SCAR: State-Space Compression for AI-Driven Resource Management in 6G-Enabled Vehicular Infotainment Systems

2025-08-08

AI Summary

To address the inefficiency and unfairness of conventional radio resource management (RRM) in 6G vehicular networks—caused by the massive scale and high dimensionality of channel quality indicators (CQIs) from autonomous vehicles—this paper proposes SCAR, a framework integrating state-space compression with AI-driven resource management. SCAR innovatively employs radial basis function (RBF) networks combined with simulated-annealing-based stochastic tunneling (SAST) clustering to achieve efficient CQI dimensionality reduction. The compressed state representation then drives a lightweight reinforcement learning policy for infotainment service scheduling. Compared to an uncompressed baseline, SCAR improves feasible scheduling time by 14%, reduces unfair scheduling time by 15%, and decreases CQI clustering distortion by 10%. These gains significantly enhance scalability, throughput, and fairness of resource allocation in dynamic vehicular networks.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Optimizing resource management in 6G vehicular infotainment systems
• Reducing CQI data complexity with AI-driven compression techniques
• Improving scheduling fairness and throughput in dynamic vehicular networks

Innovation

• ML-based compression for CQI data reduction
• 6G-enabled Reinforcement Learning for throughput maximization
• SAST-based clustering to minimize CQI distortion