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UAV-Aided Cellular Communications with Deep Reinforcement Learning Against Jamming
IEEE Wireless Communications ( IF 12.9 ) Pub Date : 2020-08-18 , DOI: 10.1109/mwc.001.1900207
Xiaozhen Lu , Liang Xiao , Canhuang Dai , Huaiyu Dai

Cellular systems have to resist smart jammers that can optimize their selection of jamming channels and powers based on the estimated ongoing network states. In this article, we present an unmanned aerial vehicle (UAV) aided cellular framework against jamming, in which an UAV uses reinforcement learning to choose the relay policy for a mobile user whose serving base station is attacked by a jammer. More specifically, the UAV applies deep reinforcement learning and transfer learning to help cellular systems resist smart jamming without knowing the cellular topology, the message generation model, the server computation model and the jamming model, based on the previous anti-jamming relay experiences and the observed current communication status. The performance bound in terms of the bit error rate and the UAV energy consumption is derived from the Nash equilibrium of the studied dynamic relay game and verified via simulations. Simulation results show that this scheme can reduce the bit error rate and save the UAV energy consumption in comparison with the benchmark.

中文翻译:

无人机辅助蜂窝通信具有深度增强学习,可防止干扰

蜂窝系统必须抵制智能干扰器,该智能干扰器可以根据估计的持续网络状态优化其干扰信道和功率选择。在本文中,我们提出了一种抗干扰的无人飞行器(UAV)辅助蜂窝框架,其中,UAV使用强化学习为服务基站受到干扰者攻击的移动用户选择中继策略。更具体地说,无人机基于先前的抗干扰中继经验和经验,运用深度强化学习和传递学习来帮助蜂窝系统抵御智能干扰,而无需了解蜂窝拓扑,消息生成模型,服务器计算模型和干扰模型。观察当前的通讯状态。从误码率和无人机能量消耗的角度出发,性能范围是从研究的动态接力游戏的纳什均衡得出的,并通过仿真进行了验证。仿真结果表明,与基准相比,该方案可以降低误码率,节省无人机能耗。
更新日期:2020-08-21
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