# CoMP-Aware BBU Placements for 5G Radio Access Networks over Optical Aggregation Networks
## 2 Related Work  
C-RAN has been studied intensely in the literature. The concept of C-RAN has attracted the attention of many researchers. For example, the authors of [13] provided a comprehensive survey on the recent research direction of C-RAN. In [14], the authors propose a model to optimize the energy consumption of Heterogenous C-RAN (H-CRAN). The model considers the energy consumption of radio access points (RAPs), fronthaul, and the BBU pool. In [15], the authors propose a novel resource allocation model that optimizes the energy consumption of a C-RAN. The resource allocation problem is split into two subproblems--namely, the Bandwidth Power Allocation (BPA) and the BBU Energy-Aware Resource Allocation (EARA). In [8], the authors propose an analytical model to evaluate the multiplexing gain obtained from deploying C-RAN compared to the Distributed RAN (D-RAN). In C-RAN, the front-haul network restricted requirements in terms of end-to-end delay and data rate [16], which led to the introduction of optical aggregation networks based on wavelength division multiplexing (WDM) as an efficient solution to fulfill the aforementioned requirements. In this context, several studies have discussed the BBU placement problem using both ILP model. In [17], the authors introduce a BBU placement optimization problem over WDM aggregation networks using ILP to minimize the deployment cost. The model is not limited for BBU placement but also the electronic switches placement. The authors compare between the impact of two different fronthaul transport options, Optical Transport Network (OTN) and Overlay, on BBU centralization. The authors in [18] propose a cost-efficient placement model for RRHs, BBUs and optical fiber in 5G Radio-Optical communication Networks (5G-RONs). The problem is formulated using ILP. The authors aim to solve the problem taking into consideration achieving certain level of population coverage. The authors discuss the difficulty of practical solving ILP models in 5G-RONs scenarios. Similarly, authors in [19] propose a model for BBU placement that optimizes the total cost of ownership (TCO) using ILP under different delay thresholds. They considered the Time and Wavelength Division Multiplexing Passive Optical Network (TWDM-PON) as a fronthaul with different splitting ratios. In [20], the authors propose TWDM-PON architecture to support both the fronthaul network of the Cloud-Fog Radio Access Network (CF-RAN) and the interconnection of cell-sites to the fog nodes. The authors propose an ILP model that decides the number of fog nodes necessary for accommodating all traffic demands and where to process them considering TWDM-PON's capacity and the best overall network energy performance. The BBU placement problem is investigated using different heuristic algorithms to solve the problem for large network scenarios. In [21], the authors propose a BBU placement model considering different functional split options. The authors aimed to reduce the fronthaul latency and minimized cost reductions. They formulated an ILP optimization problem and then developed a heuristic algorithm proposed to solve the proposed model for large network scenarios. In [22], the authors present a model to optimize the storage allocation problem and content placement problem jointly in a hierarchical cache-enabled C-RAN architecture for IoT sensing services. They formulate the joint problem as an integer linear programming (ILP) model with the objective to minimize the total network's traffic cost. Then, two heuristic algorithms were proposed in order to reduce the computational complexity of ILP. In [23], the authors tackle the BBU placement problem while considering network survivability. The authors propose an ILP optimization problem to determine the primary and secondary locations of the BBU pools, as well as the primary and secondary paths between the RRH and BBU pool. Similar studies were conducted in [24] while using heuristic algorithms.