Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Dani Korpi"'
Autor:
Mattia Merluzzi, Tamas Borsos, Nandana Rajatheva, Andras A. Benczur, Hamed Farhadi, Taha Yassine, Markus Dominik Mueck, Sokratis Barmpounakis, Emilio Calvanese Strinati, Dilin Dampahalage, Panagiotis Demestichas, Pietro Ducange, Miltiadis C. Filippou, Leonardo Gomes Baltar, Johan Haraldson, Leyli Karacay, Dani Korpi, Vasiliki Lamprousi, Francesco Marcelloni, Jafar Mohammadi, Nuwanthika Rajapaksha, Alessandro Renda, Mikko A. Uusitalo
Publikováno v:
IEEE Access, Vol 11, Pp 65620-65648 (2023)
This paper provides an overview of the most recent advancements and outcomes of the European 6G flagship project Hexa-X, on the topic of in-network Artificial Intelligence (AI) and Machine Learning (ML). We first present a general introduction to the
Externí odkaz:
https://doaj.org/article/57d5497cd7194d2786f17b6fbbf2d66b
Autor:
Saeed R. Khosravirad, Olav Tirkkonen, Ulo Parts, Liang Zhou, Dani Korpi, Paolo Baracca, Mikko A. Uusitalo
Publikováno v:
IEEE Network. 36:66-72
Publisher Copyright: © 1986-2012 IEEE. Industrial wireless control systems are mainly designed on the premise of time-sensitive ultra-reliable low-latency communications (URLLC). With the introduction of survival time to the quality of service requi
Autor:
Jaakko Pihlajasalo, Dani Korpi, Taneli Riihonen, Jukka Talvitie, Mikko A. Uusitalo, Mikko Valkama
Publikováno v:
2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC).
With wireless networks evolving towards mmWave and sub-THz frequency bands, hardware impairments such as IQ imbalance, phase noise (PN) and power amplifier (PA) nonlinear distortion are increasingly critical implementation challenges. In this paper,
Publikováno v:
2022 IEEE Wireless Communications and Networking Conference (WCNC).
Autor:
Karthik Upadhya, Kimmo Valkealahti, Martti Moisio, Dani Korpi, Tero Ihalainen, Mikko A. Uusitalo
Publikováno v:
2022 IEEE Wireless Communications and Networking Conference (WCNC).
Autor:
Taneli Riihonen, Dani Korpi, Mikko Honkala, Jaakko Pihlajasalo, Mikko Uusitalo, Alberto Brihuega, Mikko Valkama, Janne Huttunen, Jukka Talvitie
Publikováno v:
IEEE Transactions on Wireless Communications. :1-1
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 neural network (CNN) for receiver processing and shown it to provide
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2467a0812e2f0cb61671901e2ea82ea5
http://arxiv.org/abs/2202.07998
http://arxiv.org/abs/2202.07998
Autor:
Liang Zhou, Olav Tirkkonen, Ulo Parts, Saeed R. Khosravirad, Paolo Baracca, Dani Korpi, Mikko Uusitalo
Funding Information: ACKNOWLEDGMENT This work was supported in part by the Finnish public funding agency for research, Business Finland under the project “5G VIIMA”, grant number 6430/31/2018. 5G VIIMA is part of 5G Test Network Finland (5GTNF).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::12ace0270d3a431ce0cf7de0b8afd1f9
https://aaltodoc.aalto.fi/handle/123456789/118812
https://aaltodoc.aalto.fi/handle/123456789/118812
Autor:
Jaakko Pihlajasalo, Dani Korpi, Mikko Honkala, Janne M. J. Huttunen, Taneli Riihonen, Jukka Talvitie, Mikko A. Uusitalo, Mikko Valkama
Publikováno v:
2021 55th Asilomar Conference on Signals, Systems, and Computers.
In this paper, we propose a machine learning (ML) aided physical layer receiver technique for demodulating OFDM signals that are subject to very high Doppler effects and the corresponding distortion in the received signal. Specifically, we develop a
Autor:
Jaakko Pihlajasalo, Jukka Talvitie, Taneli Riihonen, Mikko A. Uusitalo, Alberto Brihuega, Mikko Valkama, Mikko Honkala, Janne M. J. Huttunen, Dani Korpi
Publikováno v:
PIMRC
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. Specifically, a novel deep learning based convolutional neural network re