Abstract
Channel charting is a method for creating radiomaps of a cell that capture the neighborhood relationships between User Equipments (UEs) in the cell based on machine learning techniques. In this paper, we leverage channel charting
for predicting the best Base Station (BS) beam to serve a given UE in a massive-MIMO 5G network. Because of the autonomous beamforming at the UE in 5G networks, the BS cannot determine the best beam for transmission to a UE by measuring the UE transmissions in all the BS beams. To address this issue, we
propose a framework to predict the best BS beam for a mobile UE in the next transmission instant by utilizing the channel charts of the cell that the UE is currently in. We evaluate the prediction accuracy of the framework using simulated channels from QuaDRiGa channel generator. We compare the performance of channel chart and physical location based predictors. While
the prediction accuracy attained using channel charting is less than that of the prediction using physical locations, there remain several ways to improve the performance.
for predicting the best Base Station (BS) beam to serve a given UE in a massive-MIMO 5G network. Because of the autonomous beamforming at the UE in 5G networks, the BS cannot determine the best beam for transmission to a UE by measuring the UE transmissions in all the BS beams. To address this issue, we
propose a framework to predict the best BS beam for a mobile UE in the next transmission instant by utilizing the channel charts of the cell that the UE is currently in. We evaluate the prediction accuracy of the framework using simulated channels from QuaDRiGa channel generator. We compare the performance of channel chart and physical location based predictors. While
the prediction accuracy attained using channel charting is less than that of the prediction using physical locations, there remain several ways to improve the performance.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of IEEE 93rd Vehicular Technology Conference, VTC 2021 |
| Publisher | IEEE |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-7281-8964-2 |
| DOIs | |
| Publication status | Published - 15 Jun 2021 |
| MoE publication type | A4 Conference publication |
| Event | IEEE Vehicular Technology Conference - Helsinki, Finland Duration: 25 Apr 2021 → 28 Apr 2021 Conference number: 93 |
Publication series
| Name | IEEE Vehicular Technology Conference |
|---|---|
| ISSN (Print) | 1090-3038 |
| ISSN (Electronic) | 2577-2465 |
Conference
| Conference | IEEE Vehicular Technology Conference |
|---|---|
| Abbreviated title | VTC-Spring |
| Country/Territory | Finland |
| City | Helsinki |
| Period | 25/04/2021 → 28/04/2021 |
Keywords
- mmWave
- CSI features
- 5G TDD system
- channel charting
- BS beam prediction