Adaptive Beam Alignment in Mm-Wave Networks: A Deep Variational Autoencoder Architecture

Muddassar Hussain, Nicolo Michelusi

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

2 Scopus citations

Abstract

This paper proposes a dual timescale learning and adaptation framework to learn a probabilistic model of beam dynamics and concurrently exploit this model to design adaptive beam-training with low overhead: on a long timescale, a deep recurrent variational autoencoder (DR-VAE) uses noisy beam-training observations to learn a probabilistic model of beam dynamics; on a short timescale, an adaptive beam-training procedure is formulated as a partially observable Markov decision process and optimized using point-based value iteration by leveraging beam-training feedback and probabilistic predictions of the strongest beam pair provided by the DR-VAE. In turn, beam-training observations are used to refine the DR-VAE via stochastic gradient ascent in a continuous process of learning and adaptation. It is shown that the proposed DR-VAE learning framework learns accurate beam dynamics and, as learning progresses, the training overhead decreases and the spectral efficiency increases. Moreover, the proposed dual timescale approach achieves near-optimal spectral efficiency, with a gain of 85% over a policy that scans exhaustively over the dominant beam pairs, and of 18% over a state-of-the-art POMDP policy.

Original languageEnglish (US)
Title of host publication2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728181042
DOIs
StatePublished - 2021
Event2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain
Duration: Dec 7 2021Dec 11 2021

Publication series

Name2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings

Conference

Conference2021 IEEE Global Communications Conference, GLOBECOM 2021
Country/TerritorySpain
CityMadrid
Period12/7/2112/11/21

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Health Informatics

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