# Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework

arXiv: https://arxiv.org/abs/2302.02711 · HTML: https://mlsanity.com/paper/2302.02711/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 58 references that are in the map.
- First posted on arXiv 6 Feb 2023.

## Builds on (most cited first, 9 of 9)

- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures](https://mlsanity.com/paper/2209.14171/) (2022)
- [Reinforcement Learning Based Dynamic Function Splitting in Disaggregated Green Open RANs](https://mlsanity.com/paper/2012.03213/) (2020)
- [Intelligent Traffic Steering in Beyond 5G Open RAN based on LSTM Traffic Prediction](https://mlsanity.com/paper/2210.08829/) (2022)
- [Joint Path Selection and Rate Allocation Framework for 5G Self-Backhauled mmWave Networks](https://mlsanity.com/paper/1805.07743/) (2018)
- [Backhaul-Aware Interference Management in the Uplink of Wireless Small Cell Networks](https://mlsanity.com/paper/1308.5835/) (2013)
- [Proportional Fair Traffic Splitting and Aggregation in Heterogeneous Wireless Networks](https://mlsanity.com/paper/1508.05542/) (2015)

## Built on by (most cited first, 3 of 3)

- [Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach](https://mlsanity.com/paper/2304.12502/) (2023)
- [LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN](https://mlsanity.com/paper/2504.18062/) (2025)
- [Oranits: Mission Assignment and Task Offloading in Open RAN-based ITS using Metaheuristic and Deep Reinforcement Learning](https://mlsanity.com/paper/2507.19712/) (2025)

## Abstract (arXiv)

To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iii) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.