1 citations · 1 across the 5 of their papers we have counts for
7 papers
Combining Improvements in Uplink AI-RAN
Petteri Kela, Dani Korpi, Mikko Honkala
One of the major transformative factors in 6G will be the integration of Artificial Intelligence (AI) to become a native part of Radio Access Network (RAN). While most physical-lay…
Cross-Site Transfer and Spatial Forgetting in OTA-Trained Neural Receivers
Riku Luostari, Dani Korpi, Harri Holma +1
Most published neural-receiver studies train on synthetic channel models, although real over-the-air measurements can improve performance. We examine how much of this benefit trans…
Input-Correlated Supervision Noise Limits the Benefits of OTA Training for Learned Receivers
Riku Luostari, Dani Korpi, Olav Tirkkonen +1
While learned wireless receivers are typically studied using synthetic data, the impact of over-the-air (OTA) measurements for training remains unclear. We conducted a 5.88 GHz mea…
EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver
Mikko Honkala, Dani Korpi, Elias Raninen +1
While machine learning (ML)-based receiver algorithms have received a great deal of attention in the recent literature, they often suffer from poor scaling with increasing spatial…
Demonstrating Interoperable Channel State Feedback Compression with Machine Learning
Dani Korpi, Rachel Wang, Jerry Wang +20
Neural network-based compression and decompression of channel state feedback has been one of the most widely studied applications of machine learning (ML) in wireless networks. Var…
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…
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…