Beam Training in mmWave Vehicular Systems: Machine Learning for Decoupling Beam Selection
This work addresses overhead reduction in dynamic vehicular communications, but it appears incremental as it builds on existing ML and location-aided methods for a specific domain.
The paper tackles the overhead problem in beam selection for mmWave vehicular systems by developing ML-based location-aided approaches to decouple beam selection between user equipment and base stations, showing that decoupled selection with location information performs comparably to joint selection and without location approaches it when sufficient beams are swept.
Codebook-based beam selection is one approach for configuring millimeter wave communication links. The overhead required to reconfigure the transmit and receive beam pair, though, increases in highly dynamic vehicular communication systems. Location information coupled with machine learning (ML) beam recommendation is one way to reduce the overhead of beam pair selection. In this paper, we develop ML-based location-aided approaches to decouple the beam selection between the user equipment (UE) and the base station (BS). We quantify the performance gaps due to decoupling beam selection and also disaggregating the UE's location information from the BS. Our simulation results show that decoupling beam selection with available location information at the BS performs comparable to joint beam pair selection at the BS. Moreover, decoupled beam selection without location closely approaches the performance of beam pair selection at the BS when sufficient beam pairs are swept.