## 5 Results and Discussion  
A single-cell H-CRAN environment is considered; the simulation parameters are summarized in Table 1. We set the parameters according to the 3 GPP specifications [19]. In the single macrocell with an intercell distance of 1 km, ten RRHs and variable UEs are randomly distributed. The UE number \(\mid U\mid\) is dynamically set by the simulation setting. Following the 3 GPP specifications [20], we consider Rayleigh fading and log-normal shadowing. We also consider the small-cell path loss model: \(140.7+36.7\text{log}\,d(\text{km})\), where \(d\) is the distance between the RRH and UE, and noise figure of 9 dB. The datasets used in the simulation are composed of taxi mobility trace data acquired in San Francisco [21]. In addition, another dataset with taxi mobility data acquired in Beijing is used to apply the algorithms to different mobilities [22]. We used the datasets to organize the history records at various speeds, denoted as 1x, 2x, and 4x. In addition, two distribution scenarios are considered, where \(d_{u}\) is set to have Gaussian or uniform distribution. We compare the proposed algorithm with other algorithms. First, the proposed algorithm is basically an online algorithm because of predicting mobility. So, the offline version algorithm without mobility prediction is used to analyze performance of the proposed algorithm. We denote the online algorithm with mobility prediction as 'Proposed (online)' and the offline algorithm as 'Proposed (offline)'. The difference between the online algorithm and the offline algorithm is in Equation (10) for switching RRH. In the online algorithm, Equation (10) uses the predicted RRH \(\widetilde{s}\), but the offline algorithm uses the current RRH \(s\). In other words, the offline algorithm performs RRH switching without the process of predicting user mobility, and the selection of RRHs is calculated based on current associated RRHs, not on predicted results. Second, the full-activation (FA) algorithm is used in the comparison to evaluate the baseline performance. Third, the algorithm in [4] in HetNet is used, which has decentralized small BSs. Fourth, for a comparison with the same network environment, the algorithm in [12] is used. They are denoted as 'activation algorithm in HetNet (A-HN)' and 'activation algorithm H-CRAN (A-HC)' in the following figures, respectively. In Figure 2, the energy efficiencies are compared for different datasets and distribution scenarios as a function of the number of users. For the scenarios presented in Figure 2a,c with Gaussian distributions, the proposed algorithm has a higher performance by approximately 42%-68% and 58%-77% than A-HN and A-HC, respectively. In the scenarios presented in Figure 2b,d with uniform distribution, the proposed algorithm has a higher performance by approximately 32-68% and 37%-68% compared to A-HN and A-HC, respectively. Compared to the FA algorithm in all of the scenarios, energy efficiency gains of approximately 43%-70% are achieved by the proposed algorithm. Compared to the offline algorithm, the proposed online algorithm outperforms approximately 17%-22% and 17%-36% in San Francisco and Beijing datasets, respectively. Also, the offline algorithm has a higher performance by about 15%-59% and 15%-56% compared to A-HN and A-HC, respectively.