# 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
#### 3.2.6 Evaluation Techniques for RRH Clustering Methods
However, in [48], which is an extension of [52], whereas the performance evaluation was performed similarly, the performance metrics considered were power saving, re-association rate, and mean throughput per user. The authors in [51] compared the performance of the optimal centralized approach (as centralized Pareto or centralized utilitarian, named based on the approach) with those of the grand coalition and the no-clustering method through simulation. The comparison was in terms of the number of active BBUs, throughput, power consumption, and handover for different traffic load scenarios. Furthermore, the performances of the distributed and centralized approaches were compared using the Pareto and utilitarian order, with respect to the number of active BBUs, utility function, and throughput. It was demonstrated that the distributed approaches achieve performance very close to those of the centralized approaches in all the cases. The performance of the algorithm in [57] was compared with the optimal exhaustive search-based solution, no-clustering solution (in which one BBU is exclusively dedicated to one RRH), and grand coalition (in which all the RRHs are connected to one BBU). However, in [53], the performance of the algorithm was evaluated by comparing it with a centralized algorithm and bin-packing algorithm, which displays a good tradeoff between power saving and re-association rate. In [54], the modified k-means technique and the two heuristic algorithms were simulated and tested on MATLAB and compared in terms of the number of activated BBUs and the number of clusters and resource blocks. K-means performed well in terms of the number of clusters and tightness of the clusters, although it required a longer time to attain convergence. In [58], through simulation, the performance of the proposed algorithm was compared with a solution proposed in the literature [60], having an identical scenario and another optimal solution that divides the users between BBU sectors without considering their distributions in the RRHs. The load balancing of BBUs considering a different number of users was considered as the performance metric. It was demonstrated that the PSO could converge faster with fewer iterations than those for the solution provided in the literature, and optimize the QoS by reasonably balancing the BBU-RRH sectors. In [59], the LAGA-BFD algorithm for solving the joint optimization of user association and BBU-RRH mapping problem was simulated. Furthermore, the performance was evaluated with different numbers of RRHs (6-14), a different number of UEs, and different traffic arrival rates, considering more and less stringent QoS requirements. The impact of all the above parameters on the system cost was compared while comparing the proposed method with the optimal ILP by using CPLEX and the nearest-first scheme. In [63], the proposed heuristic for solving the k-MCKP-based clustering technique was simulated via Monte Carlo simulation in MATLAB. Then, the performance was compared with the optimal solution and two no-clustering approaches (no-clustering upper bound and no-clustering lower bound) in terms of the end-user throughput, spectral efficiency, and execution time. However, in [60], for analyzing and verifying the performance of the proposed algorithm for RRH allocation (SOCRAN), three problem scenarios are presented by varying the numbers of RRHs and BBUs, and the sector served by the BBUs. The algorithm was tested 30 times over 30 configurations for each benchmark. The average of the results obtained was considered for Monte Carlo analysis. The performances of both the GA and DPSO in the SOCRAN algorithm were compared with those of exhaustive search and the k-means clustering algorithm in terms of the QoS, the number of blocked users, and the number of handovers, via simulation. In [61], the performance of DPSO was analyzed in two problem scenarios considering small and large networks and compared with those of the GA and ES algorithm.