit value, the source station keeps exploring the changing radio environment until the Nash equilibrium (NE) is reached. Moreover, the proof of the NE is given to verify the strategy we proposed is optimal. Simulation results prove the effectiveness of the strategy.

Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks