6 citations · 6 across the 5 of their papers we have counts for
7 papers · 1 filter
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…
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…
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,…