摘要翻译：
蜂窝系统容易受到干扰攻击，特别是智能干扰器，它们根据当前通信策略和网络状态选择干扰信道频率和功率等干扰策略。本文提出了一种无人飞行器(UAV)辅助的抗干扰蜂窝通信框架。在该方案中，当服务基站被严重干扰时，无人机使用强化学习方法为蜂窝系统中的移动用户选择中继策略。更具体地说，我们提出了一种基于深度强化学习的无人机中继方案，以帮助蜂窝系统在不知道干扰模型和动态游戏中的网络模型的情况下抵抗智能干扰。该方案在与干扰机进行足够多的交互后，可以达到最优的性能。仿真结果表明，与现有方案相比，该方案可以降低消息的误码率，为蜂窝系统节省能量。
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英文标题：
《UAV-Aided Cellular Communications with Deep Reinforcement Learning
  Against Jamming》
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作者：
Xiaozhen Lu and Liang Xiao and Canhuang Dai and Huaiyu Dai
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最新提交年份：
2019
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Cellular systems are vulnerable to jamming attacks, especially smart jammers that choose their jamming policies such as the jamming channel frequencies and power based on the ongoing communication policies and network states. In this article, we present an unmanned aerial vehicle (UAV) aided cellular communication framework against jamming. In this scheme, UAVs use reinforcement learning methods to choose the relay policy for mobile users in cellular systems, if the serving base station is heavily jammed. More specifically, we propose a deep reinforcement learning based UAV relay scheme to help cellular systems resist smart jamming without being aware of the jamming model and the network model in the dynamic game based on the previous anti-jamming relay experiences and the observed current network status. This scheme can achieve the optimal performance after enough interactions with the jammer. Simulation results show that this scheme can reduce the bit error rate of the messages and save energy for the cellular system compared with the existing scheme.
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PDF链接：
https://arxiv.org/pdf/1805.06628