摘要翻译：
认知无线电网络中授权频谱的有效利用由于次级用户之间缺乏协调而面临挑战。文献中提出的分布式算法旨在通过保证SUS的正交信道分配来最大限度地提高网络吞吐量。然而，这些算法的工作假设是所有的SUs忠实地遵循算法，但由于网络的分散性质，这些算法可能并不总是成立。本文研究了对恶意行为（干扰攻击）具有鲁棒性的分布式算法。我们考虑干扰机发动协同攻击和不协同攻击的情况。在协同攻击中，干扰者在每个时隙选择不重叠的信道进行攻击，可以显著增加SUS的冲突次数。我们将每个场景中的问题设置为多玩家强盗，并开发算法。分析表明，当SUs忠实地实现所提出的算法时，后悔概率是恒定的。我们通过详尽的综合实验和基于USRP的实际实验验证了我们的观点。
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英文标题：
《Learning to Coordinate in a Decentralized Cognitive Radio Network in
  Presence of Jammers》
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作者：
Suneet Sawant, Rohit Kumar, Manjesh K. Hanawal and Sumit J. Darak
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最新提交年份：
2018
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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：
  Efficient utilization of licensed spectrum in the cognitive radio network is challenging due to lack of coordination among the Secondary Users (SUs). Distributed algorithms proposed in the literature aim to maximize the network throughput by ensuring orthogonal channel allocation for the SUs. However, these algorithms work under the assumption that all the SUs faithfully follow the algorithms which may not always hold due to the decentralized nature of the network. In this paper, we study distributed algorithms that are robust against malicious behavior (jamming attack). We consider both the cases of jammers launching coordinated and uncoordinated attacks. In the coordinated attack, the jammers select non-overlapping channels to attack in each time slot and can significantly increase the number of collisions for SUs. We setup the problem in each scenario as a multi-player bandit and develop algorithms. The analysis shows that when the SUs faithfully implement proposed algorithms, the regret is constant with high probability. We validate our claims through exhaustive synthetic experiments and also through a realistic USRP based experiments.
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PDF链接：
https://arxiv.org/pdf/1803.0681