## 4 Conclusions and Future Work  
This paper focuses on the propagation characteristics comparisons between mmWave and VLC bands in indoor scenarios based on the measurement campaign and ray tracing simulation. The results are analyzed based on the conference room. We find that the OPL of VLC channels is highly dependent on the physical size of PDs and an OPL model considering the size of PDs is further proposed. The size of PDs can be estimated to meet the required coverage range while designing systems by using this proposed model. In addition, the PLE is 1.73 for VLC channels, which is larger than that for mmWave channels (1.31). This result reveals that the VLC channels suffer faster decay than mmWave channels with increasing distance in this room. Moreover, mmWave signals can travel farther with a larger RMS DS (13.18 ns) than that of VLC channels (7.41 ns), and the smaller RMS DS in VLC bands shows the weaker delay dispersion compared to mmWave channels. Moreover, the RMS DS of mmWave increases with distance, while it is little dependent on the distance for VLC channels. Furthermore, the mean K-factor for mmWave is 2.88 dB with \(-1.03\) dB for VLC, and LOS components mainly account for more power (80% of K-factor \(>0\) dB) for mmWave in LOS scenarios, while NLOS components can be stronger (74% of K-factor \(<0\) dB) at a large distance for VLC channels. The results suggest that it is better to select mmWave technologies when large coverage is required, i.e., VLC technologies are expected for short-range communication. On the other hand, RMS DS and K-factor appear to present a similar trend, varying with distance, i.e., RMS DS is proportional to K-factor for both mmWave and VLC channels. However, this requires further verification. Furthermore, the K-Power-Means algorithm is used to perform clustering from the dimensions of delay and angle. The fitting cluster number is 5 and 6 for mmWave and VLC, respectively. The clustering results reveal the temporal sparsity in mmWave bands and show that VLC channels have a large angular spread.  
The propagation characteristics for VLC channels are investigated based on ray tracing methods. Although this channel modeling approach has been demonstrated by experimental results, there are differences between simulation and experimental measurements considering the fact that the reflection coefficients of walls and objects inside the conference room could not be measured and representative values were assumed in the simulations. A channel sounding platform for VLC channels is still needed, which will be our further focus.  
Figure 14: Fitting results of cluster number.  
**Author Contributions:** Conceptualization, B.L.; methodology, B.L.; software, B.L. and P.T.; validation, B.L., P.T. and Y.Y.; investigation, B.L. and P.T.; resources, B.L. and P.T.; data curation, B.L. and P.T.; writing--original draft preparation, B.L.; writing--review and editing, P.T. and J.Z.; visualization, B.L.; supervision, J.Z., G.L. and L.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the National Science Fund for Distinguished Young Scholars (No. 61925102), the National Key R&D Program of China (No. 2020 YFB1805002), the National Natural Science Foundation of China (No. 62031019 and No. 62101069), and BUFT-CMCC Joint Innovation Center (CMYJY-202000276).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.