Deep Learning Predictive Band Switching in Wireless Networks

TL;DR

This paper introduces an online-learning based band switching method for wireless networks that eliminates the need for measurement gaps, leveraging spatial and spectral correlations to improve data rates and reliability.

Contribution

It proposes a novel classifier-based band switching policy that uses location-aware machine learning models trained on ray-tracing data, reducing measurement overhead.

Findings

Achieves approximately 30% higher mean effective rates compared to industry standards.

Maintains misclassification errors below 0.5%.

Demonstrates resilience against blockage uncertainty.

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.