65 citations · 176 across the 12 of their papers we have counts for
15 papers
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…
Low-Complexity Inference in Continual Learning via Compressed Knowledge Transfer
Zhenrong Liu, Janne M. J. Huttunen, Mikko Honkala
Continual learning (CL) aims to train models that can learn a sequence of tasks without forgetting previously acquired knowledge. A core challenge in CL is balancing stability -- p…
Adapting to Reality: Over-the-Air Validation of AI-Based Receivers Trained with Simulated Channels
Riku Luostari, Dani Korpi, Mikko Honkala +1
Recent research shows that integrating artificial intelligence (AI) into wireless communication systems can significantly improve spectral efficiency. However, most AI-based receiv…
Deep Learning-Based Pilotless Spatial Multiplexing
Dani Korpi, Mikko Honkala, Janne M. J. Huttunen
This paper investigates the feasibility of machine learning (ML)-based pilotless spatial multiplexing in multiple-input and multiple-output (MIMO) communication systems. Especially…
DeepTx: Deep Learning Beamforming with Channel Prediction
Janne M. J. Huttunen, Dani Korpi, Mikko Honkala
Machine learning algorithms have recently been considered for many tasks in the field of wireless communications. Previously, we have proposed the use of a deep fully convolutional…
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…
Estimation of groundwater storage from seismic data using deep learning
Timo Lähivaara, Alireza Malehmir, Antti Pasanen +3
Convolutional neural networks can provide a potential framework to characterize groundwater storage from seismic data. Estimation of key components such as the amount of groundwate…
Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography
Timo Lähivaara, Leo Kärkkäinen, Janne M. J. Huttunen +1
We study the feasibility of data based machine learning applied to ultrasound tomography to estimate water-saturated porous material parameters. In this work, the data to train the…
Cauchy difference priors for edge-preserving Bayesian inversion with an application to X-ray tomography
Markku Markkanen, Lassi Roininen, Janne M J Huttunen +1
We study Cauchy-distributed difference priors for edge-preserving Bayesian statistical inverse problems. On the contrary to the well-known total variation priors, one-dimensional C…
Elliptic boundary value problems with Gaussian white noise loads
Sari Lasanen, Lassi Roininen, Janne M. J. Huttunen
Linear second order elliptic boundary value problems (BVP) on bounded Lipschitz domains are studied in the case of Gaussian white noise loads. Especially, Neumann and Robin BVPs ar…