# ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities

arXiv: https://arxiv.org/abs/2604.01239 · HTML: https://mlsanity.com/paper/2604.01239/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 78 of its 288 references that are in the map.
- First posted on arXiv 27 Mar 2026.

## Builds on (most cited first, 30 of 78)

- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms](https://mlsanity.com/paper/2112.09559/) (2021)
- [Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments](https://mlsanity.com/paper/1909.11800/) (2019)
- [Deep Reinforcement Learning for Resource Management in Network Slicing](https://mlsanity.com/paper/1805.06591/) (2018)
- [Federated Edge Learning : Design Issues and Challenges](https://mlsanity.com/paper/2009.00081/) (2020)
- [Toward Next Generation Open Radio Access Network--What O-RAN Can and Cannot Do!](https://mlsanity.com/paper/2111.13754/) (2021)
- [5G-NIDD: A Comprehensive Network Intrusion Detection Dataset Generated over 5G Wireless Network](https://mlsanity.com/paper/2212.01298/) (2022)
- [RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN](https://mlsanity.com/paper/2111.06978/) (2021)
- [Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicing](https://mlsanity.com/paper/2208.01736/) (2022)
- [Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning](https://mlsanity.com/paper/2202.06439/) (2022)
- [EXPLORA: AI/ML EXPLainability for the Open RAN](https://mlsanity.com/paper/2310.13667/) (2023)
- [Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks](https://mlsanity.com/paper/2206.11328/) (2022)
- [Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures](https://mlsanity.com/paper/2209.14171/) (2022)
- [Explainable AI in 6G O-RAN: A Tutorial and Survey on Architecture, Use Cases, Challenges, and Future Research](https://mlsanity.com/paper/2307.00319/) (2023)
- [Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN)](https://mlsanity.com/paper/2201.07385/) (2022)
- [Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN](https://mlsanity.com/paper/2208.14394/) (2022)
- [On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration](https://mlsanity.com/paper/2211.03088/) (2022)
- [PACIFISTA: Conflict Evaluation and Management in Open RAN](https://mlsanity.com/paper/2405.04395/) (2024)
- [Intelligent Zero Trust Architecture for 5G/6G Networks: Principles, Challenges, and the Role of Machine Learning in the context of O-RAN](https://mlsanity.com/paper/2105.01478/) (2021)
- [How to Attack and Defend NextG Radio Access Network Slicing with Reinforcement Learning](https://mlsanity.com/paper/2101.05768/) (2021)
- [Self-play Learning Strategies for Resource Assignment in Open-RAN Networks](https://mlsanity.com/paper/2103.02649/) (2021)
- [Predictive Closed-Loop Service Automation in O-RAN based Network Slicing](https://mlsanity.com/paper/2202.01966/) (2022)
- [User Trajectory Prediction in Mobile Wireless Networks Using Quantum Reservoir Computing](https://mlsanity.com/paper/2301.08796/) (2023)
- [Successive Data Injection in Conditional Quantum GAN Applied to Time Series Anomaly Detection](https://mlsanity.com/paper/2310.05307/) (2023)
- [System-level Analysis of Adversarial Attacks and Defenses on Intelligence in O-RAN based Cellular Networks](https://mlsanity.com/paper/2402.06846/) (2024)
- [Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN](https://mlsanity.com/paper/2410.03737/) (2024)
- [A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches](https://mlsanity.com/paper/2208.09125/) (2022)
- [Implementing and Evaluating Security in O-RAN: Interfaces, Intelligence, and Platforms](https://mlsanity.com/paper/2304.11125/) (2023)
- [When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges](https://mlsanity.com/paper/2201.04677/) (2022)
- [Energy-aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning](https://mlsanity.com/paper/2208.09956/) (2022)

## Built on by (most cited first, 4 of 4)

- [Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recovery](https://mlsanity.com/paper/2604.01049/) (2026)
- [When Critics Disagree: Adaptive Reward Poisoning Attacks in RIS-Aided Wireless Control System](https://mlsanity.com/paper/2605.20037/) (2026)
- [Securing the Future of IoMT in the Post-Quantum Era: An Edge-Native Federated Learning Approach](https://mlsanity.com/paper/2606.14515/) (2026)
- [AI-RAN on NPUs: Baseband Processing Without Baseband Chips](https://mlsanity.com/paper/2607.04224/) (2026)

## Abstract (arXiv)

As wireless communication systems become more advanced, Open Radio Access Networks (O-RAN) stand out as a notable framework that promotes interoperability and cost-effectiveness. An examination of the progression of RAN architectures, as well as O-RAN's underlying principles, reveals the importance of machine learning (ML) in addressing various challenges, including spectrum management, resource allocation, and security. Hence, this survey provides a comprehensive overview of the integration of ML within O-RAN, highlighting its transformative potential in enhancing network performance and efficiency. This survey aims to describe the current status of ML applications in O-RAN while indicating possible directions for future research by analyzing existing literature. The findings aim to assist researchers and stakeholders in formulating optimal service strategies and advancing the understanding of intelligent wireless networks.