Beam Training and Data Transmission Optimization in Millimeter-Wave Vehicular Networks

TL;DR

This paper investigates how to optimize the trade-off between beam training and data transmission in millimeter-wave vehicular networks using learning-based methods, aiming to improve communication efficiency amid mobility challenges.

Contribution

It introduces a Partially Observable Markov Decision Process framework to adaptively balance beam training and data transmission, outperforming heuristic schemes in vehicular mm-wave networks.

Findings

Adaptive policies outperform heuristic schemes

Mobility features improve system performance

Optimized resource utilization enhances data delivery

Abstract

Future vehicular communication networks call for new solutions to support their capacity demands, by leveraging the potential of the millimeter-wave (mm-wave) spectrum. Mobility, in particular, poses severe challenges in their design, and as such shall be accounted for. A key question in mm-wave vehicular networks is how to optimize the trade-off between directive Data Transmission (DT) and directional Beam Training (BT), which enables it. In this paper, learning tools are investigated to optimize this trade-off. In the proposed scenario, a Base Station (BS) uses BT to establish a mm-wave directive link towards a Mobile User (MU) moving along a road. To control the BT/DT trade-off, a Partially Observable (PO) Markov Decision Process (MDP) is formulated, where the system state corresponds to the position of the MU within the road link. The goal is to maximize the number of bits delivered by the BS to the MU over the communication session, under a power constraint. The resulting optimal policies reveal that adaptive BT/DT procedures significantly outperform common-sense heuristic schemes, and that specific mobility features, such as user position estimates, can be effectively used to enhance the overall system performance and optimize the available system resources.