Mmwave Beam Management in Urban Vehicular Networks

TL;DR

This paper proposes a traffic signal-based beam management strategy for mmwave vehicular networks, leveraging traffic signals to improve beam alignment without real-time vehicle position data, validated through real-world mobility traces.

Contribution

It introduces a novel beam management approach using traffic signals, combining a realistic mmwave model with a heuristic strategy, outperforming static and real-time based methods.

Findings

Approach closely matches the optimal beam design.

Outperforms static beam strategies based on road topology.

Achieves better performance than real-time mobility solutions.

Abstract

Millimeter-wave (mmwave) communication represents a potential solution to capacity shortage in vehicular networks. However, effective beam alignment between senders and receivers requires accurate knowledge of the vehicles' position for fast beam steering, which is often impractical to obtain in real time. We address this problem by leveraging the traffic signals regulating vehicular mobility: as an example, we may coordinate beams with red traffic lights, as they correspond to higher vehicle densities and lower speeds. To evaluate our intuition, we propose a tractable, yet accurate, mmwave communication model accounting for both the distance and the heading of vehicles being served. Using such a model, we optimize the beam design and define a low-complexity, heuristic strategy. For increased realism, we consider as reference scenario a large-scale, real-world mobility trace of vehicles in Luxembourg. The results show that our approach closely matches the optimum and always outperforms static beam design based on road topology alone. Remarkably, it also yields better performance than solutions based on real-time mobility information.