The increasing demand for connected vehicular services poses significant challenges for AI-based network and service management due to the high volume and rapid variability of network state information. Traditional management and control mechanisms struggle to scale when processing fine-grained metrics such as Channel Quality Indicators (CQIs) in dynamic vehicular environments. To address this challenge, we propose SCAR (State-Space Compression for AI-Based Network Management), an edge-assisted framework that improves scalability and fairness in vehicular services through network state abstraction. SCAR employs machine-learning (ML)-based compression techniques, including clustering and radial basis function (RBF) networks, to reduce the dimensionality of CQI-derived state information while preserving essential features relevant to management decisions. The resulting compressed states are used to train reinforcement learning (RL)-based management policies that aim to maximize network efficiency while satisfying service-level fairness objectives defined by the NGMN. Simulation results show that SCAR increases the time spent in feasible management regions by 14% and reduces unfair service allocation time by 15% compared to reinforcement learning baselines operating on uncompressed state information. Furthermore, simulated annealing with stochastic tunneling (SAST)-based clustering reduces state compression distortion by 10%, confirming the effectiveness of the proposed approach. These results demonstrate that SCAR enables scalable and fair AI-assisted network and service management in dynamic vehicular systems.

翻译：车联网服务需求的日益增长，对基于人工智能的网络与服务管理提出了重大挑战，这主要源于网络状态信息的高吞吐量与快速时变性。在动态车联网环境中处理细粒度指标（如信道质量指示器CQI）时，传统管理与控制机制难以实现有效扩展。为应对这一挑战，本研究提出SCAR（面向AI网络管理的状态空间压缩框架），这是一种通过网络状态抽象来提升车联网服务可扩展性与公平性的边缘辅助框架。SCAR采用基于机器学习（ML）的压缩技术，包括聚类与径向基函数（RBF）网络，在保留管理决策相关关键特征的同时，对CQI衍生的状态信息进行降维处理。所得压缩状态用于训练基于强化学习（RL）的管理策略，该策略旨在最大化网络效率的同时，满足NGMN定义的服务层级公平性目标。仿真结果表明：相较于基于未压缩状态信息的强化学习基线方法，SCAR将可行管理区域的驻留时间提升了14%，并将不公平服务分配时间降低了15%。此外，基于随机隧道模拟退火（SAST）的聚类方法将状态压缩失真降低了10%，验证了所提方法的有效性。这些结果证明，SCAR能够在动态车联网系统中实现可扩展且公平的AI辅助网络与服务管理。