Interpreting Deep Neural Network-Based Receiver Under Varying Signal-To-Noise Ratios
This provides insights for researchers and engineers in wireless communications by offering a robust interpretation method, though it is incremental as it builds on existing neural network interpretation techniques.
The paper tackles the problem of interpreting neural networks by proposing a method to identify units in a convolutional neural network-based receiver model that contain the most or least information about channel parameters, specifically signal-to-noise ratio processing, with experiments on link-level simulations demonstrating its effectiveness.
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 local levels -- with global explanations aggregating local ones. Experiments on link-level simulations demonstrate the method's effectiveness in identifying units that contribute most (and least) to signal-to-noise ratio processing. Although we focus on a radio receiver model, the method generalizes to other neural network architectures and applications, offering robust estimation even in high-dimensional settings.