@inproceedings{6e407a6f064f415582c2d01a5122acce,
title = "Learning congestion state for mmWave channels",
abstract = "Millimeter wave (commonly known as mmWave) is enabling the next generation of last-hop communications for mobile devices. But these technologies cannot reach their full potential because existing congestion control schemes at the transport layer perform sub-optimally over mmWave links. In this paper, we show how existing congestion control schemes perform sub-optimally in such channels. Then, we propose that we can learn early congestion signals by using end-to-end measurements at the sender and receiver. We believe that these learned measurements can help build a better congestion control scheme. We show that we can learn Explicit Congestion Notification (ECN) per packet with an F1-score as high as 97%. We achieve this by leveraging unsupervised learning on data obtained from sending periodic bursts of probe packets over emulated 60 GHz links (based on real-world WiGig measurements), with random background traffic.",
keywords = "Congestion control, ECN, Machine learning, MmWave",
author = "Talal Ahmad and Iyer, {Shiva R.} and Luis Diez and Yasir Zaki and Ram{\'o}n Ag{\"u}ero and Lakshminarayanan Subramanian",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 3rd ACM Workshop on Millimeter-Wave Networks and Sensing Systems, mmNets 2019, co-located with MobiCom 2019 ; Conference date: 25-10-2019",
year = "2019",
month = oct,
day = "7",
doi = "10.1145/3349624.3356769",
language = "English (US)",
series = "Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM",
publisher = "Association for Computing Machinery",
pages = "19--25",
booktitle = "mmNets 2019 - Proceedings of the 3rd ACM Workshop on Millimeter-Wave Networks and Sensing Systems, co-located with MobiCom 2019",
}