Investigating Massive MIMO-Based Resource Allocation Schemes for Autonomous Driving Applications

Abstract

The advent of autonomous driving has introduced unprecedented demands on wireless communication systems, particularly in terms of low latency, high reliability, and massive connectivity. Massive Multiple-Input Multiple-Output (MIMO) technology has emerged as a promising enabler to meet these requirements, leveraging its ability to exploit spatial multiplexing and enhance spectral efficiency. This paper investigates resource allocation schemes based on massive MIMO for autonomous driving applications, focusing on optimizing communication efficiency, ensuring reliability, and minimizing latency. The research encompasses a comprehensive review of existing resource allocation strategies, their applicability to vehicular networks, and potential enhancements tailored to autonomous driving. We analyze the challenges posed by high mobility, dynamic network topologies, and stringent quality-of-service (QoS) requirements in vehicular environments. Furthermore, we explore advanced beamforming techniques, power control, and user scheduling mechanisms optimized for vehicular communication scenarios. A simulation-based evaluation of the proposed schemes demonstrates significant improvements in data rates, latency, and reliability compared to conventional methods. The findings underscore the critical role of massive MIMO in enabling next-generation autonomous driving systems and provide a roadmap for further research in this domain.