A Survey on Air-Ground Networks of Unmanned Aerial Vehicles
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摘要:
无人机具有快速部署、成本低廉等优势. 无人机空地网络通过将基站设备部署至升空无人机平台，能从空中快速构建对地覆盖网络，因而在应急救灾、偏远覆盖、智能交通、智慧城市等方面具有广阔的应用前景，近年来受到广泛关注. 面向无人机空地网络应用场景，结合无人机的机动、组网、载荷等特点，围绕无人机空地网络覆盖性能提升、无人机空地网络通感算一体化设计、智能反射面技术辅助的无人机空地网络、鲁棒无人机空地网络四个维度，从网络场景、关键技术挑战、性能优化控制方法等几方面梳理无人机空地网络的研究现状，并探索优化提升无人机空地网络性能的未来研究方向.
Abstract:Unmanned aerial vehicles (UAVs) can be rapidly and cost-effectively deployed. By deploying the base station equipment to the launching UAV platform, the air-ground networks of UAVs can quickly build ground coverage network from the air, so it has broad application prospects in emergency relief, remote area coverage, intelligent transportation, smart city, and other aspects and has received wide attention in recent years. Based on the application scenario of air-ground networks of UAVs, the characteristics of UAVs’maneuver, network, and load were considered. From the four dimensions of coverage performance improvement of air-ground networks, integrated communication-sensing-calculation design of air-ground networks, reconfigurable intelligent surfaces (RIS)-assisted air-ground networks, and robust air-ground networks of UAVs, the research status of air-ground networks of UAVs was reviewed in terms of network scenarios, key technical challenges, and performance optimization control methods. In addition, the future research direction of optimizing the performance of air-ground networks of UAVs was explored.
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图 2 基于无人机的通感算一体化网络结构
Figure 2. Integrated communication-sensing-computing network architecture of UAVs
表 1 无人机空地网络国内外研究现状汇总
Table 1. Research summary of air-ground UAV networks
场景
描述优化算法 控制手段 性能指标 来源 待进一步解决问题 无人机空地网络覆盖 启发式算法 无人机部署位置、
无人机轨迹用户覆盖百分比 文献[1,13] 现有研究通常针对用户静止场景下，采用传统优化算法实现轨迹与资源控制，以提高网络性能. 当地面用户数量增加且用户出现高移动特性时，如何进一步优化空地双移动约束下的对地网络覆盖性能还需进一步分析 覆盖率 文献[22,24] 凸优化 无人机部署位置、
无人机轨迹、功率覆盖用户数量 文献[14-15] 中断概率 文献[27-30] 聚类算法 无人机基站的三维位置、用户对路径损失的补偿因子 覆盖用户数量 文献[16-17,31] 回声状态网络、深度强化学习 无人机初始部署位置、轨迹、功率控制、带宽 吞吐量（移动用户覆盖问题） 文献[20] 覆盖时间与覆盖率 文献[21] 覆盖效率（覆盖率与覆盖时间比值） 文献[23-26] 基于网络的异构粒子群优化算法 无人机轨迹、带宽 覆盖面积 文献[33] 无人机空地网络通感算一体化 凸优化、聚类 射波束成型、无人机位置、轨迹、飞行速度、用户关联、发射波束成型、感知频率/时间、无人机飞行轨迹 平均用户速率 文献[4,13-15] 现有研究主要围绕通感一体化或通算一体化场景，未充分研究通感算一体化网络设计与优化控制. 此外，在网络控制时所采取的传统优化算法复杂度较高导致其很难适应高度变化的信道场景. 后续研究可针对无人机通感算场景，基于机器学习技术设计低复杂度的无人机通感算一体化网络管控方案 用户关联、功率分配、无人机位置 效用函数 文献[18] 任务卸载比、网络资源、无人机轨迹 能耗 文献[48-49] 深度强化学习 无人机飞行方向、飞行距离、功率分配、信道分配 感知有效性和公
平性文献[19] 任务卸载比、网络资源、无人机轨迹 时延 文献[51] 用户关联、无人机轨迹规划、功率分配 加权频谱效率 文献[52] 无人机轨迹、时隙总数、感知调度 感知目标的平均信息年龄 文献[8] RIS 辅助的无人机空地网络 深度强化学习
算法无人机飞行轨迹、频谱资源、计算资源、智能反射面系数、设备发射功率等 数据新鲜度 文献[61] 当前面向 RIS 辅助的无人机空地网络控制研究通常具有极高的控制复杂度和极大的信息反馈量，如何进一步降低网络控制复杂度并降低网络控制时的信息反馈量，使得其能够更好地适配无线环境变化 能耗 文献[62] 凸优化算法、块坐标下降法 无人机飞行轨迹、频谱资源、计算资源、智能反射面系数、设备发射功率等 能耗 文献[63] 能效 文献[12,65] 鲁棒无人机空地网络 联邦学习 设备选择、无人机部署、子信道分配、传输功率控制 最小化联邦学习模型的收敛时间和学习精度损失 文献[69-71] 当前研究考虑的外部电磁环境相对简单，当面对外部复杂电磁环境与有意干扰情况，如何提升无人机空地网络鲁棒性还需要进一步研究 深度优先搜索算法、连续凸近似、拉格朗日对偶、块坐标下降算法、遗传算法和粒子群算法 用户调度，带宽分配、功率控制、轨迹控制、半径和圆心 能耗效率、最大化每个用户的平均最小传输速率、最大化系统容量、最大化服务用户数 文献[19,72,75-77] 多智能体深度强化学习 飞行轨迹、子载波分配、通信/干扰功率 能耗效率、最大化每个用户的平均最小安全传输速率、最大化每个用户的安全容量 文献[78-81] 
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