# Earth-to-Earth Microwave Rain Attenuation Measurements: A Survey On the Recent Literature
## 4 Rainfall Measurements Through Backhaul Cellular Infrastructure
The results shew that the theoretically underestimated formulas systematically deviated the signal attenuator by several dB relevant to the measurements. Path-averaged rainfalls were estimated in a microwave network comprising eight links at frequencies 6-8 GHz and distances between 5.7 and 37.4 km [57]. The accuracy of the estimation of rain-induced attenuation on the links was found higher than 80%, and it was confirmed using concurrent measurements by rain detectors in weather stations. The power resolution of signals was 0.01 dBm and the temporal resolution was 1 min. It is shown that it is possible to detect rain using errors and attenuations recorded by microwave backhaul network, in a wet-dry classification fashion, employing machine learning techniques [58]. Measurements records from 34 commercial microwave links in two years were studied. Detection using both attenuation and error measurements outperforms slightly instead of using only attenuation measurements. The experimental setup included data from two active CMLs from the cellular operator Cellcom near the city of Arad in Israel [59]. The two CML shared the same path-link with a length of 16 km, an operating frequency of 18.6 GHz, and polarization being horizontal or vertical. The output log recorded the maximum received signal level (RSL) value for a 15-min time interval and \(\pm\)0.3 dB quantization. For the correlation of the data, two typical rain gauges measured rainfall with ten min integration time. In addition, data were also derived from the Israel Meteorological Sector's controlled radar, which was located roughly 85 km from the CML to one located in the town of Arad and the other in the Shani area. Six rainfall events from November 2015 to March 2016 were analyzed and compared with CML. Through these observations, it is shown that the bias affecting the min/max RSL, TSL interferes with CML based rain estimation methodologies. A further step in the prediction of rain was conducted by [PERSON] et al. with the use of convolutional neural networks [60]. As far as the quantitative estimation of the precipitation with CMLs, this could be, as aforementioned, supplemental to other already known techniques such as rain gauges. The opportunistic character of this method could be finely correlated to revolutionary data processing. This work is oriented towards the processing part of the rain detection system and i.e., the time series of the signal level at CMLs. A convolutional neural network (CNN) is employed with 20 layers of neurons (along with \(1.4\times 105\) trainable parameters) for this purpose in order to be trained for detecting and analyzing attenuation patterns (apart from only the amount of fluctuations) due to rainfall and the utilized data originated from 3904 CMLs in Germany. This CNN was a probabilistic classifier and was trained by a learning algorithm of [PERSON] et al. [61]. A more in-depth view reveals that this CNN was trained for about four months and by using 400 randomly selected CMLs. The produced results were tested against those of gauge radar RADOLAN-RW on an hourly basis. Furthermore, receiver operating characteristic (ROC) analysis reported that the CNN was far better than the reference with an average detection score of 87% for rainy and 91% for non-rainy conditions. Nevertheless, a serious difficulty was to report the noisy part correctly from the light rain's signal fluctuation. Other specifications of the system included path lengths from 0.1 to over 30 km and CMLs' frequencies from 10 to 40 GHz. Moreover, the TSL was stored with a 1-min rate and a power resolution of 1.0 dB while the power resolution for RSL was equal to 0.3 dB. Two research teams from Germany used data from 3904 CML operated by Ericsson [62]. CMLs were distributed throughout Germany, and sampling was done every minute. The data retrieval period was approximately one year from September 2017 to August 2018. The power resolution was 1 dB for the transmitted signal and regularly 0.3 dB for the received signal.