Semantic-Aware Spectrum Efficiency for 6G V2x URLLC with Multi-Agent Hierarchical DRL

In this study, we propose SCF6, a novel semantic communication framework for 6 G -enabled vehicular networks tailored to ultra-reliable low-latency communication (URLLC) scenarios. SCF6 integrates semantic encoding/decoding with conventional channel processing, optimizing transmission by focusing on data meaning. Leveraging BERT (bidirectional encoder representations from transformers)-based natural language processing, it ensures high semantic similarity between transmitted and received messages. To maximize semantic spectrum efficiency (SSEE) and success rate (SR) for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications under strict URLLC constraints, we design a multi-agent hierarchical attention-based semantic deep reinforcement learning (MAHAS-DRL) framework. MAHASDRL coordinates resource allocation and spectrum sharing, embedding hierarchical attention at both semantic and channel levels to enhance decision-making, optimize power control, and reduce interference. Simulations demonstrate SCF6's superiority over traditional DRL methods in spectrum efficiency, reliability, and latency, proving effective for dynamic urban vehicular networks.