Tracking the Best Beam for a Mobile User via Bayesian Optimization
- Resource Type
- Conference
- Authors
- Maggi, Lorenzo; Koblitz, A. Ryo; Zhu, Qiping; Andrews, Matthew
- Source
- 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring) Vehicular Technology Conference (VTC2023-Spring), 2023 IEEE 97th. :1-7 Jun, 2023
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Power, Energy and Industry Applications
Signal Processing and Analysis
Transportation
Training
Vehicular and wireless technologies
Power measurement
5G mobile communication
Processor scheduling
Neural networks
Time measurement
Beamforming
RSRP
Bayesian optimization
beam tracking
overhead reduction
- Language
- ISSN
- 2577-2465
The standard beam management procedure in 5G requires the user equipment (UE) to periodically measure the received signal reference power (RSRP) on each of a set of beams proposed by the basestation (BS). It is prohibitively expensive to measure the RSRP on all beams and so the BS should propose a beamset that is large enough to allow a high-RSRP beam to be identified, but small enough to prevent excessive reporting overhead. Moreover, the beamset should evolve over time according to UE mobility. We address this fundamental performance/overhead trade-off via a Bayesian optimization technique that requires no or little training on historical data and is rooted on a low complexity algorithm for the beamset choice with theoretical guarantees. We show the benefits of our approach on 3GPP compliant simulation scenarios.