A Belief Propagation Solution for Beam Coordination in MmWave Vehicular Networks

TL;DR

This paper introduces CRAB, a belief propagation-based algorithm for beam management in mmWave vehicular networks, significantly improving data transfer and user coverage compared to existing methods.

Contribution

It formulates a novel optimization problem for beam configuration and develops a scalable belief propagation framework tailored for large urban vehicular scenarios.

Findings

CRAB achieves 50% more data transfer on average.

CRAB provides up to 30% better user coverage.

The approach is effective in real-world urban settings.

Abstract

Millimeter-wave communication is widely seen as a promising option to increase the capacity of vehicular networks, where it is expected that connected cars will soon need to transmit and receive large amounts of data. Due to harsh propagation conditions, mmWave systems resort to narrow beams to serve their users, and such beams need to be configured according to traffic demand and its spatial distribution, as well as interference. In this work, we address the beam management problem, considering an urban vehicular network composed of gNBs. We first build an accurate, yet tractable, system model and formulate an optimization problem aiming at maximizing the total network data rate while accounting for the stochastic nature of the network scenario. Then we develop a graph-based model capturing the main system characteristics and use it to develop a belief propagation algorithmic framework, called CRAB, that has low complexity and, hence, can effectively cope with large-scale scenarios. We assess the performance of our approach under real-world settings and show that, in comparison to state-of-the-art alternatives, CRAB provides on average a 50% improvement in the amount of data transferred by the single gNBs and up to 30% better user coverage.