摘要翻译：
研究了一种新的无人机与地面无线通信系统频谱共享方案。我们考虑一个认知/辅助无人机发射机与一个地面辅助接收机(SR)通信时，存在多个在同一频带上工作的主要地面通信链路。通过轨迹设计，利用无人机的可控机动性，在控制各主接收机的同信道干扰的同时，提高无人机的认知通信性能。特别是，在有限的任务/通信周期内，我们通过联合优化无人机轨迹和发射功率分配来最大化从无人机到SR的平均可达到速率，这受制于无人机的最大速度、初始/最终位置和平均发射功率的约束，以及在每个PRs上施加的一组干扰温度(IT)约束，以保护它们的通信。然而，联合轨迹和功率优化问题是非凸的，因此很难得到最优解。为了解决这一问题，我们提出了一种有效的算法，通过交替优化和逐次凸逼近(SCA)技术，确保获得局部最优解。数值结果表明，与基准方案相比，我们提出的无人机航迹和功率联合控制方案显著提高了认知无人机通信系统的可达率。
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英文标题：
《Cognitive UAV Communication via Joint Trajectory and Power Control》
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作者：
Yuwei Huang, Jie Xu, Ling Qiu, Rui Zhang
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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：
  This paper investigates a new spectrum sharing scenario between unmanned aerial vehicle (UAV) and terrestrial wireless communication systems. We consider that a cognitive/secondary UAV transmitter communicates with a ground secondary receiver (SR), in the presence of a number of primary terrestrial communication links that operate over the same frequency band. We exploit the UAV's controllable mobility via trajectory design, to improve the cognitive UAV communication performance while controlling the co-channel interference at each of the primary receivers (PRs). In particular, we maximize the average achievable rate from the UAV to the SR over a finite mission/communication period by jointly optimizing the UAV trajectory and transmit power allocation, subject to constraints on the UAV's maximum speed, initial/final locations, and average transmit power, as well as a set of interference temperature (IT) constraints imposed at each of the PRs for protecting their communications. However, the joint trajectory and power optimization problem is non-convex and thus difficult to be solved optimally. To tackle this problem, we propose an efficient algorithm that ensures to obtain a locally optimal solution by applying the techniques of alternating optimization and successive convex approximation (SCA). Numerical results show that our proposed joint UAV trajectory and power control scheme significantly enhances the achievable rate of the cognitive UAV communication system, as compared to benchmark schemes.
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PDF链接：
https://arxiv.org/pdf/1802.0509