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).
Highlights
As the powerful supplement of terrestrial networks, the satellite networks are playing an increasingly significant role in the generation global communication system [1]
low earth orbit (LEO) satellite networks are usually composed of tens or hundreds of satellites, its routing problem is more complicated than terrestrial network mainly due to its features, such as dynamic link states and unbalanced traffic load caused by arbitrary flow arrival and communication hot spots [4]
To evaluate the proposed Deep reinforcement learning (DRL)-THSA, we use NS-3.29 (Network Simulator 3, Version 3.29) as the simulation tool to construct the simulations in an Iridium-like satellite network with 66 satellites distributed over six planes
Summary
As the powerful supplement of terrestrial networks, the satellite networks are playing an increasingly significant role in the generation global communication system [1]. Considering the link state may frequently change in the partial area, a state-aware and load-balanced routing (SALB) model for dynamic LEO satellite networks is proposed in [11]. To cope with routing packets efficiently and dynamically, a method to observe LEO satellite networks topology is necessary. A two-hops state-aware routing strategy based on deep reinforcement learning (DRL-THSA) is proposed for LEO satellite networks. In DRL-THSA, each node collects link state information within two-hop neighbors and makes routing decisions based on the information. The remainder of this paper has the following structure: In Section 2, the LEO satellite networks model, which includes the satellite networks topology, setup and update of link state, and two-hops state aware updating, are described.
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