Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Jaakko Pihlajasalo"'
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,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4a24c0c46a48b6403d8c7d40cf619913
https://doi.org/10.1109/spawc51304.2022.9834021
https://doi.org/10.1109/spawc51304.2022.9834021
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3f96f24f00776516b1df5df9f15f32bc
https://doi.org/10.1109/twc.2023.3235059
https://doi.org/10.1109/twc.2023.3235059
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c8ecd6ffc4136dd9bce3eb75fc7ddb83
https://doi.org/10.1109/ieeeconf53345.2021.9723316
https://doi.org/10.1109/ieeeconf53345.2021.9723316
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1ed36c14eaae2215ccb83349c30a0d30
https://doi.org/10.1109/pimrc50174.2021.9569393
https://doi.org/10.1109/pimrc50174.2021.9569393
Publikováno v:
VTC-Fall
The 5th generation mobile networks introduce large bandwidths with extended beamforming capabilities, which results in increased spatial selectivity of received channel state information. A channel chart is a map of the radio geometry that surrounds
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4ba7dc495e6ed0547821478bfe29659e
https://trepo.tuni.fi/handle/10024/132986
https://trepo.tuni.fi/handle/10024/132986
Publikováno v:
ICL-GNSS
Clock offset predictions along with satellite orbit predictions are used in self-assisted GNSS to reduce the Time-to-First-Fix of a satellite positioning device. This paper compares three methods for predicting GNSS satellite clock offsets: polynomia
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7300aff6b257b87c6b94882985f94ef3
https://doi.org/10.1109/icl-gnss.2019.8752725
https://doi.org/10.1109/icl-gnss.2019.8752725
Publikováno v:
2018 European Navigation Conference (ENC).
This paper presents a method for improving the accuracy of extended GNSS satellite orbit predictions with convolutional neural networks (CNN). Satellite orbit predictions are used in self-assisted GNSS to reduce the Time to First Fix of a satellite p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::590ae4f7f6b75b4ebc2564fafd177945
https://doi.org/10.1109/euronav.2018.8433244
https://doi.org/10.1109/euronav.2018.8433244
Publikováno v:
Tampere University
Channel charts are a map of the radio geometry that surrounds the base station and they are generated in an unsupervised manner from the received channel state information. In this work, we generate channel charts for two different antenna types for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::1214770d742b3693df8f8a2e127cfa34
https://researchportal.tuni.fi/en/publications/06a353c4-809b-48f2-8469-f5764c89f5ab
https://researchportal.tuni.fi/en/publications/06a353c4-809b-48f2-8469-f5764c89f5ab