Deep Learning Predictive Band Switching in Wireless Networks

Faris B. Mismar, Ahmad Alammouri, Ahmed Alkhateeb, Jeffrey G. Andrews, Brian L. Evans

Research output: Contribution to journalArticlepeer-review

Abstract

In cellular systems, the user equipment (UE) can request a change in the frequency band when its rate drops below a threshold on the current band. The UE is then instructed by the base station (BS) to measure the quality of candidate bands, which requires a measurement gap in the data transmission, thus lowering the data rate. We propose an online-learning based band switching approach that does not require any measurement gap. Our proposed classifier-based band switching policy instead exploits spatial and spectral correlation between radio frequency signals in different bands based on knowledge of the UE location. We focus on switching between a lower (e.g., 3.5 GHz) band and a millimeter wave band (e.g., 28 GHz), and design and evaluate two classification models that are trained on a ray-tracing dataset. A key insight is that measurement gaps are overkill, in that only the relative order of the bands is necessary for band selection, rather than a full channel estimate. Our proposed machine learning-based policies achieve roughly 30% improvement in mean effective rates over those of the industry standard policy, while achieving misclassification errors well below 0.5% and maintaining resilience against blockage uncertainty.

Original languageEnglish (US)
Article number9199558
Pages (from-to)96-109
Number of pages14
JournalIEEE Transactions on Wireless Communications
Volume20
Issue number1
DOIs
StatePublished - Jan 2021
Externally publishedYes

Keywords

  • Artificial intelligence
  • band switching
  • deep learning
  • millimeter wave (mmWave)
  • out-of-band estimation
  • wireless communications

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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