# ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms

arXiv: https://arxiv.org/abs/2112.09559 · HTML: https://mlsanity.com/paper/2112.09559/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 63 references that are in the map.
- First posted on arXiv 17 Dec 2021.

## Builds on (most cited first, 16 of 16)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Reinforcement Learning with Deep Energy-Based Policies](https://mlsanity.com/paper/1702.08165/) (2017)
- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2002.06215/) (2020)
- [Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks](https://mlsanity.com/paper/1802.06958/) (2018)
- [Machine Learning in the Air](https://mlsanity.com/paper/1904.12385/) (2019)
- [Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues](https://mlsanity.com/paper/1809.08707/) (2018)
- [srsLTE: An Open-Source Platform for LTE Evolution and Experimentation](https://mlsanity.com/paper/1602.04629/) (2016)
- [Open, Programmable, and Virtualized 5G Networks: State-of-the-Art and the Road Ahead](https://mlsanity.com/paper/2005.10027/) (2020)
- [Intelligent O-RAN for Beyond 5G and 6G Wireless Networks](https://mlsanity.com/paper/2005.08374/) (2020)
- [Learning to Communicate: Channel Auto-encoders, Domain Specific Regularizers, and Attention](https://mlsanity.com/paper/1608.06409/) (2016)
- [Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation](https://mlsanity.com/paper/2110.10617/) (2021)
- [Handover Control in Wireless Systems via Asynchronous Multi-User Deep Reinforcement Learning](https://mlsanity.com/paper/1801.02077/) (2018)
- [RAN Resource Slicing in 5G Using Multi-Agent Correlated Q-Learning](https://mlsanity.com/paper/2107.01018/) (2021)
- [Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks](https://mlsanity.com/paper/1808.07647/) (2018)
- [Actor-Critic Learning Based QoS-Aware Scheduler for Reconfigurable Wireless Networks](https://mlsanity.com/paper/2102.00145/) (2021)

## Built on by (most cited first, 30 of 35)

- [Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures](https://mlsanity.com/paper/2209.14171/) (2022)
- [Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN](https://mlsanity.com/paper/2208.14394/) (2022)
- [OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN](https://mlsanity.com/paper/2201.05632/) (2022)
- [Attention-based Open RAN Slice Management using Deep Reinforcement Learning](https://mlsanity.com/paper/2306.09490/) (2023)
- ["X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systems](https://mlsanity.com/paper/2507.19657/) (2025)
- [OpenRAN Gym: AI/ML Development, Data Collection, and Testing for O-RAN on PAWR Platforms](https://mlsanity.com/paper/2207.12362/) (2022)
- [Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach](https://mlsanity.com/paper/2309.07265/) (2023)
- [Advancing RAN Slicing with Offline Reinforcement Learning](https://mlsanity.com/paper/2312.10547/) (2023)
- [Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning](https://mlsanity.com/paper/2309.01730/) (2023)
- [Near-Real-Time Resource Slicing for QoS Optimization in 5G O-RAN using Deep Reinforcement Learning](https://mlsanity.com/paper/2509.14343/) (2025)
- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [xApp Distillation: AI-based Conflict Mitigation in B5G O-RAN](https://mlsanity.com/paper/2407.03068/) (2024)
- [Average Reward Reinforcement Learning for Wireless Radio Resource Management](https://mlsanity.com/paper/2501.06700/) (2025)
- [Intelligent Closed-loop RAN Control with xApps in OpenRAN Gym](https://mlsanity.com/paper/2208.14877/) (2022)
- [MobiLLM: An Agentic AI Framework for Closed-Loop Threat Mitigation in 6G Open RANs](https://mlsanity.com/paper/2509.21634/) (2025)
- [SymbXRL: Symbolic Explainable Deep Reinforcement Learning for Mobile Networks](https://mlsanity.com/paper/2601.22024/) (2026)
- [SIA: Symbolic Interpretability for Anticipatory Deep Reinforcement Learning in Network Control](https://mlsanity.com/paper/2601.22044/) (2026)
- [A Family of Open Time-Series Foundation Models for the Radio Access Network](https://mlsanity.com/paper/2604.04271/) (2026)
- [Fairness Guaranteed and Auction-based x-haul and Cloud Resource Allocation in Multi-tenant O-RANs](https://mlsanity.com/paper/2301.00597/) (2023)
- [On AI Verification in Open RAN](https://mlsanity.com/paper/2510.18417/) (2025)
- [Confounding-Valid Conformal Inference for Counterfactual KPIs in Wireless Networks](https://mlsanity.com/paper/2609.05073/) (2026)
- [FORLORN: A Framework for Comparing Offline Methods and Reinforcement Learning for Optimization of RAN Parameters](https://mlsanity.com/paper/2209.13540/) (2022)
- [Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach](https://mlsanity.com/paper/2303.02657/) (2023)
- [A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration](https://mlsanity.com/paper/2312.16142/) (2023)
- [Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition](https://mlsanity.com/paper/2505.01783/) (2025)
- [BRAIN: Bayesian Reasoning via Active Inference for Agentic and Embodied Intelligence in Mobile Networks](https://mlsanity.com/paper/2602.14033/) (2026)
- [Intelligent Radio Resource Slicing for 6G In-Body Subnetworks](https://mlsanity.com/paper/2603.28529/) (2026)
- [Adversarial Water-Filling: Theory, Algorithms, and a Domain-Specific Wireless Foundation Model](https://mlsanity.com/paper/2605.26163/) (2026)
- [Explainable Runtime Dependency Tracking for AI-RAN Conflict Monitoring](https://mlsanity.com/paper/2606.06663/) (2026)
- [ORAN-DEFEND: Subspace Detection and Sanitization of Backdoor DRL xApps in Open RAN](https://mlsanity.com/paper/2607.06647/) (2026)

## Abstract (arXiv)

In spite of the new opportunities brought about by the Open RAN, advances in ML-based network automation have been slow, mainly because of the unavailability of large-scale datasets and experimental testing infrastructure. This slows down the development and widespread adoption of Deep Reinforcement Learning (DRL) agents on real networks, delaying progress in intelligent and autonomous RAN control. In this paper, we address these challenges by proposing practical solutions and software pipelines for the design, training, testing, and experimental evaluation of DRL-based closed-loop control in the Open RAN. We introduce ColO-RAN, the first publicly-available large-scale O-RAN testing framework with software-defined radios-in-the-loop. Building on the scale and computational capabilities of the Colosseum wireless network emulator, ColO-RAN enables ML research at scale using O-RAN components, programmable base stations, and a "wireless data factory". Specifically, we design and develop three exemplary xApps for DRL-based control of RAN slicing, scheduling and online model training, and evaluate their performance on a cellular network with 7 softwarized base stations and 42 users. Finally, we showcase the portability of ColO-RAN to different platforms by deploying it on Arena, an indoor programmable testbed. Extensive results from our first-of-its-kind large-scale evaluation highlight the benefits and challenges of DRL-based adaptive control. They also provide insights on the development of wireless DRL pipelines, from data analysis to the design of DRL agents, and on the tradeoffs associated to training on a live RAN. ColO-RAN and the collected large-scale dataset will be made publicly available to the research community.