Machine learning for reliable mmwave systems: Blockage prediction and proactive handoff

Ahmed Alkhateeb, Iz Beltagy, Sam Alex

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

Abstract

The sensitivity of millimeter wave (mmWave) signals to blockages is a fundamental challenge for mobile mmWave communication systems. The sudden blockage of the line-of-sight (LOS) link between the base station and the mobile user normally leads to disconnecting the communication session, which highly impacts the system reliability. Further, reconnecting the user to another LOS base station incurs high beam training overhead and critical latency problem. In this paper, we leverage machine learning tools and propose a novel solution for these reliability and latency challenges in mmWave MIMO systems. In the developed solution, the base stations learn how to predict that a certain link will experience blockage in the next few time frames using their observations of adopted beamforming vectors. This allows the serving base station to proactively hand-over the user to another base station with highly probable LOS link. Simulation results show that the developed deep learning based strategy successfully predicts blockage/hand-off in close to 95% of the times. This reduces the probability of communication session disconnection, which ensures high reliability and low latency in mobile mmWave systems.

Original languageEnglish (US)
Title of host publication2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1055-1059
Number of pages5
ISBN (Electronic)9781728112954
DOIs
StatePublished - Feb 20 2019
Event2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States
Duration: Nov 26 2018Nov 29 2018

Publication series

Name2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

Conference

Conference2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
CountryUnited States
CityAnaheim
Period11/26/1811/29/18

Fingerprint

Base stations
Learning systems
Millimeter waves
Communication
Beamforming
MIMO systems
Communication systems

Keywords

  • Beamforming
  • Blockages
  • Hand-off
  • Machine learning
  • Millimeter wave

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

Cite this

Alkhateeb, A., Beltagy, I., & Alex, S. (2019). Machine learning for reliable mmwave systems: Blockage prediction and proactive handoff. In 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings (pp. 1055-1059). [8646438] (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2018.8646438

Machine learning for reliable mmwave systems : Blockage prediction and proactive handoff. / Alkhateeb, Ahmed; Beltagy, Iz; Alex, Sam.

2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1055-1059 8646438 (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings).

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

Alkhateeb, A, Beltagy, I & Alex, S 2019, Machine learning for reliable mmwave systems: Blockage prediction and proactive handoff. in 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings., 8646438, 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1055-1059, 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018, Anaheim, United States, 11/26/18. https://doi.org/10.1109/GlobalSIP.2018.8646438
Alkhateeb A, Beltagy I, Alex S. Machine learning for reliable mmwave systems: Blockage prediction and proactive handoff. In 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1055-1059. 8646438. (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings). https://doi.org/10.1109/GlobalSIP.2018.8646438
Alkhateeb, Ahmed ; Beltagy, Iz ; Alex, Sam. / Machine learning for reliable mmwave systems : Blockage prediction and proactive handoff. 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1055-1059 (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings).
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