State-incomplete intelligent dynamic multipath routing algorithm in LEO satellite networks

The low Earth orbit (LEO) satellite networks have outstanding advantages such as wide coverage area and not being limited by geographic environment, which can provide a broader range of communication services and has become an essential supplement to the terrestrial network. However, the dynamic changes and uneven distribution of satellite network traffic inevitably bring challenges to multipath routing. Even worse, the harsh space environment often leads to incomplete collection of network state data for routing decision-making, which further complicates this challenge. To address this problem, this paper proposes a state-incomplete intelligent dynamic multipath routing algorithm (SIDMRA) to maximize network efficiency even with incomplete state data as input. Specifically, we model the multipath routing problem as a markov decision process (MDP) and then combine the deep deterministic policy gradient (DDPG) and the K shortest paths (KSP) algorithm to solve the optimal multipath routing policy. We use the temporal correlation of the satellite network state to fit the incomplete state data and then use the message passing neuron network (MPNN) for data enhancement. Simulation results show that the proposed algorithm outperforms baseline algorithms regarding average end-to-end delay and packet loss rate and performs stably under certain missing rates of state data.