Unmanned aerial vehicles (UAVs) that are widely utilized for video capturing, processing and transmission have to address jamming attacks with dynamic topology and limited energy. In this paper, we propose a reinforcement learning (RL)-based UAV anti-jamming video transmission scheme to choose the video compression quantization parameter, the channel coding rate, the modulation and power control strategies against jamming attacks. More specifically, this scheme applies RL to choose the UAV video compression and transmission policy based on the observed video task priority, the UAV-controller channel state and the received jamming power. This scheme enables the UAV to guarantee the video quality-of-experience (QoE) and reduce the energy consumption without relying on the jamming model or the video service model. A safe RL-based approach is further proposed, which uses deep learning to accelerate the UAV learning process and reduce the video transmission outage probability. The computational complexity is provided and the optimal utility of the UAV is derived and verified via simulations. Simulation results show that the proposed schemes significantly improve the video quality and reduce the transmission latency and energy consumption of the UAV compared with existing schemes.

中文翻译：

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电动/混合动力电动飞机推进系统


广泛用于视频捕获、处理和传输的无人机（UAV）必须应对动态拓扑和有限能量的干扰攻击。在本文中，我们提出了一种基于强化学习（RL）的无人机抗干扰视频传输方案，以选择视频压缩量化参数、信道编码率、针对干扰攻击的调制和功率控制策略。更具体地说，该方案应用强化学习根据观察到的视频任务优先级、无人机控制器信道状态和接收到的干扰功率来选择无人机视频压缩和传输策略。该方案使得无人机能够在不依赖干扰模型或视频服务模型的情况下保证视频体验质量（QoE）并降低能耗。进一步提出了一种基于强化学习的安全方法，利用深度学习加速无人机学习过程并降低视频传输中断概率。提供了计算复杂度，并通过仿真推导和验证了无人机的最佳效用。仿真结果表明，与现有方案相比，所提出的方案显着提高了视频质量，降低了无人机的传输延迟和能耗。