# Reinforcement Learning Based Dynamic Function Splitting in Disaggregated Green Open RANs

arXiv: https://arxiv.org/abs/2012.03213 · HTML: https://mlsanity.com/paper/2012.03213/

## Key facts

- Cited by 7 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 6 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN](https://mlsanity.com/paper/2111.06978/) (2021)
- [Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework](https://mlsanity.com/paper/2302.02711/) (2023)
- [IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture](https://mlsanity.com/paper/2209.07382/) (2022)
- [Choose, not Hoard: Information-to-Model Matching for Artificial Intelligence in O-RAN](https://mlsanity.com/paper/2208.04229/) (2022)
- [To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs](https://mlsanity.com/paper/2302.07399/) (2023)
- [A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration](https://mlsanity.com/paper/2312.16142/) (2023)
- [EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RAN](https://mlsanity.com/paper/2602.09206/) (2026)

## Abstract (arXiv)

With the growing momentum around Open RAN (O-RAN) initiatives, performing dynamic Function Splitting (FS) in disaggregated and virtualized Radio Access Networks (vRANs), in an efficient way, is becoming highly important. An equally important efficiency demand is emerging from the energy consumption dimension of the RAN hardware and software. Supplying the RAN with Renewable Energy Sources (RESs) promises to boost the energy-efficiency. Yet, FS in such a dynamic setting, calls for intelligent mechanisms that can adapt to the varying conditions of the RES supply and the traffic load on the mobile network. In this paper, we propose a reinforcement learning (RL)-based dynamic function splitting (RLDFS) technique that decides on the function splits in an O-RAN to make the best use of RES supply and minimize operator costs. We also formulate an operational expenditure minimization problem. We evaluate the performance of the proposed approach on a real data set of solar irradiation and traffic rate variations. Our results show that the proposed RLDFS method makes effective use of RES and reduces the cost of an MNO. We also investigate the impact of the size of solar panels and batteries which may guide MNOs to decide on proper RES and battery sizing for their networks.