SCAR: State-Space Compression for AI-Driven Resource Management in 6G-Enabled Vehicular Infotainment Systems
This work addresses resource management for 6G-enabled vehicular infotainment systems, representing an incremental improvement with specific gains in scheduling efficiency and fairness.
The paper tackles the challenge of managing radio resources in 6G vehicular infotainment systems, where traditional methods struggle with complex data like Channel Quality Indicators (CQI). It proposes SCAR, an Edge AI framework that uses ML-based compression to reduce CQI data size, resulting in a 14% increase in feasible scheduling time and a 15% reduction in unfair scheduling time compared to RL baselines.
The advent of 6G networks opens new possibilities for connected infotainment services in vehicular environments. However, traditional Radio Resource Management (RRM) techniques struggle with the increasing volume and complexity of data such as Channel Quality Indicators (CQI) from autonomous vehicles. To address this, we propose SCAR (State-Space Compression for AI-Driven Resource Management), an Edge AI-assisted framework that optimizes scheduling and fairness in vehicular infotainment. SCAR employs ML-based compression techniques (e.g., clustering and RBF networks) to reduce CQI data size while preserving essential features. These compressed states are used to train 6G-enabled Reinforcement Learning policies that maximize throughput while meeting fairness objectives defined by the NGMN. Simulations show that SCAR increases time in feasible scheduling regions by 14\% and reduces unfair scheduling time by 15\% compared to RL baselines without CQI compression. Furthermore, Simulated Annealing with Stochastic Tunneling (SAST)-based clustering reduces CQI clustering distortion by 10\%, confirming its efficiency. These results demonstrate SCAR's scalability and fairness benefits for dynamic vehicular networks.