Joint Initial Access and Localization in Millimeter Wave Vehicular Networks: a Hybrid Model/Data Driven Approach

TL;DR

This paper introduces a low-complexity hybrid model/data-driven approach for vehicle localization in millimeter wave vehicular networks, achieving sub-meter accuracy with reduced computational requirements.

Contribution

It proposes a novel channel estimation method combined with a deep neural network for path order prediction, enhancing localization accuracy and efficiency.

Findings

Achieves sub-meter localization accuracy for 50% of users.

Reduces complexity compared to high-resolution MIMO systems.

Does not require perfect synchronization or all-digital architectures.

Abstract

High resolution compressive channel estimation provides information for vehicle localization when a hybrid mmWave MIMO system is considered. Complexity and memory requirements can, however, become a bottleneck when high accuracy localization is required. An additional challenge is the need of path order information to apply the appropriate geometric relationships between the channel path parameters and the vehicle, RSU and scatterers position. In this paper, we propose a low complexity channel estimation strategy of the angle of departure and time difference of arrival based on multidimensional orthogonal matching pursuit. We also design a deep neural network that predicts the order of the channel paths so only the LoS and first order reflections are used for localization. Simulation results obtained with realistic vehicular channels generated by ray tracing show that sub-meter accuracy can be achieved for 50% of the users, without resorting to perfect synchronization assumptions or unfeasible all-digital high resolution MIMO architectures.