IEEE Antennas and Wireless Propagation Letters · 2020 · 19 citations · 13 references
EngineeringElectrical EngineeringAntennasMicrowave EngineeringWireless CommunicationsMicrowave AntennasMicrowave MeasurementsMicrowave PropagationPrecipitation EstimationTime Series ForecastingAttenuation Estimation AlgorithmsLong Short-term Memory NetworksMicrowave Attenuation ModelsMillimeter-wave PropagationRain AttenuationRain Attenuation Modeling
Commercial 1.164 km long microwave link (CML) data with frequencies of 72.75 and 82.75 GHz (E-band) deployed in the Prague urban area were used to estimate the wet antenna attenuation (WAA). By analyzing the time series of WAA, a WAA model of E-band CML is proposed based on the long short-term memory neural network. The results show that the predicted value is consistent with the real value, especially when the attenuation fluctuation is not significant, but it understates cumulative rainfall when rain sharply increases. In addition, the performance of the model applied to the 72.75 GHz link is better than that applied to the 82.75 GHz link.
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