{"ID":2884711,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.06243","arxiv_id":"2508.06243","title":"SCAR: State-Space Compression for Scalable AI-Based Network Management of Vehicular Services","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2508.06243","url_pdf":"https://arxiv.org/pdf/2508.06243v2","authors":"[\"Ioan-Sorin Comsa\",\"Purav Shah\",\"Karthik Vaidhyanathan\",\"Deepak Gangadharan\",\"Christof Imhof\",\"Per Bergamin\",\"Aryan Kaushik\",\"Gabriel-Miro Muntean\",\"Ramona Trestian\"]","published":"2025-08-08T11:53:18Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.NE\",\"eess.SY\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}