Slice aware baseband function splitting and placement in disaggregated 5G Radio Access Network

Efficient utilization of network resources is becoming increasingly challenging for mobile network operators (MNOs) due to the widespread adoption of 5G and Beyond 5G (B5G) networks. With the introduction of functional splits in 5G Radio Access Network (RAN), the baseband functions are disaggregated and placed in geographically separated locations, remarkably improving the flexibility and efficiency of RAN. However, such disaggregation also brings new challenges when it comes to determining the most appropriate placement option for the baseband functions. Different functional splits and network slices have different requirements in terms of delay and data rate. Moreover, the processing nodes of the edge clouds and regional clouds, as well as the transport links, have different capacities. All these factors can affect the placement option for the baseband functions. Though significant efforts have been put into addressing these challenges in recent works, further exploration is required to develop more efficient baseband function placement solutions. To this end, we aim to develop a resource-efficient strategy that considers both functional splits and network slice-specific requirements, limited capacities of transport links, and processing nodes to reduce the Operational Expenditure (OPEX) of RAN. One of the significant factors contributing to the OPEX stems from the active processing nodes in the network. Hence, the primary goal of this work is to minimize the cost of active processing nodes in the edge and regional cloud. We propose an optimization model using Mixed Integer Linear Programming (MILP) that selects appropriate functional split, baseband function placement options, and the set of paths for routing the traffic from different RAN slices. We perform extensive simulations and show that the proposed model incurs a lower cost than baseline strategies while placing the baseband functions in the network. To address the high computational complexity of the optimization model, we also introduce a novel polynomial time heuristic algorithm. Although the heuristic incurs a higher cost than the MILP, we show that it takes significantly less execution time, making it applicable to large-scale deployment scenarios.