Work

Artificial Intelligence-Aided Beam Tracking in Autonomous Vehicles: State of the Art and Future Directions

Year: 2025

Type: article

Abstract: Autonomous vehicles (AVs) require real-time, low-latency, and high-throughput communication to ensure safe and efficient operation. Fifth-generation (5G) and beyond communication networks, particularly New Radio (NR) operating at millimeter wave (mmWave) frequencies, offer promising solutions. However, higher frequencies introduce challenges like signal attenuation, blockage, and complexities of beam management. This paper provides a comprehensive survey of 5G and beyond communication networks, emphasizing their role in vehicle-to-everything (V2X) communication. It uniquely addresses the technical challenges of beam training and management, highlighting the application of artificial intelligence (AI) and machine learning (ML) techniques to optimize beam selection and reduce delays. Furthermore, the paper explores future directions, focusing on AI/ML-driven solutions to enhance beamforming and communication reliability in dynamic AV environments.

Source: IEEE Transactions on Intelligent Transportation Systems

DOI: 10.1109/tits.2025.3597346

Authors: Mohammad Nadeem Ahangar, Qasim Zeeshan Ahmed, Maryam Hafeez, Muhammad Salman Bashir

Institutions: University of Leeds, University of Huddersfield

Language: English

Topic: Millimeter-Wave Propagation and Modeling

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W4413318929

FWCI: 1.133

Cites:

Cited by: 5

Related to: 10