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eess.SP2026
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…
eess.SP2026
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…
eess.SP2025
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…