A Two-Hops State-Aware Routing Strategy Based on Deep Reinforcement Learning for LEO Satellite Networks

Abstract

Low Earth Orbit (LEO) satellite networks can provide complete connectivity and worldwide data transmission capability for the internet of things. However, arbitrary flow arrival and uneven traffic load among areas bring about unbalanced traffic distribution over the LEO constellation. Therefore, the routing strategy in LEO networks should have the ability to adjust routing paths based on changes in network status adaptively. In this paper, we propose a Two-Hops State-Aware Routing Strategy Based on Deep Reinforcement Learning (DRL-THSA) for LEO satellite networks. In this strategy, each node only needs to obtain the link state within the range of two-hop neighbors, and the optimal next-hop node can be output. The link state is divided into three levels, and the traffic forwarding strategy for each level is proposed, which allows DRL-THSA to cope with link outage or congestion. The Double-Deep Q Network (DDQN) is proposed in DRL-THSA to figure out the optional next hop by inputting the two-hops link states. The DDQN is analyzed from three aspects: model setting, training process and running process. The effectiveness of DRL-THSA, in terms of end-to-end delay, throughput, and packet drop rate, is verified via a set of simulations using the Network Simulator 3 (NS3).