mlsanityData to 14 Oct 2026

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

arXiv 2506.11882 ↗First posted 13 Jun 2025cs.LG cs.AIcited by 1

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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%.

Key facts. Cited by 1 paper 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

4 of 20 references.

References · 4

  1. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,148
  2. 2023Explaining Reinforcement Learning with Shapley Valuescited by 26
  3. 2025Towards Sustainability in 6G and beyond: Challenges and Opportunities of Open RANcited by 5
  4. 2019Network Slicing for Vehicular Communicationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Handover Analysis for Vehicular Communication with Explainability on the Flycited by 0

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  1. 2024DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach
  2. 2024Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning
  3. 2024On Enhancing Network Throughput using Reinforcement Learning in Sliced Testbeds
  4. 2025Explainable AI-aided Feature Selection and Model Reduction for DRL-based V2X Resource Allocation
  5. 2026Deadline-Aware Multi-Agent Reinforcement Learning for TSN-Based Vehicular Edge Networks
  6. 2025SCAR: State-Space Compression for Scalable AI-Based Network Management of Vehicular Services
  7. 2024Event-Triggered Reinforcement Learning Based Joint Resource Allocation for Ultra-Reliable Low-Latency V2X Communications
  8. 2025Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures