Ko, Youngwook and Choi, Jinho (2019) Unsupervised Machine Intelligence for Automation of Multi-Dimensional Modulation. IEEE Communications Letters. ISSN 1089-7798
Abstract
In this letter, we propose a new unsupervised machine learning technique for a multi-dimensional modulator that can autonomously learn key exploitable features from significant variations of multi-dimensional wireless propagation parameters, followed by a real-time prediction of the best multi-dimensional modulation mode to be used for the next resilient transmission. The proposed method aims to embrace the potential of the unsupervised K-means clustering into the physical layer of noncoherent multi-dimensional transmission. Simulation results show that the proposed scheme can outperform the benchmarks at a cost of simple offline training.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) The University of York |
Depositing User: | Pure (York) |
Date Deposited: | 08 Oct 2019 09:40 |
Last Modified: | 16 Oct 2024 16:06 |
Published Version: | https://doi.org/10.1109/LCOMM.2019.2932417 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1109/LCOMM.2019.2932417 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151905 |