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Fast Channel Estimation and Beam Tracking for Millimeter Wave Vehicular Communications

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TLDR
This paper proposes 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, for channel estimation and beam tracking in a vehicular network setting.
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.

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Radar-assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensing

TL;DR: A radar-assisted predictive beamforming design for vehicle-to-infrastructure (V2I) communication by exploiting the dual-functional radar-communication (DFRC) technique is investigated, showing that the proposed DFRC based beam tracking approach significantly outperforms the communication-only feedback based technique in the tracking performance.
Journal ArticleDOI

Radar-Assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensing

TL;DR: In this paper, the authors investigated a radar-assisted predictive beamforming design for vehicle-to-infrastructure (V2I) communication by exploiting the dual-functional radar-communication (DFRC) technique.
Journal ArticleDOI

Bayesian Predictive Beamforming for Vehicular Networks: A Low-Overhead Joint Radar-Communication Approach

TL;DR: In this article, the authors proposed a predictive beamforming scheme in the context of dual-functional radar-communication (DFRC) systems, where the road-side unit estimates and predicts the motion parameters of vehicles based on the echoes of the DFRC signal.
Posted Content

Bayesian Predictive Beamforming for Vehicular Networks: A Low-overhead Joint Radar-Communication Approach

TL;DR: Simulation results show that the proposed DFRC based beamforming scheme is superior to the feedback-based approach in terms of both estimation and communication performance, and the proposed message passing algorithm achieves a similar performance of the high-complexity particle filtering-based methods.
Journal ArticleDOI

Learning-Based Predictive Beamforming for UAV Communications With Jittering

TL;DR: A learning-based predictive beamforming scheme is developed to address the beam misalignment caused by UAV jittering and a deep learning approach is adopted to predict the angles between the UAV and the UE.
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