Interpreting Deep Neural Network-Based Receiver Under Varying Signal-To-Noise Ratios

2024/09/25 by Marko Tuononen, Tuononen, Marko, Dani Korpi +3 · 1 citation
Computer Science · #68T07 #Blind Source Separation Techniques #C.2.1 #FOS: Computer and information sciences #FOS: Electrical engineering #I.2.6 #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Neural Networks and Applications #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2409.16768

openalex publication_date 2024/09/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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• Refining Neural Activation Patterns for Layer-Level Concept Discovery in Neural Network-Based Receivers

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