SCAR: State-Space Compression for Scalable AI-Based Network Management of Vehicular Services
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908801432551424 |
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| author | Comsa, Ioan-Sorin Shah, Purav Vaidhyanathan, Karthik Gangadharan, Deepak Imhof, Christof Bergamin, Per Kaushik, Aryan Muntean, Gabriel-Miro Trestian, Ramona |
| author_facet | Comsa, Ioan-Sorin Shah, Purav Vaidhyanathan, Karthik Gangadharan, Deepak Imhof, Christof Bergamin, Per Kaushik, Aryan Muntean, Gabriel-Miro Trestian, Ramona |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06243 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SCAR: State-Space Compression for Scalable AI-Based Network Management of Vehicular Services Comsa, Ioan-Sorin Shah, Purav Vaidhyanathan, Karthik Gangadharan, Deepak Imhof, Christof Bergamin, Per Kaushik, Aryan Muntean, Gabriel-Miro Trestian, Ramona Machine Learning Neural and Evolutionary Computing Systems and Control 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. |
| title | SCAR: State-Space Compression for Scalable AI-Based Network Management of Vehicular Services |
| topic | Machine Learning Neural and Evolutionary Computing Systems and Control |
| url | https://arxiv.org/abs/2508.06243 |