# CoMP-Aware BBU Placements for 5G Radio Access Networks over Optical Aggregation Networks
## 2 Related Work
The authors in [25] proposed an efficient dynamic-resourceinfrastructure management algorithm based on a combination of Markov-Process and Time Division Multiple Access (TDMA) protocols in Software-Based Networks. In [26], the authors co-located the BBU pool with an edge cloud at the so-called BBU node. They proposed a heuristic algorithm to allocate the users' tasks to the BBU nodes and to route the traffic from the RRH to that node. Then, they solved the problem by taking into consideration survivability concerns. They use both primary and backup BBU nodes to issue the request such that the primary path and backup path are disjointly linked. Recently, machine learning has gained increasing attention in optimizing 5G networks. The authors in [27] used Deep Reinforcement Learning (DRL) algorithms to solve Base Band Function (BBF) placements and routing in C-RAN and Next Generation RAN (NG-RAN). The authors show that DRL enables the network operator to solve the problem dynamically according to the traffic demand. In [28], the authors developed a model to allocate the RRH to a certain BBU pool in the C-RAN. The model is based on a deep neural network algorithm that can accurately predict future reconfigurable optical add-drop multiplexer (ROADM) network resource requirements. Several studies have been conducted in the literature investigating the CoMP performance in different mobile technologies (e.g., 4G and 5G). CoMP in 4G LTE networks has been widely investigated; in [29], the authors show that CoMP leads to network throughput and capacity expansions in LTE-A. They show the enhancement in terms of spectral efficiency through cooperative coordination strategies. In [30], the authors propose a CoMP model that demonstrates the performance of an LTE-A network at the cell edge for high capacities in order to improve the throughput and spectral efficiency. In [31], the authors designed an adaptive online learning approach and smart access for CoMP with carrier aggregation in order to improve network performance. In [32], the authors propose different scenarios of the CoMP model dealing with backhaul traffic in order to improve network throughputs in LTE-A network. In [33], the authors propose an algorithm to coordinate the clusters of the cells as well as a Radio Coordination Controller (RCC) placement optimization problem. This results in an overall improvement in CoMP performance. More studies related to our research on CoMP in 5G networks are intensely discussed. In [34], the study discusses the enhancement of user throughput in C-RAN by distributing the available resources of cooperative cells among CoMP and non-CoMP users. In [35], the authors propose a novel JT-CoMP scheme with a path loss model to minimize the network's back-haul traffic. The authors in [36; 37] integrate CoMP with NOMA in order to improve the cell edge users' performance. In [38], the authors study CoMP joint transmission clustering problems by taking into consideration the caches at base stations and mmWave mesh backhaul. The authors propose a heuristic algorithm to minimize the backhaul traffic while achieving the date rate requirement of each user. In [39], the authors study the effect of CoMP transmission on Ultra-dense networks (UDNs) complexity and transmission delay with collaboration among Mobile Edge Computing (MEC) servers. In [40], the authors propose a C-RAN system model with dynamic clustering approaches in order to reduce the inter-cluster interference. In [41], the authors investigate a joint beamforming coordination and user-selection problem on CoMP-based New Radio-Unlicensed (NR-U) networks. The authors propose an optimum solution and suboptimum solution in order to solve Mixed-Integer Non-Linear Programming (MINLP) problems. In [42], the authors propose deep learning algorithms in order to improve the performance of Downlink-coordinated Multipoint (DL-CoMP) in heterogenous 5G New Radio (NR) networks. In [43], the authors propose User-Centric Clustering algorithms in order to select UEs that will operate JT-CoMP.