# An Explainable AI Framework for Dynamic Resource Management in Vehicular Network Slicing

arXiv: https://arxiv.org/abs/2506.11882 · HTML: https://mlsanity.com/paper/2506.11882/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 13 Jun 2025.

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

- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [Explaining Reinforcement Learning with Shapley Values](https://mlsanity.com/paper/2306.05810/) (2023)
- [Towards Sustainability in 6G and beyond: Challenges and Opportunities of Open RAN](https://mlsanity.com/paper/2503.08353/) (2025)
- [Network Slicing for Vehicular Communication](https://mlsanity.com/paper/1905.09578/) (2019)

## Built on by (most cited first, 1 of 1)

- [Handover Analysis for Vehicular Communication with Explainability on the Fly](https://mlsanity.com/paper/2608.14820/) (2026)

## Abstract (arXiv)

Effective resource management and network slicing are essential to meet the diverse service demands of vehicular networks, including Enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low-Latency Communications (URLLC). This paper introduces an Explainable Deep Reinforcement Learning (XRL) framework for dynamic network slicing and resource allocation in vehicular networks, built upon a near-real-time RAN intelligent controller. By integrating a feature-based approach that leverages Shapley values and an attention mechanism, we interpret and refine the decisions of our reinforcementlearning agents, addressing key reliability challenges in vehicular communication systems. Simulation results demonstrate that our approach provides clear, real-time insights into the resource allocation process and achieves higher interpretability precision than a pure attention mechanism. Furthermore, the Quality of Service (QoS) satisfaction for URLLC services increased from 78.0% to 80.13%, while that for eMBB services improved from 71.44% to 73.21%.