MPTCP Throughput Enhancement by Q-Learning for Mobile Devices | IEEE Conference Publication | IEEE Xplore

MPTCP Throughput Enhancement by Q-Learning for Mobile Devices


Abstract:

Mobile devices are able to leverage diverse heterogeneous network paths by Multi-Path Transmission Control Protocol (MPTCP); nevertheless, boosting MPTCP throughput in wi...Show More

Abstract:

Mobile devices are able to leverage diverse heterogeneous network paths by Multi-Path Transmission Control Protocol (MPTCP); nevertheless, boosting MPTCP throughput in wireless networks is a real bear. Not only the best path(s) should be selected, but also the optimal congestion control mechanism should be chosen. We investigate the impact of different paths and congestion control for different signal quality states. Consequently, we present the novel MPTCP algorithm augmenting the end user throughput by understating the best policy in different situations by Q-learning. The Results reveal a tremendous effect of switching between the different interfaces and changing the congestion control mechanism on throughput and delay. By and large, the proposed framework achieves 10% more throughput compared to base MPTCP.
Date of Conference: 28-30 June 2018
Date Added to IEEE Xplore: 24 January 2019
ISBN Information:
Conference Location: Exeter, UK

References

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