## 1 Introduction  
The increased level of demands for data transmission is a major issue that needs to be addressed in relation to cellular networks [1]. In 2016, there was an increase in data demand levels of 63 percent, with levels up 18-fold over five years [2]. The increased level of demand for data must be solved through increased cellular network performance. An increased number of base stations (BS) and spatial reuse are examples of solutions capable of solving this problem [3]. However, these solutions must be accompanied by strategies that prevent data transmission collisions, especially in edge cell regions [4]. Coordination among base stations is required in such cases. In order to prevent data transmission collisions, the base station coordination approach has been introduced by the Third Generation Partnership Project (3 GPP), Release-11 [5], in Long Term Evolution Advanced (LTE-A). This method is known as the Coordinated Multipoint (CoMP). CoMP has become one of the key methods in the fifth-generation (5G) wireless communication field. CoMP is a network cooperative method that mitigates inter-cell interference (ICI) from neighboring cells to provide higher spectral efficiency. CoMP provides benefits in many directions, such as extending cell coverage area and improving edge cell throughput [3; 4; 6; 7]. Downlink CoMP systems can be classified into two types: joint processing coordinated multipoint (JP-CoMP) and coordinated beamforming coordinated multipoint (CB-CoMP) [8]. The main difference between JP-CoMP and CB-CoMP lies in their implementation scheme, i.e., whether user data is shared across the cooperating cells via backhaul or not [6]. JP-CoMP exchanges data and CSI concurrently among cooperating base stations in a cluster [9], while CB-CoMP only shares CSI without exchanges of data among cooperating base stations in a cluster [10]. In the CoMP schemes, the formation of efficient clusters is a critical factor affecting overall CoMP performance. However, finding the optimal clustering approach typically requires combinatorial optimization due to the nonlinearity. In general, suboptimal clustering results in increased computational complexity, failures of proper data exchanges, and less optimal throughput performances of CoMP [8]. A limited backhaul capacity is another crucial factor that degrades JP-CoMP performance levels on the real-world networks. The cell clustering in CoMP has been widely studied in recent years as part of effort to improve inter-cell interference management [8]. Clustering algorithms are classified into two types: the static and dynamic clustering types. Static clustering methods have been proposed in order to optimize edge throughput cells by relying on a predetermined fixed base station cluster [8]. Each static clustering algorithm utilizes different strategies to determine the efficient cluster formation. Examples include overlapping [11], formation cooperative strategies [12], and sectoring [13]. These clustering methods have simple configurations, but the aforementioned works did not consider suitable methods for adapting clustering to changes on the network. To this end, dynamic clustering is introduced here to realize additional performance improvements. These methods utilizes different approaches to achieve optimal performance goals, i.e., dynamic network-centric clustering [14], the blossom tree algorithm [15], graph-based clustering [16], the use of sub-cluster [17], a novel re-clustering [18], coalitional game theory [19], density-based spatial clustering [20], the use of channel state prediction [21], a weight traffic model [22], the exchange-matching algorithm [23], mixed-integer nonlinear programming [24], and the successive convex algorithm [25]. Dynamic clustering adapts to network changes, but these methods are designed based on centralized control on the network, which requires extensive information sharing and high computational complexity.