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

(1) Department of Electrical and Computer Engineering, Wireless Networking and Communications Group, The University of Texas at Austin, Austin, TX 78712 USA
faris.mismar@utexas.edu - alammouri@utexas.edu - jandrews@ece.utexas.edu - bevans@ece.utexas.edu

(2) School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287 USA
alkhateeb@asu.edu

Paper Draft

Software: Deep learning predictive policy for handover and Millimeter wave channel modeling dataset using ray tracing

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 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.