IEEE Transactions on Wireless Communications,
vol. 20, no. 1, Jan. 2021, pp. 96-109, DOI 10.1109/TWC.2020.3023397.
Deep Learning Predictive Band Switching in Wireless Networks
Faris B. Mismar (1),
Ahmad AlAmmouri (1),
Ahmed Alkhateeb (2)
Jeffrey G. Andrews (1), and
Brian L. Evans (1)
Department of Electrical and Computer Engineering,
Wireless Networking and Communications Group,
The University of Texas at Austin,
Austin, TX 78712 USA
School of Electrical, Computer and Energy Engineering,
Arizona State University,
Tempe, AZ 85287 USA
IEEE Explore and
Deep learning predictive policy for handover and
Millimeter wave channel modeling dataset using ray tracing
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.
COPYRIGHT NOTICE: All the documents on this server
have been submitted by their authors to scholarly journals or conferences
as indicated, for the purpose of non-commercial dissemination of
The manuscripts are put on-line to facilitate this purpose.
These manuscripts are copyrighted by the authors or the journals in which
they were published.
You may copy a manuscript for scholarly, non-commercial purposes, such
as research or instruction, provided that you agree to respect these
Last Updated 01/13/21.