# Intelligent O-RAN Traffic Steering for URLLC Through Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2303.01960 · HTML: https://mlsanity.com/paper/2303.01960/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [A Deeper Look at Experience Replay](https://mlsanity.com/paper/1712.01275/) (2017)
- [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)
- [Maximum Reward Formulation In Reinforcement Learning](https://mlsanity.com/paper/2010.03744/) (2020)
- [Joint Energy Efficient and QoS-aware Path Allocation and VNF Placement for Service Function Chaining](https://mlsanity.com/paper/1710.02611/) (2017)
- [Reinforcement Learning Algorithm for Traffic Steering in Heterogeneous Network](https://mlsanity.com/paper/2111.15029/) (2021)
- [Downtime-Aware O-RAN VNF Deployment Strategy for Optimized Self-Healing in the O-Cloud](https://mlsanity.com/paper/2110.06060/) (2021)

## Built on by (most cited first, 1 of 1)

- [Federated Neuroevolution O-RAN: Enhancing the Robustness of Deep Reinforcement Learning xApps](https://mlsanity.com/paper/2506.12812/) (2025)

## Abstract (arXiv)

The goal of Next-Generation Networks is to improve upon the current networking paradigm, especially in providing higher data rates, near-real-time latencies, and near-perfect quality of service. However, existing radio access network (RAN) architectures lack sufficient flexibility and intelligence to meet those demands. Open RAN (O-RAN) is a promising paradigm for building a virtualized and intelligent RAN architecture. This paper presents a Machine Learning (ML)-based Traffic Steering (TS) scheme to predict network congestion and then proactively steer O-RAN traffic to avoid it and reduce the expected queuing delay. To achieve this, we propose an optimized setup focusing on safeguarding both latency and reliability to serve URLLC applications. The proposed solution consists of a two-tiered ML strategy based on Naive Bayes Classifier and deep Q-learning. Our solution is evaluated against traditional reactive TS approaches that are offered as xApps in O-RAN and shows an average of 15.81 percent decrease in queuing delay across all deployed SFCs.