# Explainable AI in 6G O-RAN: A Tutorial and Survey on Architecture, Use Cases, Challenges, and Future Research

arXiv: https://arxiv.org/abs/2307.00319 · HTML: https://mlsanity.com/paper/2307.00319/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2023.

## Built on by (most cited first, 12 of 12)

- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm](https://mlsanity.com/paper/2512.04405/) (2025)
- [Digital Twin-Assisted Explainable AI for Robust Beam Prediction in mmWave MIMO Systems](https://mlsanity.com/paper/2507.14180/) (2025)
- [Automated Modeling Method for Pathloss Model Discovery](https://mlsanity.com/paper/2505.23383/) (2025)
- [From Connectivity to Autonomy: The Dawn of Self-Evolving Communication Systems](https://mlsanity.com/paper/2505.23710/) (2025)
- [EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RAN](https://mlsanity.com/paper/2602.09206/) (2026)
- [Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence](https://mlsanity.com/paper/2602.24115/) (2026)
- [AutORAN: LLM-driven Natural Language Programming for Agile xApp Development](https://mlsanity.com/paper/2603.18604/) (2026)
- [Feature Attribution in 5G Intrusion Detection: A Statistical vs. Logic-Based Comparison](https://mlsanity.com/paper/2509.10206/) (2025)
- [Interpretable Attention-Based Multi-Agent PPO for Latency Spike Resolution in 6G RAN Slicing](https://mlsanity.com/paper/2602.11076/) (2026)
- [AI-Empowered UAV-Assisted Backscatter Localization and ISAC for Zero-Energy IoT: A Comprehensive Survey](https://mlsanity.com/paper/2606.23125/) (2026)
- [Physically Constrained Federated Additive Models for O-RAN SLA-Risk Prediction](https://mlsanity.com/paper/2607.21665/) (2026)

## Abstract (arXiv)

The recent O-RAN specifications promote the evolution of RAN architecture by function disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop control architecture managed by RAN Intelligent Controllers (RICs) entities. This paves the road to novel data-driven network management approaches based on programmable logic. Aided by Artificial Intelligence (AI) and Machine Learning (ML), novel solutions targeting traditionally unsolved RAN management issues can be devised. Nevertheless, the adoption of such smart and autonomous systems is limited by the current inability of human operators to understand the decision process of such AI/ML solutions, affecting their trust in such novel tools. eXplainable AI (XAI) aims at solving this issue, enabling human users to better understand and effectively manage the emerging generation of artificially intelligent schemes, reducing the human-to-machine barrier. In this survey, we provide a summary of the XAI methods and metrics before studying their deployment over the O-RAN Alliance RAN architecture along with its main building blocks. We then present various use cases and discuss the automation of XAI pipelines for O-RAN as well as the underlying security aspects. We also review some projects/standards that tackle this area. Finally, we identify different challenges and research directions that may arise from the heavy adoption of AI/ML decision entities in this context, focusing on how XAI can help to interpret, understand, and improve trust in O-RAN operational networks.