| Title |
Mptcp Throughput Enhancement By Q-Learning For Mobile Devices |
| ID_Doc |
38016 |
| Authors |
Fakhimi E.; Beig G.M.; Daneshjoo P.; Rezaei S.; Akbar Movassagh A.; Karimi R.; Qin Y. |
| Year |
2019 |
| Published |
Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018 |
| DOI |
http://dx.doi.org/10.1109/HPCC/SmartCity/DSS.2018.00197 |
| 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. © 2018 IEEE. |
| Author Keywords |
Congestion control; Multi-path TCP; Q-learning; Throughput |
