  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.
