Millimeter Wave V2V Beam Tracking using Radar: Algorithms and Real-World Demonstration

TL;DR

This paper presents radar-assisted algorithms for beam tracking in millimeter wave vehicle-to-vehicle communication, demonstrating real-world effectiveness and highlighting the importance of high-resolution radar for dynamic beam management.

Contribution

It introduces a radar-aided beam-tracking framework with two novel approaches combining radar processing and machine learning for V2V scenarios.

Findings

High angular resolution radar improves beam prediction accuracy

Radar-based methods outperform traditional beam tracking techniques

Real-world dataset validation confirms feasibility

Abstract

Utilizing radar sensing for assisting communication has attracted increasing interest thanks to its potential in dynamic environments. A particularly interesting problem for this approach appears in the vehicle-to-vehicle (V2V) millimeter wave and terahertz communication scenarios, where the narrow beams change with the movement of both vehicles. To address this problem, in this work, we develop a radar-aided beam-tracking framework, where a single initial beam and a set of radar measurements over a period of time are utilized to predict the future beams after this time duration. Within this framework, we develop two approaches with the combination of various degrees of radar signal processing and machine learning. To evaluate the feasibility of the solutions in a realistic scenario, we test their performance on a real-world V2V dataset. Our results indicated the importance of high angular resolution radar for this task and affirmed the potential of using radar for the V2V beam management problems.