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A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications
- 6G networks are expected to revolutionize Connected and Autonomous Vehicles (CAVs) by providing ultra-reliable, low-latency, and high-capacity connectivity through Vehicle-to-Everything (V2X) communication.
- Artificial Intelligence (AI) and Machine Learning (ML) play a key role in optimizing V2X communication by enhancing network management, predictive analytics, security, and cooperative driving.
- AI and ML have excelled in domains like natural language processing and computer vision, contributing significantly to the evolution of 6G-V2X applications.
- This survey delves into recent advancements of AI and ML models in the context of 6G-V2X communication, with a focus on techniques like Deep Learning (DL), Reinforcement Learning (RL), Generative Learning (GL), and Federated Learning (FL).
- Notably, Generative Learning (GL) has shown remarkable progress in enhancing the performance, adaptability, and intelligence of 6G-V2X systems.
- The survey aims to address the lack of a systematic summary of recent research efforts, analyzing the roles of AI and ML in intelligent resource allocation, beamforming, traffic management, and security within 6G-V2X applications.
- Challenges such as computational complexity, data privacy, and real-time decision-making constraints are explored, alongside future research directions to drive AI-driven 6G-V2X development.
- The study provides valuable insights for researchers, engineers, and policymakers involved in the advancement of intelligent, AI-powered V2X ecosystems in 6G communication.
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