Resource Allocation for Multi-UAV Relay Networks in 6G Semantic Communication

XIAO Liming; GUAN Zheng; LIU Jie; YU Jihong; CHEN Liyuan

doi:10.11999/JEIT260520

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2026 >

XIAO Liming, GUAN Zheng, LIU Jie, YU Jihong, CHEN Liyuan. Resource Allocation for Multi-UAV Relay Networks in 6G Semantic Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260520

Citation:

XIAO Liming, GUAN Zheng, LIU Jie, YU Jihong, CHEN Liyuan. Resource Allocation for Multi-UAV Relay Networks in 6G Semantic Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260520

Citation:

PDF( 3169 KB)

Resource Allocation for Multi-UAV Relay Networks in 6G Semantic Communication

doi: 10.11999/JEIT260520 cstr: 32379.14.JEIT260520

School of Information Science and Engineering, Yunnan University, Kunming 650500, China

Funds: Key Project of Yunnan Provincial Fundamental Research Program (202601AS070109), The Yunnan University Professional Degree Graduate Practice Innovation Fund (ZC-252513639)

Received Date: 2026-04-27
Accepted Date: 2026-07-15
Rev Recd Date: 2026-07-15

Available Online: 2026-07-25

Abstract

Abstract

Objective Sixth-generation (6G) mobile networks aim to achieve global seamless coverage through space-air-ground integrated architectures. In this context, Unmanned Aerial Vehicles (UAVs) can serve as mobile aerial relay nodes to support massive ground user access in complex environments. However, traditional data-oriented communication paradigms incur high bandwidth overhead, making them difficult to apply under the limited spectrum resources of UAV networks. In addition, traditional centralized resource allocation methods struggle with real-time implementation due to highly dynamic network topologies and the complex coupling of multi-dimensional resources. To address these challenges, semantic communication has emerged as a new paradigm that extracts the essential meaning of information at the transmitter and reconstructs it at the receiver, thereby reducing redundant data traffic. Although semantic communication provides a feasible solution, existing semantic-driven resource allocation methods primarily focus on static ground networks or single-UAV scenarios, often overlooking the coverage limitations and co-channel interference in multi-UAV collaborative networks. Therefore, an intelligent joint resource optimization model and a distributed resource allocation framework are needed for multi-UAV relay networks. By jointly optimizing multi-dimensional resources, such a framework can improve semantic transmission efficiency and ensure long-term user fairness, thereby supporting intelligent resource scheduling in 6G integrated networks. Methods This study formulates a joint resource optimization model for multi-UAV relay networks under the semantic communication paradigm (Fig.1), coupling the number of semantic symbols, UAV trajectories, transmission power, and channel allocation. To evaluate semantic transmission quality, the proposed model introduces a Semantic Communication Quality of Service (SC-QoS) metric, which integrates Semantic Quantization Efficiency with normalized transmission delay. The optimization objective is formulated as a Mixed Integer Nonlinear Programming problem that maximizes the weighted sum of system-wide SC-QoS and long-term user fairness evaluated by Jain's fairness index. To solve this problem, a Two-Stage Hybrid Reinforcement Learning (TS-HRL) framework is proposed (Fig.2). The first stage employs a Capacity-Aware K-means (CA-K-means) algorithm for heuristic UAV pre-deployment. By introducing a dynamic distance compensation term based on residual capacity, this algorithm guides edge users toward low-load UAVs, achieving load balancing among UAV clusters while preserving spatial proximity. The second stage models the dynamic scheduling problem as a Decentralized Partially Observable Markov Decision Process and solves it using a Recurrent Independent Proximal Policy Optimization with Parameter Sharing (R-IPPO-PS) algorithm (Algorithm 1). This algorithm leverages Long Short-Term Memory (LSTM) networks to aggregate historical trajectories and observations, thereby capturing hidden environmental states. Furthermore, the multi-agent parameter-sharing mechanism reduces model complexity with respect to the agent scale, while the preference decoupling strategy converts discrete channel allocation decisions into continuous preference variables to facilitate model training. Results and Discussions The proposed TS-HRL framework is evaluated in a dynamic environment with randomly moving ground users. The convergence analysis shows that the proposed method achieves a higher initial reward and reaches a stable state with fewer iterations than the random deployment, memoryless allocation, and Enhanced Independent Soft Actor-Critic (EI-SAC) baselines (Fig.3). By leveraging LSTM-based historical information and CA-K-means topology initialization, the proposed method reduces invalid exploration and improves convergence stability. Compared with traditional bit-based communication, the semantic communication framework increases Semantic Spectral Efficiency (S-SE) by 3.6 times, reduces transmission delay by 92.1%, and improves fairness by 14.3% (Fig.4). Within the semantic communication paradigm, compared with the memoryless and heuristic schemes, the proposed method improves S-SE by 92.7% and 51.9%, reduces transmission delay by 13.5% and 57.7%, and improves fairness by 17.3% and 39.7%, respectively. Although the EI-SAC baseline achieves a fairness index of 0.93, its S-SE remains relatively low. In contrast, the proposed TS-HRL framework maintains a high fairness level while achieving an S-SE approximately 4.3 times higher than that of EI-SAC. Compared with random deployment, the proposed method improves S-SE by 11.3% with only a marginal 1.1% decrease in fairness, demonstrating a better trade-off between transmission efficiency and fairness. As the number of users increases from 10 to 40, most baselines show decreases in S-SE and fairness due to intensified co-channel interference and spectrum constraints (Fig.5). In contrast, the adaptive scheduling and global fairness reward mechanisms of the proposed method mitigate performance degradation and maintain the average transmission delay below 0.1 ms. These results indicate that the proposed method improves overall system performance while ensuring long-term user fairness. Conclusions This paper investigates joint resource allocation and trajectory planning in dynamic 6G multi-UAV relay networks by integrating the semantic communication paradigm. A joint optimization model coupling the number of semantic symbols, UAV trajectory, power control, and channel allocation is formulated to maximize system SC-QoS and long-term user fairness. To address the high-dimensional coupling of the formulated problem, a TS-HRL framework is proposed. This framework combines CA-K-means pre-deployment with the R-IPPO-PS algorithm to handle multi-dimensional resource scheduling under partial observability. Simulation results verify the convergence, stability, and scalability of the proposed method under different user densities. Through distributed cooperation among UAVs, the proposed method enhances semantic transmission efficiency and reduces transmission delay while ensuring long-term service fairness among ground users. These findings provide a viable approach for intelligent resource allocation in UAV-assisted semantic communication for future space-air-ground integrated networks.
- Semantic communication,
- Multi-UAV network,
- Resource allocation,
- Trajectory optimization,
- Multi-agent reinforcement learning

FullText(HTML)

References(29)

References

[1]	ZHANG Ruichen, DU Hongyang, NIYATO D, et al. Generative AI for space-air-ground integrated networks[J]. IEEE Wireless Communications, 2024, 31(6): 10–20. doi: 10.1109/MWC.016.2300547.
[2]	FENG Zikai, WU Di, HUANG Mengxing, et al. Graph-attention-based reinforcement learning for trajectory design and resource assignment in multi-UAV-assisted communication[J]. IEEE Internet of Things Journal, 2024, 11(16): 27421–27434. doi: 10.1109/JIOT.2024.3397823.
[3]	XIE Ping, CHEN Qian, WU Jingyan, et al. NOMA-based intelligent resource allocation and trajectory optimization for multi-UAVs assisted semantic communication networks[J]. Ad Hoc Networks, 2025, 171: 103762. doi: 10.1016/j.adhoc.2025.103762.
[4]	张广驰, 顾泽霖, 崔苗. 空地协同通信感知一体化系统的轨迹与资源分配联合优化[J]. 电子与信息学报, 2024, 46(6): 2382–2390. doi: 10.11999/JEIT230716. ZHANG Guangchi, GU Zelin, and CUI Miao. Joint trajectory and resource allocation optimization for air-ground collaborative integrated sensing and communication systems[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2382–2390. doi: 10.11999/JEIT230716.
[5]	冯斯梦, 张云弈, 刘凯, 等. 低空混合障碍下无人机协同多智能体航迹规划[J]. 电子与信息学报, 2025, 47(5): 1291–1300. doi: 10.11999/JEIT250012. FENG Simeng, ZHANG Yunyi, LIU Kai, et al. Collaborative multi-agent trajectory optimization for unmanned aerial vehicles under low-altitude mixed-obstacle airspace[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1291–1300. doi: 10.11999/JEIT250012.
[6]	张平, 牛凯, 姚圣时, 等. 面向未来的语义通信: 基本原理与实现方法[J]. 通信学报, 2023, 44(5): 1–14. doi: 10.11959/j.issn.1000−436x.2023079. ZHANG Ping, NIU Kai, YAO Shengshi, et al. Semantic communications for future: Basic principle and implementation methodology[J]. Journal on Communications, 2023, 44(5): 1–14. doi: 10.11959/j.issn.1000−436x.2023079.
[7]	QIN Zhijin, GAO Feifei, LIN Bo, et al. A generalized semantic communication system: From sources to channels[J]. IEEE Wireless Communications, 2023, 30(3): 18–26. doi: 10.1109/MWC.013.2200553.
[8]	FU Yuzhou, CHENG Wenchi, ZHANG Wei, et al. Scalable extraction based semantic communication for 6G wireless networks[J]. IEEE Communications Magazine, 2024, 62(7): 96–102. doi: 10.1109/MCOM.021.2300269.
[9]	DING Yahao, YANG Zhaohui, PHAM Q V, et al. Distributed machine learning for UAV swarms: Computing, sensing, and semantics[J]. IEEE Internet of Things Journal, 2024, 11(5): 7447–7473. doi: 10.1109/JIOT.2023.3341307.
[10]	WANG Lingyi, WU Wei, ZHOU Fuhui, et al. Adaptive resource allocation for semantic communication networks[J]. IEEE Transactions on Communications, 2024, 72(11): 6900–6916. doi: 10.1109/TCOMM.2024.3405355.
[11]	YAN Lei, QIN Zhijin, ZHANG Rui, et al. Resource allocation for text semantic communications[J]. IEEE Wireless Communications Letters, 2022, 11(7): 1394–1398. doi: 10.1109/LWC.2022.3170849.
[12]	YAN Lei, QIN Zhijin, LI Chunfeng, et al. QoE-based semantic-aware resource allocation for multi-task networks[J]. IEEE Transactions on Wireless Communications, 2024, 23(9): 11958–11971. doi: 10.1109/TWC.2024.3386807.
[13]	XU Jiaqi, YAO Haipeng, ZHANG Ru, et al. Federated learning powered semantic communication for UAV swarm cooperation[J]. IEEE Wireless Communications, 2024, 31(4): 140–146. doi: 10.1109/MWC.005.2300147.
[14]	HOA N T, HAI C T T, LE HUNG H, et al. Joint edge computing and semantic communication in UAV-enabled networks[J]. IEEE Communications Letters, 2025, 29(1): 80–84. doi: 10.1109/LCOMM.2024.3496534.
[15]	DENG Yundi, LENG Supeng, ZHANG Ke, et al. Semantic communication assisted cooperative sensing in UAV networks[C]. Proceedings of 2025 10th International Conference on Computer and Communication System (ICCCS), Chengdu, China, 2025: 862–867. doi: 10.1109/ICCCS65393.2025.11069451.
[16]	SI Peiyuan, ZHAO Jun, LAM K Y, et al. UAV-assisted semantic communication with hybrid action reinforcement learning[C]. Proceedings of 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023: 3801–3806. doi: 10.1109/GLOBECOM54140.2023.10437643.
[17]	XIE Wancheng, YANG Helin, and XIONG Zehui. Resource allocation for UAV-assisted anti-jamming semantic D2D networks: A graph reinforcement learning approach[J]. Computer Networks, 2025, 269: 111463. doi: 10.1016/j.comnet.2025.111463.
[18]	时统志, 李博, 杨洪娟, 等. 基于传输公平性的多无人机通感一体化空间部署与波束成形设计[J]. 电子与信息学报, 2025, 47(1): 57–65. doi: 10.11999/JEIT240590. SHI Tongzhi, LI Bo, YANG Hongjuan, et al. Spatial deployment and beamforming for design multi-unmanned aerial vehicle-integrated sensing and communication based on transmission fairness[J]. Journal of Electronics & Information Technology, 2025, 47(1): 57–65. doi: 10.11999/JEIT240590.
[19]	HU Han, ZHU Xingwu, ZHOU Fuhui, et al. Resource allocation for multi-modal semantic communication in UAV collaborative networks[J]. IEEE Transactions on Communications, 2025, 73(9): 7599–7616. doi: 10.1109/TCOMM.2025.3552303.
[20]	YANG Helin, ZHAO Jun, NIE Jiangtian, et al. UAV-assisted 5G/6G networks: Joint scheduling and resource allocation based on asynchronous reinforcement learning[C]. Proceedings of IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Vancouver, Canada, 2021: 1–6. doi: 10.1109/INFOCOMWKSHPS51825.2021.9484604.
[21]	CHEN Ying, YANG Yaozong, WU Yuan, et al. Joint trajectory optimization and resource allocation in UAV-MEC systems: A lyapunov-assisted DRL approach[J]. IEEE Transactions on Services Computing, 2025, 18(2): 854–867. doi: 10.1109/TSC.2025.3544124.
[22]	TUSHA A and ARSLAN H. Interference burden in wireless communications: A comprehensive survey from PHY layer perspective[J]. IEEE Communications Surveys & Tutorials, 2025, 27(4): 2204–2246. doi: 10.1109/COMST.2024.3487068.
[23]	XIE Huiqiang, QIN Zhijin, LI G Y, et al. Deep learning enabled semantic communication systems[J]. IEEE Transactions on Signal Processing, 2021, 69: 2663–2675. doi: 10.1109/TSP.2021.3071210.
[24]	CHRYSOLOGOU A P, CHATZIDIAMANTIS N D, and KARAGIANNIDIS G K. Ergodic rate and fairness analysis of uplink RSMA with randomly deployed users[J]. IEEE Wireless Communications Letters, 2025, 14(11): 3764–3768. doi: 10.1109/LWC.2025.3603208.
[25]	TANG Qinqin, XIE Renchao, YU F R, et al. Decentralized computation offloading in IoT fog computing system with energy harvesting: A Dec-POMDP approach[J]. IEEE Internet of Things Journal, 2020, 7(6): 4898–4911. doi: 10.1109/JIOT.2020.2971323.
[26]	LI Rongyu, LU Wei, and CHU Jinghui. Multi-objective path planning for UAV swarm-assisted wireless information and power transfer system based on decentralized MADRL[J]. IEEE Open Journal of the Communications Society, 2025, 6: 8539–8555. doi: 10.1109/OJCOMS.2025.3615969.
[27]	CAI Xuli, LOHAN P, and KANTARCI B. Multi-agent deep reinforcement learning for optimized multi-UAV coverage and power-efficient UE connectivity[C]. Proceedings of 2025 IEEE 36th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Istanbul, Turkey, 2025: 1–6. doi: 10.1109/PIMRC62392.2025.11274644.
[28]	GHOSH S, MUKHERJEE P, and GHOSH S C. Energy-efficient UAV movement and user-UAV association in multi-UAV networks[C]. Proceedings of 2025 IEEE International Mediterranean Conference on Communications and Networking (MeditCom), Nice, France, 2025: 1–6. doi: 10.1109/MeditCom64437.2025.11104463.
[29]	LIN Xinyi, LI Peizheng, and AIJAZ A. RL-driven semantic compression model selection and resource allocation in semantic communication systems[C]. Proceedings of 2025 IEEE 36th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Istanbul, Turkey, 2025: 1–6. doi: 10.1109/PIMRC62392.2025.11274758.