Learning congestion state for mmWave channels

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

doi:10.1145/3349624.3356769

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 proceeding › Conference 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 language	English (US)
Title of host publication	mmNets 2019 - Proceedings of the 3rd ACM Workshop on Millimeter-Wave Networks and Sensing Systems, co-located with MobiCom 2019
Publisher	Association for Computing Machinery
Pages	19-25
Number of pages	7
ISBN (Electronic)	9781450369329
DOIs	https://doi.org/10.1145/3349624.3356769
State	Published - Oct 7 2019
Event	3rd 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

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

Conference

Conference	3rd ACM Workshop on Millimeter-Wave Networks and Sensing Systems, mmNets 2019, co-located with MobiCom 2019
Country/Territory	Mexico
City	Los Cabos
Period	10/25/19 → …

Keywords

Congestion control
ECN
Machine learning
MmWave

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Software

Access to Document

10.1145/3349624.3356769

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

Learning congestion state for mmWave channels. / Ahmad, Talal; Iyer, Shiva R.; Diez, Luis et al.

mmNets 2019 - Proceedings of the 3rd ACM Workshop on Millimeter-Wave Networks and Sensing Systems, co-located with MobiCom 2019. Association for Computing Machinery, 2019. p. 19-25 (Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ahmad, T, Iyer, SR, 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. Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, Association for Computing Machinery, pp. 19-25, 3rd ACM Workshop on Millimeter-Wave Networks and Sensing Systems, mmNets 2019, co-located with MobiCom 2019, Los Cabos, Mexico, 10/25/19. https://doi.org/10.1145/3349624.3356769

Ahmad T, Iyer SR, Diez L, Zaki Y, Agüero R, Subramanian L. 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. Association for Computing Machinery. 2019. p. 19-25. (Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM). doi: 10.1145/3349624.3356769

Ahmad, Talal ; Iyer, Shiva R. ; Diez, Luis et al. / Learning congestion state for mmWave channels. mmNets 2019 - Proceedings of the 3rd ACM Workshop on Millimeter-Wave Networks and Sensing Systems, co-located with MobiCom 2019. Association for Computing Machinery, 2019. pp. 19-25 (Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM).

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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.",

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note = "Funding Information: This work was supported by Defense Advanced Research Projects Agency (DARPA) contract HR001117C0048. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of DARPA. Luis Diez & Ram{\'o}n Ag{\"u}ero were partially supported by the Spanish Government (Ministerio de Econom{\'i}a y Competitividad, Fondo Europeo de Desarrollo Regional, FEDER) by means of the projects ADVICE: Dynamic provisioning of connectivity in high density 5G wireless scenarios (TEC2015-71329-C2-1-R) and FIERCE: Future Internet Enabled Resilient Cities (RTI2018-093475-A-100). 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",

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N1 - Funding Information: This work was supported by Defense Advanced Research Projects Agency (DARPA) contract HR001117C0048. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of DARPA. Luis Diez & Ramón Agüero were partially supported by the Spanish Government (Ministerio de Economía y Competitividad, Fondo Europeo de Desarrollo Regional, FEDER) by means of the projects ADVICE: Dynamic provisioning of connectivity in high density 5G wireless scenarios (TEC2015-71329-C2-1-R) and FIERCE: Future Internet Enabled Resilient Cities (RTI2018-093475-A-100). Publisher Copyright: © 2019 Association for Computing Machinery.

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N2 - 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.

AB - 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.

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Learning congestion state for mmWave channels

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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