Joint Optimization of Computing and Routing in LEO Satellite Constellations with Distributed Deep Reinforcement Learning

Earth observation satellites in low earth orbit (LEO) collect a large amount of image data daily, while space-to-ground links have become the major bottleneck for data transmission due to the limited bandwidth. Existing approaches focus on exploring more efficient routing strategies to achieve better data transmission but still struggle to keep pace with the surging volume of observed data. However, the advancement of onboard computing power has opened the possibility of processing data on satellites to reduce the transmitted data volume. This paper proposes a distributed deep reinforcement learning (DRL) algorithm to improve transmission efficiency by jointly optimizing computing and routing. Aiming to minimize task latency while considering the limitations of satellite storage resources, the problem is modeled as a partially observable Markov process (POMDP). An algorithm based on dueling double deep Q-Network (Dueling-DDQN) is proposed to achieve dynamic decision-making utilizing local and neighboring resource states. Furthermore, a method for dynamic optimization of backhaul destinations is proposed, using the pre-trained Q-network to estimate action values across multiple candidate destination satellites, thus enabling further optimization of data transmission without additional training. Simulation results indicate that the proposed algorithm achieves the lowest latency across various task loads compared to baseline methods.