247 citations · 253 across the 3 of their papers we have counts for
6 papers · 1 filter
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…
Gradient-Adaptive Spline-Interpolated LUT Methods for Low-Complexity Digital Predistortion
Pablo Pascual Campo, Alberto Brihuega, Lauri Anttila +4
In this paper, new digital predistortion (DPD) solutions for power amplifier (PA) linearization are proposed, with particular emphasis on reduced processing complexity in future 5G…
Digital Cancellation of Passive Intermodulation in FDD Transceivers
Muhammad Zeeshan Waheed, Pablo Pascual Campo, Dani Korpi +3
Modern radio systems and transceivers utilize carrier aggregation (CA) to meet the demands for higher and higher data rates. However, the adoption of CA in the existing Long Term E…