Unsupervised Machine Intelligence for Automation of Multi-Dimensional Modulation

Youngwook Ko; Jinho Choi

doi:10.1109/LCOMM.2019.2932417

Unsupervised Machine Intelligence for Automation of Multi-Dimensional Modulation

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Journal	IEEE Communications Letters
DOIs	https://doi.org/10.1109/LCOMM.2019.2932417
Publication status	Published - 1 Aug 2019

Access to Document

10.1109/LCOMM.2019.2932417

FINAL VERSIONAccepted author manuscript, 278 KB

Cite this

@article{9dd0f404430848f9a74ee67289ed8935,

title = "Unsupervised Machine Intelligence for Automation of Multi-Dimensional Modulation",

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.",

author = "Youngwook Ko and Jinho Choi",

year = "2019",

month = aug,

day = "1",

doi = "10.1109/LCOMM.2019.2932417",

language = "English",

journal = "IEEE Communications Letters",

issn = "1089-7798",

publisher = "IEEE",

}

TY - JOUR

T1 - Unsupervised Machine Intelligence for Automation of Multi-Dimensional Modulation

AU - Ko, Youngwook

AU - Choi, Jinho

PY - 2019/8/1

Y1 - 2019/8/1

N2 - 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.

AB - 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.

U2 - 10.1109/LCOMM.2019.2932417

DO - 10.1109/LCOMM.2019.2932417

M3 - Article

SN - 1089-7798

JO - IEEE Communications Letters

JF - IEEE Communications Letters

ER -