# SCAR: State-Space Compression for Scalable AI-Based Network Management of Vehicular Services

arXiv: https://arxiv.org/abs/2508.06243 · HTML: https://mlsanity.com/paper/2508.06243/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 8 Aug 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 4 of 4)

- [Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning](https://mlsanity.com/paper/2012.03414/) (2020)
- [Learn to Compress CSI and Allocate Resources in Vehicular Networks](https://mlsanity.com/paper/1908.04685/) (2019)
- [Analysis of Spectrum Occupancy Using Machine Learning Algorithms](https://mlsanity.com/paper/1503.07104/) (2015)
- [Distributed Computation Offloading for Energy Provision Minimization in WP-MEC Networks with Multiple HAPs](https://mlsanity.com/paper/2411.00397/) (2024)

## Abstract (arXiv)

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.