Resource allocation in V2X communication: State-of-the-art and research challenges

This paper explores the multifaceted domain of resource allocation (RA) in vehicle-to-everything (V2X) communication, emphasizing its pivotal role in advancing intelligent transportation systems (ITS) and beyond. While V2X communication stands as a cornerstone for ITS, enabling enhanced safety, efficiency, and automation in vehicular environments, it necessitates sophisticated RA strategies to address the dynamic and diverse demands of modern networks. As vehicle numbers grow, there is a rising need for more spectral resources to ensure efficient and dependable services. In areas with high vehicle concentrations, the demand for these resources intensifies to maintain swift, stable connectivity. Due to limited power, channel, and spectrum resources, their allocation is a critical challenge in V2X network. Efficient allocation of these resources is vital to prevent interference and ensure smooth communication. This study categorizes RA methods into graph-based, game theory-based, genetic algorithm-based, heuristic-based, optimization technique-based, machine learning-based, deep learning-based, and reinforcement learning-based approaches, each with its unique advantages and applicability in V2X contexts. We further delineate the evaluation metrics critical for assessing these methods, including throughput, latency, reliability, and energy efficiency, to provide a comprehensive context for comparison. Beyond ITS, the paper explores the broader implications of efficient RA in facilitating emergent V2X applications, such as emergency service provisioning, vehicle platooning, and speed harmonization. However, achieving optimal RA in V2X networks is fraught with challenges, including mobility and interference management, scalability, dynamic network topology, and the integration of heterogeneous technologies. The discussion extends to potential future directions, highlighting the importance of cross-layer design, integration of next-generation wireless technologies, and the adoption of artificial intelligence for adaptive and anticipatory resource management. By expanding the discourse beyond ITS, this paper aims to offer readers a holistic understanding of the current state, challenges, and forward-looking insights into RA for V2X communication.