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Open AccessArticle

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

School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
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Electronics 2019, 8(9), 920; https://doi.org/10.3390/electronics8090920
Received: 15 July 2019 / Revised: 18 August 2019 / Accepted: 20 August 2019 / Published: 22 August 2019
(This article belongs to the Section Microwave and Wireless Communications)
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). View Full-Text
Keywords: LEO satellite networks; satellite routing; state aware; virtual node; deep reinforcement leaning LEO satellite networks; satellite routing; state aware; virtual node; deep reinforcement leaning
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Wang, C.; Wang, H.; Wang, W. A Two-Hops State-Aware Routing Strategy Based on Deep Reinforcement Learning for LEO Satellite Networks. Electronics 2019, 8, 920.

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