Presentation 2015-03-06
Sub-Optimum Maximum Likelihood Modulation Classification Algorithm Using Multiple Antennas
Kenta HOMMA, Tetsuya SHIMAMURA,
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Abstract(in English) Blind modulation classification (MC) plays an important role in many wireless communication systems such as cognitive radio. MC is a process to determine the unknown modulation schemes of the received signal from a set of modulation candidates. Maximum likelihood (ML) algorithm is typically used in blind MC. This algorithm, however, suffers from high computational complexity. Recently, MC algorithms with multiple antennas have been addressed. Most of the algorithms with multiple antennas are devoted to only classification accuracy for blind MC. We consider the computational complexity for the MLMC algorithm and propose a novel efficient MC algorithm with multiple antennas. Through computer simulations, we show that the performance of the proposed algorithm is competitive to that of the MLMC algorithm with single antenna under certain conditions.
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Keyword(in English) modulation classification / modulation / maximum likelihood
Paper # SIS2014-104
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Committee SIS
Conference Date 2015/2/26(1days)
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Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Sub-Optimum Maximum Likelihood Modulation Classification Algorithm Using Multiple Antennas
Sub Title (in English)
Keyword(1) modulation classification
Keyword(2) modulation
Keyword(3) maximum likelihood
1st Author's Name Kenta HOMMA
1st Author's Affiliation Graduate School of Science Engineering, Saitama University()
2nd Author's Name Tetsuya SHIMAMURA
2nd Author's Affiliation Graduate School of Science Engineering, Saitama University
Date 2015-03-06
Paper # SIS2014-104
Volume (vol) vol.114
Number (no) 496
Page pp.pp.-
#Pages 6
Date of Issue