# OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN

arXiv: https://arxiv.org/abs/2201.05632 · HTML: https://mlsanity.com/paper/2201.05632/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 14 Jan 2022.

## Builds on (most cited first, 14 of 14)

- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms](https://mlsanity.com/paper/2112.09559/) (2021)
- [5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning](https://mlsanity.com/paper/2106.05370/) (2021)
- [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)
- [Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation](https://mlsanity.com/paper/2110.10617/) (2021)
- [Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks](https://mlsanity.com/paper/1808.07647/) (2018)
- [DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks](https://mlsanity.com/paper/2012.14350/) (2020)
- [Sl-EDGE: Network Slicing at the Edge](https://mlsanity.com/paper/2005.00886/) (2020)
- [π-ROAD: a Learn-as-You-Go Framework for On-Demand Emergency Slices in V2X Scenarios](https://mlsanity.com/paper/2012.06208/) (2020)
- [CellOS: Zero-touch Softwarized Open Cellular Networks](https://mlsanity.com/paper/2004.03418/) (2020)
- [Towards Inference Delivery Networks: Distributing Machine Learning with Optimality Guarantees](https://mlsanity.com/paper/2105.02510/) (2021)
- [Toward Operator-to-Waveform 5G Radio Access Network Slicing](https://mlsanity.com/paper/1905.08130/) (2019)
- [PlaceRAN: Optimal Placement of Virtualized Network Functions in the Next-generation Radio Access Networks](https://mlsanity.com/paper/2102.13192/) (2021)

## Built on by (most cited first, 10 of 10)

- [OpenRAN Gym: AI/ML Development, Data Collection, and Testing for O-RAN on PAWR Platforms](https://mlsanity.com/paper/2207.12362/) (2022)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [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)
- [Elastic Federated Learning over Open Radio Access Network (O-RAN) for Concurrent Execution of Multiple Distributed Learning Tasks](https://mlsanity.com/paper/2305.02109/) (2023)
- [Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach](https://mlsanity.com/paper/2303.02657/) (2023)
- [Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN](https://mlsanity.com/paper/2507.18111/) (2025)
- [Adversarial Water-Filling: Theory, Algorithms, and a Domain-Specific Wireless Foundation Model](https://mlsanity.com/paper/2605.26163/) (2026)
- [From RAN Control to Agentic Intelligence: Architecture and Vision for Energy Efficient AI-RAN](https://mlsanity.com/paper/2606.21955/) (2026)
- [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026)

## Abstract (arXiv)

The next generation of cellular networks will be characterized by softwarized, open, and disaggregated architectures exposing analytics and control knobs to enable network intelligence. How to realize this vision, however, is largely an open problem. In this paper, we take a decisive step forward by presenting and prototyping OrchestRAN, a novel orchestration framework that embraces and builds upon the Open RAN paradigm to provide a practical solution to these challenges. OrchestRAN has been designed to execute in the non-real-time RAN Intelligent Controller (RIC) and allows Network Operators (NOs) to specify high-level control/inference objectives (i.e., adapt scheduling, and forecast capacity in near-real-time for a set of base stations in Downtown New York). OrchestRAN automatically computes the optimal set of data-driven algorithms and their execution location to achieve intents specified by the NOs while meeting the desired timing requirements. We show that the problem of orchestrating intelligence in Open RAN is NP-hard, and design low-complexity solutions to support real-world applications. We prototype OrchestRAN and test it at scale on Colosseum. Our experimental results on a network with 7 base stations and 42 users demonstrate that OrchestRAN is able to instantiate data-driven services on demand with minimal control overhead and latency.