Intelligent Resource Allocation for Multi-UAV Communication Systems: Architectures, Models, and Challenges

XU Yongjun; XIAO Hui; ZHANG Haibo; NI Hao; CHEN Qianbin; GU Bowen; LI Xingwang; HE Yi

doi:10.11999/JEIT260945

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XU Yongjun, XIAO Hui, ZHANG Haibo, NI Hao, CHEN Qianbin, GU Bowen, LI Xingwang, HE Yi. Intelligent Resource Allocation for Multi-UAV Communication Systems: Architectures, Models, and Challenges[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260945

Citation:

XU Yongjun, XIAO Hui, ZHANG Haibo, NI Hao, CHEN Qianbin, GU Bowen, LI Xingwang, HE Yi. Intelligent Resource Allocation for Multi-UAV Communication Systems: Architectures, Models, and Challenges[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260945

Citation:

XU Yongjun, XIAO Hui, ZHANG Haibo, NI Hao, CHEN Qianbin, GU Bowen, LI Xingwang, HE Yi. Intelligent Resource Allocation for Multi-UAV Communication Systems: Architectures, Models, and Challenges[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260945

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Intelligent Resource Allocation for Multi-UAV Communication Systems: Architectures, Models, and Challenges

doi: 10.11999/JEIT260945 cstr: 32379.14.JEIT260945

XU Yongjun^{1, 2
,},
XIAO Hui^{1, 2
,},
ZHANG Haibo^{1
,
,},
NI Hao^1
,,
CHEN Qianbin^1
,,
GU Bowen^3
,,
LI Xingwang^4
,,
HE Yi^5
,

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Institute of Artificial Intelligence, Chongqing 401339, China
3.
School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China
4.
School of Physics and Electronic Information, Henan Polytechnic University, Jiaozuo 454003, China
5.
Space Engineering University, Beijing 101416, China

Funds: The Key Program of the National Natural Science Foundation of China (No.U23A20279), Natural Science Foundation of Chongqing (No.CSTB2024NSCQ-QCXMX0059), Xinjiang Tianchi Talents Yong Doctors Fund (No.51052501823), Doctoral Student Innovation Program of Chongqing University of Posts and Telecommunications (No.BYJS202502)

Accepted Date: 2026-10-16
Rev Recd Date: 2026-10-16

Available Online: 2026-10-26

Abstract

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.
- Multi-UAV communication,
- Artificial intelligence,
- Resource allocation

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References(48)

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