# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 3 Resource Management Techniques in C-RAN
### Radio Resource Management Techniques
#### 3.1.4 Evaluation Techniques for Radio Resource Management Methods  
Different evaluation techniques and performance comparisons based on different metrics for the RRM techniques in C-RANs have been proposed by researchers. Table 3 presents the evaluation techniques together with the performance metrics for the RRM schemes reviewed in this work (which are mentioned in Table 2). The performance of the proposed two-step DRL-based approach in [39] is evaluated by comparing it with two widely used baselines: single BS association (SA) and full coordinated association (FA). It is illustrated that the approach yields better results in terms of the total power consumption and user demand in each decision time. In this work, the reduction in the action space size and state transition overheads (power consumption during a transition from active/sleep to sleep/active) via a two-step DRL-based framework was considered. [PERSON] et al. used simulations to compare the efficient local search algorithm as proposed in [40] with two other methods (no RRH selection and greedy-based RRH selection) in terms of the power consumption during each iteration with different numbers of RRHs as well as TDAs and with different spectral efficiency. It was demonstrated that the power consumption for different spectral efficiency requirements is almost equivalent. This indicates that the spectral efficiency is not a significant factor in the power consumption of the C-RAN. However, in [41], which is an extension of [40], the performance of the proposed method is evaluated through simulation and compared with the SINR-based scheme, cell range expansion, and the Min-power scheme, in terms of the number of satisfied users, active RRH for current users, and outage probability for different call departure rates. The authors in [42] evaluated the performance of the proposed MBFDthrough simulation. They demonstrated that the MBFD algorithm yields higher performance than BFD and traditional networks in minimizing the number of active BBUs and the power consumption for normalized traffic load in C-RAN. This is because MBFD achieves load balancing while saving energy, after modification from BFD. A simulation is presented and analyzed in terms of the traffic threshold and total power consumption of the network, for evaluating the performance of the method proposed in [43]. It was concluded that the consideration of only the power consumption of RRHs results in increased total power consumption of the network, because of the BBUs. Notwithstanding the marginal difference between the theoretical and simulation results, the proposed scheme reduces the total power consumption while optimizing BBU aggregation. In [44], the proposed DRAC algorithm was simulated in MATLAB, and the performance of the approach was compared with those of a few state-of-the-art schemes to demonstrate the high throughput satisfaction rate, minimal power consumption, and significant reduction in the number of BBUs required with the maximum number of RRHs handled per BBU. The DRAC scheme performs a good tradeoff between the satisfaction rate and overall power consumption for both the SINR threshold levels. In [45], the performance of the proposed algorithm RAAC was evaluated by means of simulation in a wireless LTE C-RAN environment with 19 hexagonal RRHs and three users uniformly distributed in each cell. Then, it was compared with a semi-definite positive relaxation-based algorithm (SDPRA) and a fast greedy algorithm (FGA) in terms of the number of admitted users, total transmission power, and number of BBUs. It was demonstrated that it increases user admission by 9% and 11%, respectively. Moreover, it saves 23% and 53%, respectively, more transmission power. In [46], the proposed approach was simulated using MATLAB with CVX tool, considering five mobile clones co-located with the BBUs in the BBU pool.