Open Access

ARTICLE

Recurrent MAPPO for Joint UAV Trajectory and Traffic Offloading in Space-Air-Ground Integrated Networks

Zheyuan Jia, Fenglin Jin^*, Jun Xie, Yuan He

1 College of Command & Control Engineering, Army Engineering University of PLA, Nanjing, 210044, China

* Corresponding Author: Fenglin Jin. Email:

Computers, Materials & Continua 2026, 86(1), 1-15. https://doi.org/10.32604/cmc.2025.069128

Received 15 June 2025; Accepted 24 July 2025; Issue published 10 November 2025

Abstract

This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks (SAGIN) through a novel Recursive Multi-Agent Proximal Policy Optimization (RMAPPO) algorithm. The exponential growth of mobile devices and data traffic has substantially increased network congestion, particularly in urban areas and regions with limited terrestrial infrastructure. Our approach jointly optimizes unmanned aerial vehicle (UAV) trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput, minimize energy consumption, and maintain equitable resource distribution. The proposed RMAPPO framework incorporates recurrent neural networks (RNNs) to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness. The proposed RMAPPO algorithm was evaluated through simulation experiments, with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.

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Keywords

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