# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 9 Conclusions  
As a new approach of rainfall measurement, The CMLs in wireless cellular networks have a broad application prospect. In densely populated cities, CMLs can be used as an effective supplement to traditional rainfall measurement methods. Moreover, in remote mountainous areas, where RGs and radars are not easy to deploy and maintain, CMLs have much greater potential in some scenarios, such as real-time rainfall monitoring, short-term rainfall forecasting, flash flood warning, and so on. During the more than ten years of development of CML-based rainfall measurement technology, great progresses have been made in the accuracy of wet/dry classification, the real-time dynamics of baseline determination, the authenticity of WAA simulation, the diversity of rainfall intensity retrieval and mapping algorithms, and so on. More and more researchers around the world have devoted themselves to this research.  
Although CML-based rainfall measurement has been greatly improved in terms of both monitoring range and mapping accuracy, there are still some challenges that limit the practical application of this technology. The biggest problem is the data acquisition. Because the operation processes of cellular networks for communications and sensing are independent from each other, it is relatively difficult to obtain the dedicated communication data and adapt them to suitable environmental sensing data. From the view of technological aspects, various error sources have a certain impact on the accuracy of rainfall measurement, especially the change in WAA. Therefore, dedicated microwave link experimental equipment should be built to further analyze the weights of various errors in the total errors, and thus to provide help for acquiring more accurate rainfall information in actual CML network environments.  
In recent years, machine learning has been widely used in CML-based rainfall measurement to solve problems mostly related to big data, such as classification, regression, simulation, prediction, and so on. In particular, deep learning provides a new way to simulate the complicated hydrological cycle processes. In the future, we should strengthen the combination of data-driven machine learning and physical mechanism models in order to explain and understand the data logic. It is also necessary to enhance the cooperation among different rainfall measurement methods to establish a more comprehensive rainfall measurement network. At the same time, applying CML-based rainfall measurement data to hydrological cycles should be promoted, and the potential of CMLs as an opportunistic wireless sensor network in environmental sensing should be fully explored. This paper has summarized the state-of-the-art of CML-based rainfall measurement technology and discussed the future directions, and we hope it can inspire more innovative ideas and deeper thinking.  
Conceptualization, B.L.; methodology, B.L.; validation, Z.W.; formal analysis, J.Z.; writing--original draft preparation, B.L.; writing--review and editing, X.S.; visualization, B.L. and Z.W.; supervision, Z.L. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Research Project of Higher Education Institutions of Hebei Province under Grant QN2020193 and ZD2020171, the Graduate Demonstration Course Construction Project of Hebei Province under Grant KCJSX2022090 and KCJSX2022091, the Handan Science and Technology Research and Development Program under Grant 21422031288, and the Provincial Innovation Funding Project for Graduate Students of Hebei Province under Grant CXZZBS2022024.  
Not applicable.  
The authors are grateful to the researchers who have provided open access datasets on CML-based rainfall measurement, and the anonymous reviewers are greatly appreciated for their comments and suggestions.