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

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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

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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

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

Received Date: 2026-04-28
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) serve as mobile aerial relay nodes to support massive ground-user access in complex environments. However, traditional data-oriented communication paradigms incur substantial bandwidth overhead, limiting their applicability in spectrum-constrained UAV networks. In addition, conventional centralized resource allocation methods are difficult to implement in real time because of highly dynamic network topologies and the strong coupling among multidimensional resources. To address these challenges, semantic communication has emerged as a communication paradigm that extracts semantic information at the transmitter and reconstructs it at the receiver, thereby reducing redundant data transmission. Existing semantic-driven resource allocation methods, however, primarily focus on static terrestrial networks or single-UAV scenarios and do not adequately address coverage limitations and co-channel interference in multi-UAV relay networks. Therefore, a joint resource optimization model and a distributed resource allocation framework are proposed to improve semantic transmission efficiency and long-term user fairness through the joint optimization of multidimensional resources, thereby supporting intelligent resource scheduling in future 6G integrated networks. Methods A joint resource optimization model is formulated for multi-UAV relay networks under the semantic communication paradigm (Fig. 1), jointly optimizing the number of semantic symbols, UAV trajectories, power control, and channel allocation. To evaluate semantic communication performance, a Semantic Communication Quality of Service (SC-QoS) metric is proposed by combining Semantic Quantization Efficiency (SQE) with normalized transmission delay. The optimization objective is formulated as a Mixed-Integer NonLinear Programming (MINLP) problem that maximizes the weighted sum of system-wide SC-QoS and long-term user fairness measured by Jain’s fairness index. To solve this problem, a Two-Stage Hybrid Reinforcement Learning (TS-HRL) framework is proposed (Fig. 2). In the first stage, a Capacity-Aware K-means (CA-K-means) algorithm performs heuristic UAV pre-deployment. By introducing a dynamic distance compensation term based on residual capacity, edge users are guided toward lightly loaded UAVs, thereby achieving load balancing while preserving spatial proximity. In the second stage, the dynamic scheduling problem is formulated as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and solved using a Recurrent Independent Proximal Policy Optimization with Parameter Sharing (R-IPPO-PS) algorithm (Algorithm 1). The algorithm employs Long Short-Term Memory (LSTM) networks to aggregate historical observations and action trajectories, enabling the inference of hidden environmental states. Furthermore, the parameter-sharing mechanism improves training efficiency as the number of agents increases, while the association preference decoupling strategy transforms discrete channel allocation decisions into continuous association preference variables to facilitate policy optimization. Results and Discussions The proposed TS-HRL framework is evaluated in a dynamic environment with randomly moving ground users. Convergence analysis shows that the proposed method achieves a higher initial reward and converges with fewer iterations than the random deployment, memoryless resource allocation, and Enhanced Independent Soft Actor-Critic (EI-SAC) baselines (Fig. 3). By combining LSTM-based temporal modeling with CA-K-means pre-deployment, the proposed framework reduces ineffective exploration and improves convergence stability. Compared with conventional bit-based communication, the semantic communication framework increases Semantic Spectral Efficiency (S-SE) by 3.6-fold, reduces transmission delay by 92.1%, and improves fairness by 14.3% (Fig. 4). Within the semantic communication framework, 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 level of fairness while achieving an S-SE approximately 4.3 times that of EI-SAC. Compared with random deployment, the proposed method improves S-SE by 11.3% with only a 1.1% decrease in fairness, demonstrating a better balance between transmission efficiency and fairness. As the number of users increases from 10 to 40, most baseline methods exhibit decreases in S-SE and fairness because of intensified co-channel interference and spectrum limitations (Fig. 5). In contrast, the adaptive scheduling strategy and global fairness reward mechanism mitigate performance degradation and maintain the average transmission delay below 0.1 ms. These results demonstrate that the proposed method improves overall system performance while ensuring long-term user fairness. Conclusions This paper investigates joint resource allocation and trajectory optimization for dynamic 6G multi-UAV relay networks under the semantic communication paradigm. A joint optimization model that couples the number of semantic symbols, UAV trajectories, power control, and channel allocation is formulated to maximize SC-QoS and long-term user fairness. To address the high-dimensional coupling of the optimization problem, a TS-HRL framework integrating CA-K-means pre-deployment with the R-IPPO-PS algorithm is proposed for multidimensional resource scheduling under partial observability. Simulation results demonstrate the convergence, stability, and scalability of the proposed method under different user densities. Through distributed multi-agent cooperation among UAVs, the proposed method improves semantic transmission efficiency and reduces transmission delay while maintaining long-term service fairness for ground users. These findings provide an effective approach to intelligent resource allocation for UAV-assisted semantic communication in future space-air-ground integrated networks.
- Semantic communication,
- Multi-UAV relay network,
- Resource allocation,
- Trajectory optimization,
- Multi-agent reinforcement learning

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References(29)

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