摘要翻译：
在本文中，我们考虑了一个具有多个蜂窝用户和无人机的单蜂窝网络，其中多个无人机将其收集的数据上传到基站(BS)。考虑了支持多无人机通信的两种传输方式，即无人机到基础设施(U2I)和无人机到无人机(U2U)通信。具体而言，对于U2I链路，信噪比高的无人机通过U2I通信将其收集的数据直接上传到基站，而对于U2I链路，信噪比低的无人机为了服务质量，可以通过下垫U2U通信将数据发送到附近的无人机。首先提出了一种协同的无人机感知发送协议，实现了无人机对X的通信，然后给出了子信道分配和无人机速度优化问题，以最大限度地提高上行链路和速率。为了有效地解决这个NP难问题，我们将其分解为三个子问题:U2I和蜂窝用户(CU)子信道分配、U2U子信道分配和无人机速度优化。提出了一种迭代的子信道分配和速度优化算法(ISASOA)来联合求解这些子问题。仿真结果表明，所提出的ISASOA比贪婪算法可以多上传10%的数据。
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英文标题：
《Cellular UAV-to-X Communications: Design and Optimization for Multi-UAV
  Networks》
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作者：
Shuhang Zhang, Hongliang Zhang, Boya Di, and Lingyang Song
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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：
  In this paper, we consider a single-cell cellular network with a number of cellular users (CUs) and unmanned aerial vehicles (UAVs), in which multiple UAVs upload their collected data to the base station (BS). Two transmission modes are considered to support the multi-UAV communications, i.e., UAV-to-infrastructure (U2I) and UAV-to-UAV (U2U) communications. Specifically, the UAV with a high signal to noise ratio (SNR) for the U2I link uploads its collected data directly to the BS through U2I communication, while the UAV with a low SNR for the U2I link can transmit data to a nearby UAV through underlaying U2U communication for the sake of quality of service. We first propose a cooperative UAV sense-and-send protocol to enable the UAV-to-X communications, and then formulate the subchannel allocation and UAV speed optimization problem to maximize the uplink sum-rate. To solve this NP-hard problem efficiently, we decouple it into three sub-problems: U2I and cellular user (CU) subchannel allocation, U2U subchannel allocation, and UAV speed optimization. An iterative subchannel allocation and speed optimization algorithm (ISASOA) is proposed to solve these sub-problems jointly. Simulation results show that the proposed ISASOA can upload 10\% more data than the greedy algorithm.
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PDF链接：
https://arxiv.org/pdf/1801.05