# 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  
Different evaluation techniques and performance comparisons based on different metrics for RRH clustering techniques have been proposed by researchers. Table 4 presents the evaluation techniques and the performance metrics for the RRH clustering methods reviewed in this work (listed in Table 5). \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Evaluation Techniques** & **Performance Metrics** \\ \hline
[49] & Simulation and performance comparison with other existing C-RAN schemes & QoS (number of expensive inter-cluster handover) and resource (RRH, hosts, and energy) consumption \\ \hline
[52] & Simulation and comparison with classical bin packing algorithm and comparison of heuristic solution with optimal solution & Number of active BBUs, energy efficiency, power-saving, and mean throughput per user \\ \hline
[48] & Simulation and comparison of heuristic solution with optimal solution & Power saving, re-association rate of users, and mean throughput per user \\ \hline
[50] & Simulation and comparison with FDD bin packing algorithm & Computational resource gain and power saving \\ \hline
[57] & Simulation and comparison with the optimal exhaustive search-based solution, no-clustering solution, and grand coalition & Number of active BBUs, user interference, user throughput, power consumption, and network utility \\ \hline
[53] & Simulation and comparison with a centralized algorithm and bin-packing algorithm & Power saving, re-association rate of users, mean throughput per user, and execution time \\ \hline
[51] & Simulation and performance comparison with grand coalition and no-clustering method & Number of active BBUs, throughput, power consumption, handover \\ \hline
[54] & Simulation and performance comparison among three proposed techniques & Number of active BBUs, number of clusters and resource blocks \\ \hline
[58] & Simulation and performance comparison with literature model and the optimal approach & BBU load balancing with number of users \\ \hline
[59] & Simulation and performance comparison with optimal ILP by CPLEX and nearest-first scheme & System costs for different numbers of RRHs, UEs, and average arrival rate \\ \hline
[63] & Simulation and performance comparison with the optimal solution and no-clustering scheme & End-users throughput, spectral efficiency, and execution time \\ \hline
[60] & Simulation and performance comparison with ES and k-means clustering & QoS, blocked users, and handovers \\ \hline
[61] & Simulation and performance comparison of DPSO with GA and ES, and CDI-CRAN with F-CRAN & QoS, load fairness index, network throughput, and handover \\ \hline
[55] & Training the DL model and performance comparison of the test set with traditional, ARIMA-DCCA, WANN-DCCA and MuLSTM-DC methods & Traffic forecast error, average capacity utility, and overall deployment cost \\ \hline
[62] & Simulation and performance comparison of GA with ES & Number of blocked connections, QoS \\ \hline
[56] & Simulation and performance comparison with Main Resource Packing, No UE Aggregation, Two-stage Optimization and ES & Number of active BBUs \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation techniques and performance metrics used in RRH clustering technique. In [49], a complexity analysis of the proposed location-aware VBS clustering algorithm and location and mobility-aware packing algorithm, and a comparison of their performances with those of other existing schemes in terms of QoS (number of expensive inter-cluster handover) and resource (RRH, hosts, and energy) consumption were described. In [52], the performance of IACA was compared with that of the classical bin packing algorithm through simulation. In addition, the heuristic solution was compared with the optimal solution in terms of the number of active BBUs,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
energy efficiency, power saving, and mean throughput per user.