  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.
