582 citations · 623 across the 9 of their papers we have counts for
11 papers
Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers
Sajad Rezaie, Mikko Honkala, Dani Korpi +4
Fifth-generation (5G) systems utilize orthogonal demodulation reference signals (DMRS) to enable channel estimation at the receiver. These orthogonal DMRS-also referred to as pilot…
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…
Video Ladder Networks
Francesco Cricri, Xingyang Ni, Mikko Honkala +2
We present the Video Ladder Network (VLN) for efficiently generating future video frames. VLN is a neural encoder-decoder model augmented at all layers by both recurrent and feedfo…
Semi-Supervised Learning with Ladder Networks
Antti Rasmus, Harri Valpola, Mikko Honkala +2
We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised c…
Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series
Mathias Berglund, Tapani Raiko, Mikko Honkala +3
Bidirectional recurrent neural networks (RNN) are trained to predict both in the positive and negative time directions simultaneously. They have not been used commonly in unsupervi…