# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 1 Introduction
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Techniques** & **Advantages** & **Disadvantages** \\ \hline Rain Gauge & High accuracy & Point measurement; low spatial resolution; high capital and operational cost; difficult to deploy in mountainous areas \\ \hline Weather Radar & Broad spatial coverage of up to 300 km & Low accuracy in near-surface measurement; easy to be affected by ground obstacles at a low elevation angle \\ \hline Satellite & Global scale & Coarse resolution for small spatial and temporal scales; affected by clouds; high time lag \\ \hline Commercial Microwave Links & Path-integrated and near-surface measurements; high spatial and temporal resolution; no additional capital cost & Hard to acquire CML data; relatively high complexity for data processing \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons of conventional and CML-based rainfall measurement techniques. In WCNs, in order to monitor the quality of service (QoS) of a CML, the network system always regularly monitors and records the transmission and received power of the signal in the CML for every, for example, 15 min. That is, a huge amount of CML attenuation data is already available and can be used to retrieve real-time rainfall intensity. However, there are many factors leading to the signal attenuation during the propagation, and it is nontrivial to extract the rain-induced attenuation from these attenuation data. This is because, first, microwave link attenuation data contain many spatio-temporal variations and uncertainties, which are not only related to path length, frequency, polarization, and other attributes of microwave links [15], but also affected by external factors such as temperature, humidity, atmospheric composition, and so on; second, rainfall is a highly spatio-temporal dependent event [16], and it is relatively complex and difficult to establish the model between rain-induced attenuation and rainfall intensity. Considering the problems mentioned above, data-driven methods such as machine learning have been proposed to facilitate CML-based rainfall measurement. Machine learning is a new way to solve interdisciplinary issues and the problems which are difficult to simulate their processes [17]. In particular, deep learning (DL) has been proven to be an important tool to exploit the power of big data (BD) by capturing high-dimensional and multi-modal data distribution, and extracting spatio-temporal features of data in order to understand the internal data logic and whole physical process [18]. Rainfall measured by the CML network has a broad application prospect in both densely populated cities and remote mountainous areas. Although current studies have made significant contributions on the technological aspects, there are very few review articles about this technique. One study [1] reviews the existing technologies and challenges of CML-based precipitation monitoring from a signal processing prospective, which includes calibration, detection, estimation, classification and separation, assimilation, and reconstruction. Another [19] divides the CML-based measurement studies into four groups and points out that the biggest challenge is the capitalization of CML data. Overviews of the history, theory, challenges, and opportunities toward CML-based rainfall monitoring technique are given by refs. [5; 10]. The former focuses on the background introduction and continental-scale rainfall monitoring, while the latter emphasizes the issues of wet antenna attenuation and abnormal signal fluctuations. Studies related to terrestrial microwave rain attenuation measurement from 2010 to 2020 are investigated by [20], but the detailed analyses of different methods and current challenges are insufficient. All of the above literature reviews summarize current research either from a certain prospective or focus on specific problems, but a comprehensive literature review and comparisons have not been carried out.