2025
Interpreting Deep Neural Network-Based Receiver Under Varying Signal-To-Noise Ratios
ICASSP 2025accepted
We propose a novel method for interpreting neural networks, focusing on convolutional neural network-based receiver model. The method identifies which unit or units of the model contain most (or least) information about the channel parameter(s) of the interest, providing insights at both global and…