Channel Estimation for 6G V2X HybridSystems using Multi-Vehicular Learning

TL;DR

This paper introduces a novel multi-vehicular hybrid MIMO channel estimation method for 6G V2X systems, leveraging vehicle passages and low-rank learning to improve spectral efficiency and MSE in dynamic environments.

Contribution

It proposes a new multi-vehicular beam alignment and low-rank digital channel estimation approach tailored for highly dynamic V2X scenarios, outperforming traditional methods.

Findings

Improved spectral efficiency and MSE over existing methods.

Achieves performance comparable to fully digital systems in some scenarios.

Validated with ray-tracing data and realistic vehicle trajectories.

Abstract

Channel estimation for hybrid Multiple Input Multiple Output (MIMO) systems at Millimeter-Waves (mmW)/sub-THz is a fundamental, despite challenging, prerequisite for an efficient design of hybrid MIMO precoding/combining. Most works propose sequential search algorithms, e.g., Compressive Sensing (CS), that are most suited to static channels and consequently cannot apply to highly dynamic scenarios such as Vehicle-to-Everything (V2X). To address the latter ones, we leverage \textit{recurrent vehicle passages} to design a novel Multi Vehicular (MV) hybrid MIMO channel estimation suited for Vehicle-to-Infrastructure (V2I) and Vehicle-to-Network (V2N) systems. Our approach derives the analog precoder/combiner through a MV beam alignment procedure. For the digital precoder/combiner, we adapt the Low-Rank (LR) channel estimation method to learn the position-dependent eigenmodes of the received digital signal (after beamforming), which is used to estimate the compressed channel in the communication phase. Extensive numerical simulations, obtained with ray-tracing channel data and realistic vehicle trajectories, demonstrate the benefits of our solution in terms of both achievable Spectral Efficiency (SE) and Mean Square Error (MSE) compared to the Unconstrained Maximum Likelihood (U-ML) estimate of the compressed digital channel, making it suitable for both 5G and future 6G systems. Most notably, in some scenarios, we obtain the performance of the optimal Fully Digital (FD) systems.