## 5 Future Work
### Improvement of Processing Procedures  
Even though multiple procedures for processing CML data have been developed to date, they still have a lot of room for improvement. For dry and wet period classification, the WAA caused by antenna wetting by dew or fog, the WAA dynamics after rainfall and the drift of \begin{table}
\begin{tabular}{c c} \hline
**Future Works** & **Highlights** \\ \hline Exploration of Other Applications of & Explore the potential of CMLs to monitor plant, animal and human activities [182; 183; 159]. \\ \hline Rainfall Prediction & Research rainfall forecasts based on CML data [184; 185]. \\ \hline Application and Error Analysis of & Application and error analysis of rainfall inversion for high-frequency CMLs [47; 186]. \\ \hline Combination with Earth-space & Reconstruct the three-dimensional spatial rainfall field using CMLs in conjunction with ESLs [187]. \\ \hline Attempts to Estimate Rainfall using & Attempt to establish the rainfall attenuation model of low-frequency non-line-of-sight signals between cellphones and cell towers. \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._attenuation in the dry period induced by such factors as the occlusion of metallic objects in the path may produce false positives. In addition, due to the insensitivity of most existing CMLs to light rain, light rain events are often missed. However, limited by the availability of signal level data only and the similarity of the two types of phenomena, current classification algorithms are still unable to cope with these problems effectively. In the future, based on CML data with high temporal and quantization resolution, it may be possible to make a breakthrough by discovering the differences between the above phenomena. For baseline determination, the baseline should not be constant during wet periods because of changes in the equipment conditions of the CMLs and in the environment along their paths. Existing methods more or less ignore this. With the rise of machine learning, time-series prediction algorithms based on recurrent neural networks, such as LSTM, may enable the estimation of wet period baselines [177]. For WAA correction, since WAA is highly dependent on antenna properties, the findings on WAA may vary from one research group to another. However, state-of-the-art WAA modeling is still limited to a small number of CMLs, which are usually from the same operators, and the mechanism of action between WAA and antenna properties remains unsystematically investigated. Although these models have been validated on large datasets, the errors may be masked in large-scale comparisons. In the future, investigating the effect of different antenna characteristic parameters on the WAA model by controlling variables may be a solution to achieve a more robust WAA quantization. For rainfall inversion using the \(\gamma\)-\(R\) relationship, the power-law approximation introduces inherent errors due to the nonlinearity of the curve and the dependence on DSD. One possible solution is to utilize deep learning to substitute the \(\gamma\)-\(R\) relationship [188; 189; 190; 191]. [PERSON] et al. [178] have compared data-driven Recurrent Neural Network (RNN) algorithms with traditional power-law relations. The results show that the former has better performance but poorer robustness. Adding a temporal normalization layer can improve the robustness of the RNN algorithm, but at the same time compromise performance. In the future, perhaps an attempt can be made to combine deep learning with power-law relations to ensure excellent performance and robustness at the same time. For rainfall mapping, most studies still use CML-based rainfall estimates as the rain rate at the midpoint of the path. This not only fails to take into account the spatial variability of rainfall on the path, but also ignores the advantage of CMLs in providing linear-integrated measurements. In addition, most rainfall mapping techniques directly follow the rainfall mapping methods of dedicated rain sensor networks.