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.
Original language | English (US) |
---|---|
Article number | 9199558 |
Pages (from-to) | 96-109 |
Number of pages | 14 |
Journal | IEEE Transactions on Wireless Communications |
Volume | 20 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2021 |
Externally published | Yes |
Keywords
- Artificial intelligence
- band switching
- deep learning
- millimeter wave (mmWave)
- out-of-band estimation
- wireless communications
ASJC Scopus subject areas
- Computer Science Applications
- Electrical and Electronic Engineering
- Applied Mathematics