## 3 Channel Characteristics Comparisons
### Cluster Characteristics  
The clustered delay line (CDL) model has been used widely in wireless mobile channel standardization, e.g., 3 GPP TR 38.901 [5]. In this model, a cluster means a group of similar MPCs, and MPCs can be clustered from delay and angle dimensions. Here, we use the K-Power-Means algorithm [40] with a novel initial step [41] to perform clustering. For mmWave channels, multipath information, including amplitude, delay, azimuth angle of arrival (AOA), and elevation angle of arrival (EOA), is estimated  
Figure 12: The results of K-factor and RMS DS. by the space-alternating generalized expectation-maximization (SAGE) algorithm [42]. For VLC channels, the ray tracing method is adopted and the parameters such as AOA and EOA can be obtained by geometric calculation. In Figure 12(a),b, the clustering results at the 10 th position are presented. Clusters for mmWave and VLC channels are marked with M and R, respectively. Moreover, the points with the same color belong to one cluster. It is clear that there are six clusters divided for mmWave while there are five clusters for VLC at the same position. Obviously, these two clustering results are different despite the similar cluster numbers that they have. It is apparent that the MPCs for mmWave can be distinguished more easily in the delay dimension for this measured scenario because the clusters seem to be located in different delay bins, with small fluctuations in AOA and EOA within one cluster. This shows the temporal sparsity in mmWave bands in this conference scenario. However, the difference can be found by investigating the clusters in Figure 12(b), which shows that the delay of different clusters is close while the angle dimension (AOA and EOA) varies dramatically, i.e., large angular spread in VLC bands, and we can obtain the same results at other points in this room. This can be explained by the radio wave propagation mechanisms. The light rays have a much smaller wavelength than mmWave signals. The reflection case occurs with similar directions when mmWave signals arrive the surfaces of materials, while the light rays are scattered randomly in all directions, resulting in a large angular spread, in VLC bands. In the standardization channel model, the number of clusters is commonly fixed and made equal to what exists in bands below 6 GHz. For example, the number of clusters is 15 for indoor LOS scenarios in [5]. However, sparsity in the mmWave bands makes this assumption unreasonable [43; 44]. The typical cluster numbers reported in [45; 46] are small and random with a Poisson distribution. In our work, Figure 13(a),b present the statistics of cluster numbers. The Poisson distribution is used to fit the cluster numbers. The fitting parameter \(\lambda\) denotes the average value of cluster numbers, and they are 5.71 and 6.38 for mmWave and VLC, respectively. Here, we consider that the \(\lambda\) is 5 in mmWave bands, and it is 6 in VLC bands because the cluster numbers are expected to be integers. To confirm our results in the conference scenario, the statistical test method in Statistics, e.g., Kolmogorov-Smirnov test (KS-test) [47], is used to decide if the cluster numbers follow the Poisson distribution. The confidence interval and significance level are 95 % and 0.05, respectively. The test result, i.e., the significance probability p-value for mmWave and VLC, is 0 (<0.05) and 0.547 (>0.05), respectively. The results indicate that VLC cluster numbers follow the Poisson distribution, while the mmWave cluster numbers may not follow the Poisson distribution. We can infer that mmWave cluster numbers may follow the normal distribution from its histogram in Figure 13(a), and correspondingly we plot the normal curve to fit the cluster numbers. The fitting result shows that the histogram and normal  
Figure 13: Clustering results in the 10 th position. curve of mmWave cluster numbers can fit well together, which indicates that mmWave cluster numbers follow the normal distribution [48].