Deep Learning Predictive Band Switching in Wireless Networks
This addresses the issue of data rate loss during band switching for cellular user equipment, though it is incremental as it builds on existing band switching concepts with a new machine learning approach.
The paper tackles the problem of measurement gaps reducing data rates during band switching in wireless networks by proposing an online-learning classifier that uses spatial and spectral correlation to predict band quality without gaps, achieving roughly 30% improvement in mean effective rates over the industry standard policy with misclassification errors below 0.5%.
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.