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.

中文翻译：

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启用缓存的物理层安全游戏，可抵御b5G NOMA网络中的智能uAV辅助攻击

本文研究了在具有两个用户的非正交多路访问（NOMA）网络中启用缓存的物理层安全通信，其中智能无人飞行器（UAV）配备了可以充当多种攻击模式的攻击模块。我们提出一种功率分配策略，以提高传输安全性。为此，我们提出了一种基于强化学习的自适应控制NOMA网络中源站功率分配因子的算法。源站与无人机之间的交互被视为动态游戏。在游戏过程中，源站根据无人机上攻击模块的当前工作模式适当调整功率分配因子。为了最大化收益价值，源站一直在探索不断变化的无线电环境，直到达到纳什均衡（NE）。此外，NE的证明可以证明我们提出的策略是最优的。仿真结果证明了该策略的有效性。