The energy efficiency of 5G and beyond 5G(B5G) networks is critical for reducing the high operational expenditure (OPEX) of mobile network operators. In 5G RAN, functional split enables the disaggregation of baseband functions, which significantly increases energy efficiency but induces various challenges in the placement of baseband functions. Various recent works have focused on addressing these challenges; however, most of the solutions do not consider the delay and data rate requirements of different slices as well as different functional splits. In this work, we aim to develop an energy-efficient baseband function placement strategy that jointly considers different functional splits and network slice-specific requirements. We formulate an Integer Linear Program (ILP) based optimization model to minimize the energy consumption in the network by selecting appropriate functional split and baseband function placement options for RAN slices. We show that our proposed model outperforms the baseline strategies in providing energy efficient baseband function placement solution. To tackle the computational complexity of ILP, we also design a polynomial time heuristic algorithm that can be applied in large-scale scenarios.

Network slicing is a key feature of 5G and beyond networks. Intelligent management of slices is important for reaping its highest benefits which needs further exploration. Focusing only on one goal as revenue maximization or cost minimization may not generate the highest profit for infrastructure providers in the long run. In this paper we jointly consider online admission and placement of Radio Access Network (RAN) slices with two objectives - a) maximizing revenue from accepting slices which are more profitable in the long run, and b) minimizing the cost to deploy them in Open RAN (O-RAN) enabled network by placing the slices efficiently. We formulate it as an optimization problem and propose a Deep Reinforcement Learning (DRL) based solution using Proximal Policy optimization (PPO). We compare our model with a state-of-the-art DRL based admission control solution and a greedy heuristic. We show that our proposed solution can efficiently adapt to dynamic load conditions. We also show that the proposed solution results in better performance to maximize the overall profit for infrastructure providers in comparison to the baselines.

5G Mobile network will reap the benefits from key technologies like Software Defined Networking and Network Function Virtualization. Cloud Radio Access Network architecture (Cloud RAN) is proven to be a promising architecture, but fully centralized Cloud RAN imposes a great bandwidth requirement in the fronthaul link. Different functional split options for 5G RAN have been proposed which lead to a trade-off between centralization and bandwidth requirement. Functional split at different granularity such as per cell, per logical network (slice), per user, or per bearer, have been an area of interest. To explore the effect of slice granularity in adaptive splits for slices, we formulate slice centric functional split in 5G RAN as an ILP to maximize centralization of baseband processing. By varying the slice granularity from macro slicing to micro slicing, we observe how slice centric split can impact centralization benefit of the network. We show that with increasing slice granularity slice centric split can render more centralization benefit in some scenarios but a trade off exists between centralization benefit and migration cost in the network which should be carefully considered in real deployment scenario.