Intelligent Resource Allocation for Multi-UAV Communication Systems: Architectures, Models, and Challenges
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摘要: 随着低空经济、应急通信等行业的快速发展，多无人机通信系统在广域覆盖、灾害救援等场景中具有重要应用价值。然而，该系统面临链路强耦合、拓扑高动态等挑战，使无线资源分配呈现高维、时变和协同等特征。传统模型驱动优化方法在复杂动态环境下难以满足实时、自适应决策需求。为此，本文围绕多无人机通信系统智能资源分配展开综述。首先，梳理可重构智能表面、通感一体化和移动边缘计算等技术赋能多无人机通信网络架构，并阐明传输过程；其次，研究多无人机系统智能资源分配典型模型的作用机制与适用特点；再次，分析不同通信网络架构下的无线资源智能分配研究现状；最后，给出未来挑战与发展方向。本文可为低空智联网资源分配提供参考。Abstract:
Significance With the rapid development of the low-altitude economy, emergency communications, and related industries, multi-Unmanned Aerial Vehicle (UAV) communication systems have shown significant application value in disaster rescue, low-altitude monitoring, and other scenarios, owing to their advantages in wide-area coverage and link compensation. However, affected by strong link coupling, dynamic topology variation, and enlarged Line-of-Sight (LoS) interference ranges, resource allocation in multi-UAV systems is characterized by high dimensionality, time variation, and multi-agent cooperation. Traditional model-driven optimization methods, such as convex optimization and game theory, are difficult to satisfy the requirements of real-time adaptive decision-making in complex dynamic environments. Therefore, Artificial Intelligence (AI)-enabled intelligent resource allocation has become an important direction for improving robust transmission, autonomous decision-making, and collaborative optimization in multi-UAV communication systems. Based on this, a survey is presented on the architectures, models, and challenges of intelligent resource allocation for multi-UAV communication systems. Progress This paper presents a comprehensive survey of recent advances in intelligent resource allocation for multi-UAV communication systems, with an in-depth discussion of typical network architectures, including cellular cooperative communication, Reconfigurable Intelligent Surface (RIS)-assisted communication, Integrated Sensing and Communication (ISAC), and Mobile Edge Computing (MEC). Among them, cellular cooperative architectures enhance coverage and link connectivity through UAV-enabled aerial relays or aerial base stations. RIS-assisted architectures improve blocked link quality by reconstructing the wireless propagation environment. ISAC architectures support the coordination of communication coverage, target localization, and environmental sensing. MEC architectures deploy computing resources at ground edge servers or UAV nodes to support task offloading and proximate computing. Different architectures vary in node functions, link forms, and resource coupling relationships, thereby determining different optimization variables and intelligent decision-making requirements. Then, typical intelligent models for multi-UAV resource allocation are introduced. Deep Neural Networks (DNNs) can learn nonlinear mappings from system states to continuous resource variables. Convolutional neural networks are suitable for extracting spatial features from channels, topologies, and user distributions. Recurrent neural networks can characterize temporal variations in channel states and UAV positions. Deep Reinforcement Learning (DRL) is applicable to long-term sequential decision-making problems, such as UAV trajectory control and power optimization. Federated Learning (FL) enables distributed collaborative training without sharing raw data. Furthermore, traditional optimization methods and intelligent methods are compared in terms of their applicable characteristics, and it is indicated that these methods should be selected according to scenario dynamics, model availability, real-time requirements, and cooperation scale. Finally, existing studies on intelligent resource allocation are compared across typical architectures, including cellular communication, RIS-assisted communication, ISAC, and MEC. In cellular communication systems, multi-agent proximal policy optimization, multi-agent Deep Deterministic Policy Gradient (DDPG), and other methods are used for trajectory control, frequency allocation, routing optimization, and power allocation. In RIS-assisted systems, double deep Q-network, DDPG, and other methods are used for the joint optimization of UAV trajectories, RIS phase shifts, beamforming, and task offloading. In ISAC systems, generative diffusion-driven multi-agent DRL and adaptive DNN splitting mechanisms are used for sensing-communication resource coordination, robust scheduling, and integrated sensing-communication-computation task processing. In MEC systems, DRL and FL are applied to task offloading, user association, power control, device selection, and priority-aware task scheduling. Conclusions This paper provides a comprehensive survey of intelligent resource allocation for multi-UAV communication systems, with particular emphasis on typical architectures, intelligent models, and current research progress. First, typical architectures of multi-UAV communication systems are systematically reviewed, and the node functions under different architectures are analyzed. Then, typical intelligent models for multi-UAV communication systems are summarized in terms of their mechanisms and applicable characteristics in state representation, feature extraction, sequential decision-making, and distributed cooperation. Furthermore, intelligent resource allocation studies for multi-UAV communication systems are compared across different architectures. Finally, existing challenges are identified, including difficulties in multi-objective tradeoffs under three-dimensional mobility, complex cooperative decision-making under LoS interference, and cross-layer resource coupling under multifunctional integration. Relevant research directions are also proposed. These directions are expected to provide valuable references for future research on intelligent resource allocation in multi-UAV communication systems. Prospects Although intelligent resource allocation methods have made progress in link recovery, coverage enhancement, RIS-assisted link optimization, sensing-communication resource coordination, and MEC task offloading, several challenges still remain in complex dynamic multi-UAV scenarios. First, the three-dimensional mobility of UAVs dynamically couples coverage range, service delay, and energy consumption. Existing methods with fixed reward weights are difficult to adapt to changes in task priorities. Therefore, multi-objective Reinforcement Learning (RL) and dynamic reward weighting can be combined to construct adaptive tradeoff mechanisms. Second, air-to-air and air-to-ground LoS links enlarge the interference propagation range, making spectrum reuse and user association more complex. Graph neural networks and interference prediction mechanisms can be introduced to improve the representation capability of dynamic interference topology. Finally, with the integration of RIS, ISAC, and MEC, UAVs are gradually evolving into multifunctional nodes. Hierarchical RL and cross-layer joint modeling can be further explored to realize the collaborative optimization of communication, sensing, computing, and flight-control resources. -
Key words:
- Multi-UAV communication /
- Artificial intelligence /
- Resource allocation
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表 1 典型架构对比
架构类型 特点 优点 资源分配关键挑战 应用场景 多无人机空中基站 直接接入服务 部署灵活、响应快速 用户关联、频谱与功率耦合 应急通信、偏远地区覆盖 多无人机空中中继 多跳中继转发 多跳转发、增强网络连通性 时延约束下路由与功率协同 盲区覆盖、灾后网络恢复 固定RIS辅助无人机 依赖地面建筑 低功耗、无源反射 轨迹和RIS相位高度耦合 城市高楼区、复杂遮挡场景 多无人机搭载RIS 具备智能反射 机动性强、按需链路增强 轨迹、相位与调度协同复杂 热点区域跟随、移动中继 基站侧ISAC节点 依赖地面基站 成本低、频谱共享效率高 通信和感知精度难以兼顾 高可靠通信、覆盖区域感知 无人机侧ISAC节点 移动感知通信 可动态调整通信与感知区域 通感资源与轨迹协同困难 区域精细扫描、灾害搜救 地面基站部署MEC 依赖地面基站 降低任务处理时延 任务卸载、通信与轨迹耦合 区域高清测绘、活动监控 空中MEC 具备计算能力 降低对基础设施依赖 通算与飞行能耗难协调 灾害处理、实时任务卸载 空天地一体化网络 多层协同 广域覆盖、链路可靠 节点关联、频谱与功率耦合 偏远地区覆盖、灾后通信 面向物联网的空地网络 服务大规模终端 终端关联，动态数据采集 终端关联、调度与轨迹耦合 智慧农业、环境监测 
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表 2 多无人机资源分配典型智能模型类别对比
智能模型类别 优势 劣势 适用场景 DNN 系统状态到连续资源变量的非线性映射 动态拓扑变化和离散决策的适应能力不足 网络规模稳定、状态映射明确 CNN 提取信道、拓扑和用户分布的空间特征 长距离拓扑依赖和时序变化建模能力不足 特征明显、规模固定 RNN 能够刻画信道、位置和任务需求的时序变化 递归效率较低，长时间依赖建模能力有限 状态时序相关性较强的场景 GNN 建模不规则拓扑并适应无人机变化 网络规模与连接密度增加时传递开销大 节点规模变化场景 DRL 适合轨迹、功率、频谱和任务卸载等动态决策问题 收敛开销较高，奖励设计比较依赖经验 动态环境下的长期决策 FL 数据隐私性高，隐私保护下的分布式资源分配训练 通信开销高，异构数据下聚合稳定性不足 数据分散、敏感场景
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表 4 传统优化方法与智能方法对比
方法类型 代表方法 适用条件 优势 局限 凸优化 SCA、BCD等 信道模型明确、变量规模可控 可解释性强，收敛过程清晰 依赖精确模型，实时响应不足 博弈论 非合作/合作博弈等 多无人机间的竞争或协作关系 易刻画多主体竞争与协同 均衡解求解复杂，全局最优性难保证 启发式算法 遗传算法、粒子群 模型复杂但问题规模相对可控 对问题凸性和精度要求低 计算开销高，最优性和稳定性困难 深度学习 DNN、CNN、RNN 训练样本充分，需学习状态映射 适合高维表征和快速推理 依赖训练数据，跨场景泛化能力有限 深度强化学习 PPO、DDPG等 环境动态变化，需长期序列决策 适合多变量动态协同优化 训练收敛慢，奖励函数设计依赖经验 联邦学习 FedAvg、FedProx等 数据在无人机节点且有隐私需求 支持分布式训练与隐私保护 通信开销高，异构数据下聚合不稳
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表 9 技术挑战及潜在解决方向
技术挑战 产生原因 现有不足 潜在解决方向 三维机动下的鲁棒多目标权衡 三维机动与状态误差影响多维指标 固定权重与理想状态假设适应性差 多目标RL、动态权重调整 视距干扰下的多机协同决策 视距干扰扩散加剧多机策略耦合 局部观测难以刻画动态干扰拓扑 图神经网络、干扰预测等 多功能融合下的跨层智能协同 多技术融合导致通感算与飞行耦合 联合动作空间大，模型部署开销高 分层RL、跨层联合建模
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