# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 3 Resource Management Techniques in C-RAN
### Computational Resource Management Based on RRH Clustering Techniques
#### 3.2.2 Load-aware RRH Clustering  
[PERSON] et al. formulated the RRH clustering problem as a generalization of the classical bin packing optimization problem. Here, BBUs are considered as bins, and RRHs are the item sets to be packed [50]. They designed DRA, a dynamic RRH assignment algorithm for offloading one or more RRHs from an overloaded BBU to a less loaded BBU having computing resources available to serve the incoming RRH(s), thereby forming a new cluster. DRA reduces the number of active BBUs by 87% in comparison with one-to-one BBU-RRH mapping and consumes 25% of the time required by First-fit Decreasing (FFD) for the urban cellular deployment of 1000 RRHs. In [51], [PERSON] et al. formulated the RRH clustering problem as a coalition formation game. The objective was to maximize the network performance by balancing the network throughput, handover frequency, and power consumption. They proposed a centralized approach based on an exhaustive search and a distributed approach based on the merge-and-split rule to form clusters, following either the utilitarian order (which considers the global network utility) or the Pareto order (which considers individual RRH utility). They defined the utility functions considering the network performance (specifically, the throughput and handover frequency) as well as the network power consumption at the cloud side. In the centralized approach, an exhaustive search algorithm explores all the feasible partitions and selects the best one according to either the Pareto order or the utilitarian  
Figure 5: BBU-RRH mapping: (**a**) one-to-one mapping (**b**) one-to-many mapping. order. This search becomes intractable because the number of partitions increases rapidly. Therefore, a distributed approach is adopted to overcome this complexity. The author of [52] and [48] formulated a joint optimization problem in [53] for minimizing the power consumption and handover rate of UEs. The author solved it via a hybrid algorithm consisting of two stages. Unlike the previous works, this algorithm requires less amount of signaling load information. In the first stage, a central controller predefines the number of active BBUs. In addition, all the RRHs compete (based on a potential game among themselves) to select the most adequate BBU with the lowest cost, until a Nash equilibrium is attained. The second stage is based on a centralized scheme to activate or deactivate a BBU based on the throughput per cluster, and the number of users served per cluster when a BBU is deactivated. This method reduces the signaling load over the fronthaul link between BBUs and RRHs because RRHs are not required to send their load and radio conditions to the BBUs. [PERSON] et al. formulated the RRH clustering problem to minimize the number of activated BBUs based on the current state of the user and dynamic resource requirement [54]. A modified k-means clustering technique and two heuristic solutions are proposed to form the RRH clusters. In k-means, the input is the matrix of resource blocks. Here, a row represents an RRH, and each RRH is clustered based on the pattern of resource blocks that it provides to each user. This algorithm is modified such that an overloaded BBU or cluster migrates its users to the least overloaded cluster, and the migrated users who have the minimum difference in the reserved resource blocks between its current cluster and the migrated cluster are selected. The two heuristic solutions (where one takes the next maximum, and the other takes the subsequent minimum) create RRH clusters in two steps. This is because both require user assignment to an RRH before it is assigned to a BBU prior to the clustering, to achieve efficient load balancing. In addition, this study proposed the concept of developing an independent mini distributed antenna system in an indoor C-RAN for 4G networks, to increase user rate provisioning with fewer resource blocks as well as to minimize the number of BBUs. In [55], [PERSON] et al.