Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Marko Tuononen, Dani Korpi, Ville Hautamäki
Institutions: Nokia (Finland), University of Eastern Finland
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4408345591