# Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures

arXiv: https://arxiv.org/abs/2209.14171 · HTML: https://mlsanity.com/paper/2209.14171/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 64 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 18 of 18)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks](https://mlsanity.com/paper/1802.06958/) (2018)
- [ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms](https://mlsanity.com/paper/2112.09559/) (2021)
- [End-to-End Simulation of 5G mmWave Networks](https://mlsanity.com/paper/1705.02882/) (2017)
- [Handover Control in Wireless Systems via Asynchronous Multi-User Deep Reinforcement Learning](https://mlsanity.com/paper/1801.02077/) (2018)
- [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)
- [Predictive Closed-Loop Service Automation in O-RAN based Network Slicing](https://mlsanity.com/paper/2202.01966/) (2022)
- [OpenRAN Gym: An Open Toolbox for Data Collection and Experimentation with AI in O-RAN](https://mlsanity.com/paper/2202.10318/) (2022)
- [When Machine Learning Meets Wireless Cellular Networks: Deployment, Challenges, and Applications](https://mlsanity.com/paper/1911.03585/) (2019)
- [Improved Handover Through Dual Connectivity in 5G mmWave Mobile Networks](https://mlsanity.com/paper/1611.04748/) (2016)
- [Dynamic CU-DU Selection for Resource Allocation in O-RAN Using Actor-Critic Learning](https://mlsanity.com/paper/2110.00492/) (2021)
- [Implementation of A Spatial Channel Model for ns-3](https://mlsanity.com/paper/2002.09341/) (2020)
- [User-centric Handover in mmWave Cell-Free Massive MIMO with User Mobility](https://mlsanity.com/paper/2105.09041/) (2021)

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

- [A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems](https://mlsanity.com/paper/2212.01351/) (2022)
- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework](https://mlsanity.com/paper/2302.02711/) (2023)
- [On-Device LLM for Context-Aware Wi-Fi Roaming](https://mlsanity.com/paper/2505.04174/) (2025)
- [On the use of Probabilistic Forecasting for Network Analysis in Open RAN](https://mlsanity.com/paper/2407.14375/) (2024)
- [MobiLLM: An Agentic AI Framework for Closed-Loop Threat Mitigation in 6G Open RANs](https://mlsanity.com/paper/2509.21634/) (2025)
- [Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning](https://mlsanity.com/paper/2210.05582/) (2022)
- [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)
- [AutORAN: LLM-driven Natural Language Programming for Agile xApp Development](https://mlsanity.com/paper/2603.18604/) (2026)
- [System One Models for Wireless Decision-Making:Applications and Performance Evaluation](https://mlsanity.com/paper/2610.04345/) (2026)
- [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)

## Abstract (arXiv)

5G and beyond mobile networks will support heterogeneous use cases at an unprecedented scale, thus demanding automated control and optimization of network functionalities customized to the needs of individual users. Such fine-grained control of the Radio Access Network (RAN) is not possible with the current cellular architecture. To fill this gap, the Open RAN paradigm and its specification introduce an open architecture with abstractions that enable closed-loop control and provide data-driven, and intelligent optimization of the RAN at the user level. This is obtained through custom RAN control applications (i.e., xApps) deployed on near-real-time RAN Intelligent Controller (near-RT RIC) at the edge of the network. Despite these premises, as of today the research community lacks a sandbox to build data-driven xApps, and create large-scale datasets for effective AI training. In this paper, we address this by introducing ns-O-RAN, a software framework that integrates a real-world, production-grade near-RT RIC with a 3GPP-based simulated environment on ns-3, enabling the development of xApps and automated large-scale data collection and testing of Deep Reinforcement Learning-driven control policies for the optimization at the user-level. In addition, we propose the first user-specific O-RAN Traffic Steering (TS) intelligent handover framework. It uses Random Ensemble Mixture, combined with a state-of-the-art Convolutional Neural Network architecture, to optimally assign a serving base station to each user in the network. Our TS xApp, trained with more than 40 million data points collected by ns-O-RAN, runs on the near-RT RIC and controls its base stations. We evaluate the performance on a large-scale deployment, showing that the xApp-based handover improves throughput and spectral efficiency by an average of 50% over traditional handover heuristics, with less mobility overhead.