Learning congestion state for mmWave channels

Talal Ahmad, Shiva R. Iyer, Luis Diez, Yasir Zaki, Ramón Agüero, Lakshminarayanan Subramanian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationmmNets 2019 - Proceedings of the 3rd ACM Workshop on Millimeter-Wave Networks and Sensing Systems, co-located with MobiCom 2019
PublisherAssociation for Computing Machinery
Pages19-25
Number of pages7
ISBN (Electronic)9781450369329
DOIs
StatePublished - Oct 7 2019
Event3rd ACM Workshop on Millimeter-Wave Networks and Sensing Systems, mmNets 2019, co-located with MobiCom 2019 - Los Cabos, Mexico
Duration: Oct 25 2019 → …

Publication series

NameProceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM

Conference

Conference3rd ACM Workshop on Millimeter-Wave Networks and Sensing Systems, mmNets 2019, co-located with MobiCom 2019
CountryMexico
CityLos Cabos
Period10/25/19 → …

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Keywords

  • Congestion control
  • ECN
  • Machine learning
  • MmWave

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

Cite this

Ahmad, T., Iyer, S. R., Diez, L., Zaki, Y., Agüero, R., & Subramanian, L. (2019). Learning congestion state for mmWave channels. In mmNets 2019 - Proceedings of the 3rd ACM Workshop on Millimeter-Wave Networks and Sensing Systems, co-located with MobiCom 2019 (pp. 19-25). (Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM). Association for Computing Machinery. https://doi.org/10.1145/3349624.3356769