无人机小型蜂窝网络系统设计与优化控制关键技术研究

Due to small size, high mobility and flexible deployment of unmanned aerial vehicles (UAVs), constructing small cell networks by deploying UAV basestations is able to make good use of three-dimensional space, provide temporary coverage for connection-constrained area and efficiently improve the service range and quality of ground mobile users, which makes the UAV-assisted small cell networks a helpful supplement for conventional ground cellular networks. However, due to the uniqueness of UAV basestations compared to conventional ground basestations, UAV-assisted small cell networks are still facing many challenges in terms of system design and control. Based on the characteristics of UAV-assisted communications, the project focuses on the issues that UAV-assisted small cell networks are facing and investigates system design and optimal control in the aspects of UAV route planning, wireless power transfer and system resource, aiming at efficient spatial synergy between UAV swarms and ground cellular networks in order to enhance network coverage and improve service quality. In terms of research approaches, the project combines theories and techniques in multiply disciplines such as wireless communications, machine learning, optimal control, which accords with development trend of wireless communication systems, and the results will promisingly provide solid communication support for application scenarios such as unexpected emergency and disaster relief.

由于无人机具有体积小、可移动性强以及部署灵活等特点，部署无人机基站组成小型蜂窝网络能够充分利用三维空间，为连接受限地区提供临时的通信覆盖并有效改善地面移动用户的服务范围和质量，使其成为传统地面蜂窝网络的有益补充。然而，由于无人机基站相对于传统地面基站的独特性，无人机小型蜂窝网络在系统设计和控制上仍然面临着诸多挑战。本项目针对无人机辅助通信的特点，着重围绕无人机小型蜂窝网络所面临的问题，从无人机路径规划、无线能量传输、系统资源分配等多个方面深入研究系统设计与优化控制技术，旨在实现无人机群和地面蜂窝网络在空间上的高效协同作用，以增强网络覆盖，提升用户的通信服务质量。本项目在研究方法上将结合无线通信、机器学习、最优控制等多个学科的理论与技术，符合未来无线通信系统的发展趋势，其研究成果有望为突发事件和灾害救援等应用场景提供有力的通信保障。

本项目针对无人机辅助通信的特点，着重围绕无人机蜂窝网络所面临的问题，从无人机路径规划与部署，以及无线能量传输的高效利用两个方面，增强现有蜂窝网络覆盖，提升用户的通信服务质量。在项目进行过程中，这两部分内容通常被联合考虑。.项目组针对能量受限的无线蜂窝网络研究了在用户间的功率和时间分配方面的上行链路资源分配以优化上行链路总速率；针对多用户下行通信系统，提出了无人机基站折线飞行轨迹来优化下行总速率；针对具有一对源点和目的点的典型协作通信网络，研究了信息与能量传输方案与无人机移动轨迹的联合优化方法；针对基于无线能量传输的无人机辅助下行蜂窝网络，研究了资源分配和无人机基站部署的联合优化方法；针对可充电无人机辅助无线网络，研究了下行资源分配与路径规划的联合优化方法；针对用户方信息（如位置，发射功率及信道参数）无法获得的情况，基于强化学习技术研究了无人机移动轨迹的智能设计方法。.项目组圆满完成了项目任务。