无人机辅助边缘计算网络轨迹规划与资源分配研究综述

王侃; 曹铁林; 李旭杰; 李红艳; 李萌; 周墨淼

doi:10.11999/JEIT241071

无人机辅助边缘计算网络轨迹规划与资源分配研究综述

doi: 10.11999/JEIT241071

王侃¹,
曹铁林¹,
李旭杰²,
李红艳^3, ,,
李萌⁴,
周墨淼⁵

1.
西安理工大学计算机科学与工程学院西安 710048
2.
河海大学计算机与软件学院南京 210098
3.
西安电子科技大学通信工程学院西安 710071
4.
北京工业大学信息科学技术学院北京 100124
5.
合肥工业大学计算机与信息学院合肥 230009

基金项目: 国家自然科学基金(61801379, 62371374, 62371012)，北京市自然科学基金(4252001)，安徽省自然科学基金(2408085MF160)

详细信息

作者简介:
王侃：男，副教授，博士，研究方向为算力网络、边缘计算、生成式AI与无线通信、随机优化

曹铁林：男，硕士生，研究方向为算力网络、边缘计算、神经网络

李旭杰：男，教授，博士，研究方向为无线网络、人工智能、边缘计算、车联网、水利信息交叉

李红艳：女，教授，博士，研究方向为异构融合网络、大规模时间确定网络、无线移动自组织网络、无线局域网

李萌：男，副教授博士研究方向为边缘智能与算力网络、区块链技术、网络资源优化管理

周墨淼：男，副教授，博士，研究方向未低时延高可靠车联网、交通信息视觉感知、强化学习辅助智慧交通

通讯作者:
李红艳　hyli@xidian.edu.cn

中图分类号: TN929.5
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- 被引次数: 0
出版历程
- 收稿日期: 2024-12-04
- 修回日期: 2025-03-28
- 网络出版日期: 2025-04-11
- 刊出日期: 2025-05-01

A Survey on Trajectory Planning and Resource Allocation in Unmanned Aerial Vehicle-assisted Edge Computing Networks

1.
School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
2.
College of Computer Science and Software Engineering, Hohai University, Nanjing 210098, China
3.
School of Telecommunications Engineering, Xidian University, Xi’an 71001, China
4.
School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
5.
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China

Funds: The National Natural Science Foundation of China (61801379, 62371374, 62371012), The Natural Science Foundation of Beijing (4252001), The Natural Science Foundation of Anhui Province (2408085MF160)

摘要

摘要: 无人机辅助移动边缘计算(MEC)具有灵活部署、快速响应、广域覆盖、分布计算和可扩展性等优势，在智慧城市、环境监测和应急救援等领域具有广阔应用前景，是提升低空智能网联服务质量的重要研究方向。该文围绕无人机辅助MEC场景的飞行轨迹与资源分配联合优化，从离线优化和在线优化两个维度展开分析：针对离线联合优化，以不同优化性能指标为切入点，从网络场景、性能控制方法和算法设计3个方面梳理研究现状；针对在线联合优化，以优化框架为基础，从网络场景、性能指标和控制方法3个方面梳理研究现状；针对离线与在线混合优化，阐述当前研究成果。最后，聚焦无人机辅助MEC网络与其它网络制式融合时产生的新问题，讨论离线优化环境状态收集、离线优化智能化求解、在线优化多无人机实时协同、在线优化实时信息反馈、无人机能效优化和空-地通信安全保障等关键技术挑战及其未来研究方向。
- 移动边缘计算 /
- 无人机 /
- 轨迹优化 /
- 在线优化 /
- 离线优化
Abstract: Significance Unmanned Aerial Vehicle-assisted Mobile Edge Computing (UAV-MEC) is recognized for its flexible deployment, rapid response, wide-area coverage, and distributed computing capabilities, demonstrating significant potential in smart cities, environmental monitoring, and emergency rescue. Traditional ground-based MEC systems, constrained by fixed edge server deployments, are inadequate for dynamic user distributions and remote area demands. The integration of UAVs with MEC is a critical advancement, where dynamic trajectory planning and resource allocation enhance network energy efficiency, computational efficiency, and service quality, supporting the development of low-altitude intelligent networking. This integration addresses the efficient offloading and real-time processing of computation-intensive tasks through air-ground collaborative optimization, providing foundational technical support for future 6G networks and low-altitude economies. Existing surveys in this field predominantly focus on the integration of UAVs and MEC from a resource allocation perspective, while trajectory planning and its joint optimization with resource allocation are largely overlooked. Furthermore, the distinctions between online and offline optimization are insufficiently addressed in existing surveys, necessitating a systematic analysis of current theories and methods to guide future research. Progress In UAV-MEC, joint optimization of trajectory planning and resource allocation has progressed in both online and offline domains. Algorithm frameworks, including alternating optimization and reinforcement learning, have been shown to effectively balance computational complexity with optimization performance. (1) Offline optimization: (a) Energy efficiency optimization: Existing studies employ alternating optimization methods, such as Block Coordinate Descent (BCD) and Successive Convex Approximation (SCA), as well as heuristic algorithms, including differential evolution and dynamic programming, to jointly optimize trajectories, task offloading, and resource allocation, minimizing energy consumption for both users and UAVs. Further reductions in system energy consumption are achieved by integrating Wireless Power Transfer (WPT) and Reconfigurable Intelligent Surfaces (RIS). (b) Latency optimization: Non-Orthogonal Multiple Access (NOMA) and task scheduling strategies are utilized to minimize user-perceived latency. A multi-UAV collaborative framework based on game theory and reinforcement learning is proposed. (c) Multi-objective optimization: The Dinkelbach method is introduced to address fractional programming problems, facilitating joint optimization of computational efficiency, throughput, and secure capacity. Digital Twin (DT) technology is integrated to approximate global optimality. (2) Online optimization: (a) Lyapunov framework: Long-term stochastic problems are decoupled into per-slot optimizations through temporal decomposition. Convex optimization is combined with dynamic trajectory and resource allocation adjustments to adapt to time-varying channels and user mobility. (b) Reinforcement learning: Multi-Agent Deep Reinforcement Learning (MADRL) is applied to multi-UAV collaboration, with expert knowledge guidance and noise injection incorporated to accelerate algorithm convergence. (3) Hybrid optimization: A “pre-planning + online adjustment” strategy is proposed. In the offline phase, clustering algorithms and particle swarm optimization are used to generate high-quality samples for training Deep Neural Networks (DNNs). In the online phase, incremental learning is applied to dynamically fine-tune DNNs for unknown scenarios, balancing global planning with real-time responsiveness. Conclusions Despite notable advancements, several critical challenges in UAV-assisted MEC remain unresolved: (1) Incomplete future state information: The formulation of offline optimization problems typically assumes full knowledge of environmental state information over future time horizons. However, in multi-UAV scenarios involving multi-dimensional parameters, acquiring complete and accurate state information across extended periods remains difficult, limiting the applicability of offline methods. (2) Real-time multi-UAV coordination: Enhancing system efficiency and task completion quality requires real-time coordination among UAVs. This process demands extensive information exchange within UAV swarms, complex obstacle avoidance, and high-dimensional control adjustments. Collaborative computation offloading and multi-UAV trajectory planning remain challenging due to their inherent complexity. (3) Security vulnerabilities in air-ground links: The line-of-sight propagation and open transmission environment of UAV-assisted MEC networks expose offloaded data to risks such as eavesdropping, data tampering, and signal interference. Current approaches predominantly rely on physical-layer security, whereas active defense mechanisms against emerging threats, including deep-fake signal attacks, are still underdeveloped. (4) Lack of integration across air-space-ground networks: The absence of standardized interfaces and unified cross-domain resource scheduling protocols hinders the coordination of spectrum, computing, and caching resources between satellites and UAV-MEC systems. This limitation restricts the realization of globally orchestrated heterogeneous networks. (5) Energy constraints and service quality tradeoff: UAV endurance directly affects service coverage and operational sustainability. Although energy efficiency is emphasized in both offline and online optimization strategies, a fundamental tradeoff persists between energy consumption and edge service quality. These technical bottlenecks continue to restrict the transition of UAV-MEC from theoretical frameworks to large-scale real-world deployment. Prospects Future research is expected to progress in the following directions: (1) Intelligent environmental perception will advance through the integration of Gated Recurrent Units (GRUs) or Temporal Convolutional Networks (TCNs) for dynamic parameter prediction, while Generative Adversarial Networks (GANs) will be leveraged to complement incomplete environmental state information. (2) Multi-UAV collaboration and energy efficiency will be enhanced through the development of service migration mechanisms and DT-driven MADRL frameworks, combined with solar/WPT technologies to improve UAV endurance. (3) Secure communication mechanisms will evolve with the combination of beamforming, physical-layer security techniques, and homomorphic encryption-based task offloading protocols to address eavesdropping and data tampering in air-ground channels. (4) Heterogeneous network integration will focus on exploring a “cloud-edge-device-satellite” architecture to expand UAV-MEC coverage and robustness in 6G networks, with the development of satellites-assisted cross-domain resource scheduling algorithms. (5) Green computing paradigms will emerge through the integration of energy harvesting and service migration mechanisms to reduce computing loads, promoting sustainable low-altitude intelligent computing ecosystems.
- Mobile Edge Computing (MEC) /
- Unmanned Aerial Vehicle (UAV) /
- Trajectory optimization /
- Online optimization /
- Offline optimization.

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图 1 无人机辅助MEC架构

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图 2 基于离散时间的系统状态转移

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图 3 结合WPT技术的无人机辅助MEC架构

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图 4 结合RIS技术的无人机辅助MEC架构

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图 5 基于无人机辅助MEC的物理层安全通信系统

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图 6 数字孪生辅助的无人机MEC架构

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图 7 云-边-端3层MEC集群架构

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表 1 离线联合优化研究现状

性能指标		性能优化控制方法	无人机数量	文献编号	优化算法
	用户能耗	计算卸载、无人机轨迹、悬停时间	单无人机	[13,14]	启发式、动态规划、差分进化、贪婪算法
能耗	无人机能耗	无人机轨迹、计算卸载、资源分配 (传输功率、CPU频率等)、用户协同	单无人机	[12,15]	启发式、SCA、拉格朗日对偶法、凸优化
	用户与无人机加权能耗	无人机轨迹、计算卸载、资源分配、RIS相位偏移、内容缓存、传输波束赋形、用户分簇	单无人机	[16–21]	动态规划、凸优化、CCCP, BCD, Dinkelbach
时延		资源分配、任务调度、无人机轨迹、计算卸载	文献[22,23]为单无人机文献[24–26]为多无人机	[22–26]	BCD、差分凸规划、贪心、SCA、惩罚CCCP、启发式、聚类
时延与能耗加权和		资源分配、计算卸载、无人机轨迹	多无人机	[27]	博弈论、注水法、梯度下降
计算效率		资源分配(传输功率、CPU频率等)、计算卸载、计算调度、无人机轨迹	单无人机	[28–31]	Dinkelbach, BCD, SCA、凸优化
	吞吐量与信息年龄	无人机轨迹、资源分配、计算卸载	文献[32,34]为单无人机文献[35]为多无人机	[32,34,35]	启发式、动态规划、差分进化、贪婪算法、DDPG, CQL
其它	活跃设备的计算数据量	无人机轨迹、资源分配	单无人机	[33]	启发式、SCA、拉格朗日对偶法、凸优化
	安全容量	无人机轨迹、资源分配(卸载功率、噪声发射功率)、计算卸载	单无人机	[36,37]	动态规划、CCCP, BCD, Dinkelbach

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表 2 在线优化研究现状

优化框架		性能优化控制方法	性能指标	无人机数量	文献
Lyapunov优化		无人机轨迹、任务卸载、资源分配、能量收集	用户体验质量、能耗、吞吐量、时延	文献[42,44]为多无人机，其余为单无人机	[38–45]
强化学习优化	价值学习	无人机轨迹、资源分配、任务卸载、信息年龄、用户调度	安全容量、能耗、时延、卸载任务量	文献[46–49]为单无人机，其余为多无人机	[46–51]
强化学习优化	策略学习	无人机轨迹、资源分配、任务卸载、用户-无人机关联、无人机飞行时间	能耗、时延、公平性、处理成功率、能效	文献[53–55,58,63,65]为多无人机，其余为单无人机	[52–65]
混合时间尺度机制		无人机轨迹、资源分配、任务卸载、内容缓存	时延、能耗、更新年龄	多无人机	[66–68]
在线拍卖框架		无人机轨迹、用户-无人机关联、支付代价	社会成本	多无人机	[69]

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