Self-learning Congestion Control of MPTCP in Satellites Communications | IEEE Conference Publication | IEEE Xplore

Self-learning Congestion Control of MPTCP in Satellites Communications


Abstract:

The past few years have witnessed a wide deployment of low earth orbit (LEO) satellites communications and networking. With the explosive growth of new businesses, satell...Show More

Abstract:

The past few years have witnessed a wide deployment of low earth orbit (LEO) satellites communications and networking. With the explosive growth of new businesses, satellite network is expected to provide global coverage and high bandwidth availability service. Toward this end, Multipath TCP(MPTCP) is a promising transport protocol to use in LEO satellites networks. MPTCP can not only achieve seamless handover, but also enhance throughput by using multiple paths transmission mechanism. However, following the improvement of the performance and scalability, it also brings unprecedented challenges for congestion control of multiple sub-flows. Especially, currently works on the congestion control largely relies on a manual process which presents a poor performance in the high-dynamic complexity network environment. Inspired by the recent success of applying machine learning in many challenging control decision domains, such as video game, self-driving, we employ deep deterministic policy gradient for learning the optimal congestion control strategies by interacting with the underlying network environment. Some simulation results demonstrated the effectiveness and feasibility of our architecture and algorithms.
Date of Conference: 24-28 June 2019
Date Added to IEEE Xplore: 22 July 2019
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Conference Location: Tangier, Morocco

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