4 papers
Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions
Marko Tuononen, Heikki Penttinen, Ville Hautamäki
We present the first use of influence functions for deep learning-based wireless receivers. Applied to DeepRx, a fully convolutional receiver, influence analysis reveals which trai…
Out-of-Distribution Detection via Channelwise Feature Aggregation in Neural Network-Based Receivers
Marko Tuononen, Heikki Penttinen, Duy Vu +3
Neural network-based radio receivers are expected to play a key role in future wireless systems, making reliable Out-Of-Distribution (OOD) detection essential. We propose a post-ho…
Improving Numerical Stability of Normalized Mutual Information Estimator on High Dimensions
Marko Tuononen, Ville Hautamäki
Mutual information provides a powerful, general-purpose metric for quantifying the amount of shared information between variables. Estimating normalized mutual information using a…
Interpreting Deep Neural Network-Based Receiver Under Varying Signal-To-Noise Ratios
Marko Tuononen, Dani Korpi, Ville Hautamäki
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 co…