IEEE Transactions on Wireless Communications, submitted October 2, 2019.
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), and
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
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 a band switching approach based on machine learning 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%.
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Last Updated 10/13/19.