cs.ITMar 23, 2026

Spatio-Temporal Attention Enhanced Multi-Agent DRL for UAV-Assisted Wireless Networks with Limited Communications

Authors: Che Chen, Lanhua Li, Shimin Gong, Yu Zhao, Yuming Fang, Dusit Niyato

Organizations: School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518000, China, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China · Department of Equipment Management and Unmanned Aerial Vehicle Engineering, Air Force Engineering University, Xi’an · School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics, Nanchang 330032, China · College of Computing and Data Science, Nanyang Technological University, Singapore

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Abstract

In this paper, we employ multiple UAVs to accelerate data transmissions from ground users (GUs) to a remote base station (BS) via the UAVs' relay communications. The UAVs' intermittent information exchanges typically result in delays in acquiring the complete system state and hinder their effective collaboration. To maximize the overall throughput, we first propose a delay-tolerant multi-agent deep reinforcement learning (MADRL) algorithm that integrates a delay-penalized reward to encourage information sharing among UAVs, while jointly optimizing the UAVs' trajectory planning, network formation, and transmission control strategies. Additionally, considering information loss due to unreliable channel conditions, we further propose a spatio-temporal attention based prediction approach to recover the lost information and enhance each UAV's awareness of the network state. These two designs are envisioned to enhance the network capacity in UAV-assisted wireless networks with limited communications. The simulation results reveal that our new approach achieves over 50% reduction in information delay and 75% throughput gain compared to the conventional MADRL. Interestingly, it is shown that improving the UAVs' information sharing will not sacrifice the network capacity. Instead, it significantly improves the learning performance and throughput simultaneously. It is also effective in reducing the need for UAVs' information exchange and thus fostering practical deployment of MADRL in UAV-assisted wireless networks.

Figures & tables

Fig. 1: Multiple UAVs assist GUs’ data tranmissions to the remote BS, by joint trajectory planning, network formation, and transmisison control.
Fig. 2: The STA-MADRL framework.
Fig. 3: Network structure of the spatio-temporal attention prediction module.
Fig. 4: STA-MADRL achieves close throughput with the Ideal-MADRL.
Fig. 5: Delay-penalized reward improves fair information exchange.
Fig. 6: Frequent U2B information exchange reduces information error.
Fig. 7: The UAVs’ trajectory planning with different learning algorithms.
Fig. 8: Throughput performance with different learning algorithms.
Fig. 9: Clear boundary with less service overlap in STA-MADRL.
Fig. 10: STA-MADRL achieves a higher throughput with a fewer UAVs.
Fig. 11: The impact of GUs’ deployment sparsity.

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