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A Self Training Approach to Automatic Modulation Classification Based on Semi-supervised Online Passive Aggressive Algorithm

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Abstract

Automatic classification of modulation type in detected signals is an intermediate step between channel equalization and signal demodulation. This subsystem is an essential task for an intelligent receiver in various civil and military applications. Most of automatic modulation classification algorithms have been evaluated in SNR aware case which is obviously an unrealistic case. In this paper, we propose a semi-supervised online passive-aggressive classifier that uses a self-training approach for additive white Gaussian noise channels with unknown or variable signal to noise ratios to classify the modulated signals. An appropriate set of selected feature helps the general system work for a set of initial samples of each class. In the adaptation phase, unlabeled high confidence samples are used to adapt to system. An appropriate confidence measure is proposed to collect confident samples. Simulation results shows that adding unlabeled input samples to the training set improve the generalization capacity of the presented classifier in the target SNR. In addition to online characteristics of the algorithm that make the approach suitable for cognitive radio systems, this algorithm converges with a few number of signal samples. Simulation results in SNR unaware conditions show that employing this learning method results in high classification rate close to SNR aware conditions.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Hamidreza Hosseinzadeh, Farbod Razzazi & Afrooz Haghbin

Authors
  1. Hamidreza Hosseinzadeh
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  2. Farbod Razzazi
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  3. Afrooz Haghbin
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Correspondence to Farbod Razzazi.

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Hosseinzadeh, H., Razzazi, F. & Haghbin, A. A Self Training Approach to Automatic Modulation Classification Based on Semi-supervised Online Passive Aggressive Algorithm. Wireless Pers Commun 82, 1303–1319 (2015). https://doi.org/10.1007/s11277-015-2284-7

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  • DOI: https://doi.org/10.1007/s11277-015-2284-7

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