Graph-based Model for Beam Management in Mmwave Vehicular Networks

TL;DR

This paper introduces a graph-based beam management method for mmwave vehicular networks, formulating the problem as a maximum-weight matching task and demonstrating superior performance over clustering-based approaches.

Contribution

It presents a novel graph-based formulation for beam management in vehicular networks, solving it with an efficient heuristic that outperforms existing clustering methods.

Findings

Outperforms clustering-based beam management methods

Efficient heuristic solves the maximum-weight matching problem

Improves beam design in urban vehicular mmwave networks

Abstract

Mmwave bands are being widely touted as a very promising option for future 5G networks, especially in enabling such networks to meet highly demanding rate requirements. Accordingly, the usage of these bands is also receiving an increasing interest in the context of 5G vehicular networks, where it is expected that connected cars will soon need to transmit and receive large amounts of data. Mmwave communications, however, require the link to be established using narrow directed beams, to overcome harsh propagation conditions. The advanced antenna systems enabling this also allow for a complex beam design at the base station, where multiple beams of different widths can be set up. In this work, we focus on beam management in an urban vehicular network, using a graph-based approach to model the system characteristics and the existing constraints. In particular, unlike previous work, we formulate the beam design problem as a maximum-weight matching problem on a bipartite graph with conflicts, and then we solve it using an efficient heuristic algorithm. Our results show that our approach easily outperforms advanced methods based on clustering algorithms.