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Unsupervised machine intelligence for automation of multi-dimensional modulation

journal contribution
posted on 2019-10-01, 00:00 authored by Youngwook Ko, Jinho ChoiJinho Choi
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 non-coherent multi-dimensional transmission. Simulation results show that the proposed scheme can outperform the benchmarks at a cost of simple offline training.

History

Journal

IEEE Communications Letters

Volume

23

Issue

10

Pagination

1783 - 1786

Publisher

IEEE

Location

Piscataway, N.J.

ISSN

1089-7798

eISSN

1558-2558

Language

eng

Publication classification

C1 Refereed article in a scholarly journal