Adaptive Mode Selection and Resource Management in V2X Communication Using MetaLearning and Federated Deep Reinforcement Learning Approach

Vehicle-to-Everything (V2X) communication has emerged as a critical enabler for intelligent transportation systems, supporting real-time data exchange among vehicles, infrastructure, and network entities. However, the highly dynamic nature of vehicular environments, combined with limited spectrum availability, interference, and stringent Quality-of-Service (QoS) requirements, makes efficient resource management a challenging task. Conventional approaches based on static configurations or basic deep reinforcement learning often fail to adapt effectively to rapidly changing network conditions. To address these challenges, this paper proposes a meta-learning-enhanced federated deep reinforcement learning framework integrated with an attention mechanism (Meta-Attn-FedDRL) for adaptive mode selection and resource allocation in V2X communication systems. The proposed approach leverages meta-learning to enable rapid adaptation to varying traffic scenarios, while federated learning facilitates distributed model training without sharing raw data. The attention mechanism further enhances decision-making by prioritizing critical channel conditions and interference patterns. The framework dynamically selects optimal communication modes, including Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I), while efficiently allocating spectrum and transmission power. A two-timescale asynchronous federated learning architecture is employed to improve convergence speed and scalability. Simulation results demonstrate that the proposed method achieves significant performance improvements, including higher throughput and data rate, along with reduced latency and delay compared to conventional Async-FedDRL approaches. These improvements highlight the effectiveness of the proposed framework in dynamic and dense vehicular environments.