Reinforcement Learning Based Dynamic Function Splitting in Disaggregated Green Open RANs

TL;DR

This paper introduces a reinforcement learning-based method for dynamic function splitting in disaggregated Open RANs, optimizing renewable energy use and reducing operational costs in mobile networks.

Contribution

It proposes a novel RL-based dynamic function splitting approach tailored for green Open RANs, considering renewable energy variability and traffic demands.

Findings

Effective use of renewable energy sources demonstrated

Significant reduction in operational costs achieved

Insights into RES and battery sizing for network optimization

Abstract

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.