The use of recurrent neural networks ( $RNN\text{s}$ ) to utilize measurements from commercial microwave links ( $CML\text{s}$ ) has recently gained attention. Whereas previous studies focused on the performance… Click to show full abstract
The use of recurrent neural networks ($RNN\text{s}$ ) to utilize measurements from commercial microwave links ($CML\text{s}$ ) has recently gained attention. Whereas previous studies focused on the performance of methods for wet–dry classification, here we propose an RNN algorithm for estimating the rain-rate. We empirically analyzed the proposed algorithm, using real data, and compared it with the traditional power-law (PL)-based algorithm, commonly used for estimating rain from CML attenuation measurements. Our analysis shows that the data-driven RNN algorithm, when properly trained, outperforms the PL algorithm in terms of accuracy. On the other hand, the PL algorithm is simpler and more robust when dealing with a large variety of corruptions and adverse conditions. We then introduced a time normalization (TN) layer for controlling the trade-off between performance and robustness of the RNN methods, and demonstrated its performance.
