research-article

Deep Learning Predictive Band Switching in Wireless Networks

Authors:

Faris B. Mismar,

Ahmad Alammouri,

Ahmed Alkhateeb,

Jeffrey G. Andrews,

Brian L. EvansAuthors Info & Claims

IEEE Transactions on Wireless Communications, Volume 20, Issue 1

Pages 96 - 109

https://doi.org/10.1109/TWC.2020.3023397

Published: 01 January 2021 Publication History

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 <italic>measurement gap</italic> 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.

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Cited By

Ilyas BSofiane BAli Abderrazak TMiloud K(2024)Enhancing 5G massive MIMO systems with EfficientNet‐B7‐powered deep learning‐driven beamformingTransactions on Emerging Telecommunications Technologies10.1002/ett.503435:9Online publication date: 20-Aug-2024
https://dl.acm.org/doi/10.1002/ett.5034
Pan JBedewy ASun YShroff N(2023)Age-Optimal Scheduling Over Hybrid ChannelsIEEE Transactions on Mobile Computing10.1109/TMC.2022.320529222:12(7027-7043)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TMC.2022.3205292

Index Terms

Deep Learning Predictive Band Switching in Wireless Networks
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Neural networks
2. Networks
  1. Network types
    1. Mobile networks

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Wireless Communications

IEEE Transactions on Wireless Communications Volume 20, Issue 1

Jan. 2021

724 pages

ISSN:1536-1276

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1536-1276 © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 January 2021

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Cited By

Ilyas BSofiane BAli Abderrazak TMiloud K(2024)Enhancing 5G massive MIMO systems with EfficientNet‐B7‐powered deep learning‐driven beamformingTransactions on Emerging Telecommunications Technologies10.1002/ett.503435:9Online publication date: 20-Aug-2024
https://dl.acm.org/doi/10.1002/ett.5034
Pan JBedewy ASun YShroff N(2023)Age-Optimal Scheduling Over Hybrid ChannelsIEEE Transactions on Mobile Computing10.1109/TMC.2022.320529222:12(7027-7043)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TMC.2022.3205292

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