Fast Channel Estimation and Beam Tracking for Millimeter Wave Vehicular Communications

TL;DR

This paper introduces a novel, efficient channel estimation and beam tracking framework for millimeter wave vehicular communications, significantly reducing feedback overhead and improving tracking accuracy.

Contribution

It proposes the RAF algorithm for channel estimation with fewer feedback bits and a new position-based beam tracking model, enhancing performance over existing methods.

Findings

RAF achieves comparable estimation accuracy with fewer feedback bits.

The new beam tracking model improves tracking performance significantly.

Simulations confirm reduced overhead and enhanced data transmission capacity.

Abstract

Millimeter wave (mmWave) has been claimed to be the only viable solution for high-bandwidth vehicular communications. However, frequent channel estimation and beamforming required to provide a satisfactory quality of service limits mmWave for vehicular communications. In this paper, we propose a novel channel estimation and beam tracking framework for mmWave communications in a vehicular network setting. For channel estimation, we propose an algorithm termed robust adaptive multi-feedback (RAF) that achieves comparable estimation performance as existing channel estimation algorithms, with a significantly smaller number of feedback bits. We derive upper and lower bounds on the probability of estimation error (PEE) of the RAF algorithm, given a number of channel estimations, whose accuracy is verified through Monte Carlo simulations. For beam tracking, we propose a new practical model for mmWave vehicular communications. In contrast to the prior works, the model is based on position, velocity, and channel coefficient, which allows a significant improvement of the tracking performance. Focused on the new beam tracking model, we re-derive the equations for Jacobian matrices, reducing the complexity for vehicular communications. An extensive number of simulations is conducted to show the superiority of our proposed channel estimation method and beam tracking algorithm in comparison with the existing algorithms and models. Our simulations suggest that the RAF algorithm can achieve the desired PEE, while on average, reducing the feedback overhead by 75.5% and the total channel estimation time by 14%. The beam tracking algorithm is also shown to significantly improve beam tracking performance, allowing more room for data transmission.