Zheyuan Jia, Fenglin Jin*, Jun Xie, Yuan He
CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.069128
- 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 More >
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