Active eavesdropper sends jamming signals to raise the transmit power of base stations and steal more information from cellular systems. Network coding resists the active eavesdroppers that cannot obtain all the data flows, but highly relies on the wiretap channel states that are rarely known in wireless networks. In this paper, we present a reinforcement learning (RL) based random linear network coding scheme for drone-aided cellular systems to address eavesdropping. In this scheme, the network coding policy, including the encoded packet number, the packet and power allocation, is chosen based on the measured jamming power, previous transmission performance and BS channel states. A virtual model generates simulated experiences to update Q-values besides real experiences for faster policy optimization. We also propose a deep RL version and design a hierarchical architecture to further accelerate the policy exploration and improve the anti-eavesdropping performance, in terms of the intercept probability, the latency, the outage probability and the energy consumption. We analyze the computational complexity, drone deployment, secure coverage area and the performance bound of the proposed schemes, which are verified via simulation results.

中文翻译：

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基于强化学习的无人机辅助安全无线通信网络编码


主动窃听者发送干扰信号以提高基站的发射功率并从蜂窝系统窃取更多信息。网络编码可以抵御无法获取所有数据流的主动窃听者，但高度依赖于无线网络中很少为人所知的窃听信道状态。在本文中，我们提出了一种基于强化学习（RL）的随机线性网络编码方案，用于无人机辅助蜂窝系统以解决窃听问题。在该方案中，网络编码策略，包括编码分组数量、分组和功率分配，是根据测量的干扰功率、先前的传输性能和BS信道状态来选择的。除了真实经验之外，虚拟模型还可以生成模拟经验来更新 Q 值，以实现更快的策略优化。我们还提出了深度强化学习版本并设计了分层架构，以进一步加速策略探索并在拦截概率、延迟、中断概率和能耗方面提高反窃听性能。我们分析了所提出方案的计算复杂性、无人机部署、安全覆盖区域和性能界限，并通过仿真结果进行了验证。