6 citations · 6 across the 2 of their papers we have counts for
9 papers
Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier
Dani Korpi, Mikko Honkala, Janne M. J. Huttunen +2
Machine learning (ML) has shown great promise in optimizing various aspects of the physical layer processing in wireless communication systems. In this paper, we use ML to learn jo…
HybridDeepRx: Deep Learning Receiver for High-EVM Signals
Jaakko Pihlajasalo, Dani Korpi, Mikko Honkala +6
In this paper, we propose a machine learning (ML) based physical layer receiver solution for demodulating OFDM signals that are subject to a high level of nonlinear distortion. Spe…
DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations
Dani Korpi, Mikko Honkala, Janne M. J. Huttunen +1
Recently, deep learning has been proposed as a potential technique for improving the physical layer performance of radio receivers. Despite the large amount of encouraging results,…
DeepRx: Fully Convolutional Deep Learning Receiver
Mikko Honkala, Dani Korpi, Janne M. J. Huttunen
Deep learning has solved many problems that are out of reach of heuristic algorithms. It has also been successfully applied in wireless communications, even though the current radi…
A Bayesian-based approach to improving acoustic Born waveform inversion of seismic data for viscoelastic media
Kenneth Muhumuza, Lassi Roininen, Janne M. J. Huttunen +1
In seismic waveform inversion, the reconstruction of the subsurface properties is usually carried out using approximative wave propagation models to ensure computational efficiency…
Improving pulse transit time estimation of aortic PWV and blood pressure using machine learning and simulated training data
Janne M. J. Huttunen, Leo Kärkkäinen, Harri Lindholm
Recent developments in cardiovascular modelling allow us to simulate blood flow in an entire human body. Such model can also be used to create databases of virtual subjects, with s…