Reinforcement Learning Based Dynamic Function Splitting in Disaggregated Green Open RANs

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.

Mapping the VNFs and VLs of a RAN Slice Onto Intelligent PoPs in Beyond 5G Mobile Networks

“…Other studies have examined the deployment of VNFs and allocation of virtual compute resources [42], the analysis of various functional split options under energy and cost constraints [49], the mapping of RAN slices with a special emphasis on isolation and resource allocation [50], the placement of VNFs [51], the scheduling of VNFs for RAN slices [52], the impact of functional split on fronthaul delay in a RAN slice [53], and the sharing of VNFs across multiple network slice instances (NSIs) [54]. For the sake of clarity, the aforementioned theoretical models are compared with respect to a number of attributes in Tab.…”