Robustness in Supervised Learning Based Blind Automatic Modulation Classification

Md. Abdur RAHMAN; Azril HANIZ; Minseok KIM; Jun-ichi TAKADA

doi:10.1587/transcom.E96.B.1030

Robustness in Supervised Learning Based Blind Automatic Modulation Classification

Md. Abdur RAHMAN, Azril HANIZ, Minseok KIM, Jun-ichi TAKADA

Full Text Views

0

Share
Cite this

Summary :

Automatic modulation classification (AMC) involves extracting a set of unique features from the received signal. Accuracy and uniqueness of the features along with the appropriate classification algorithm determine the overall performance of AMC systems. Accuracy of any modulation feature is usually limited by the blindness of the signal information such as carrier frequency, symbol rate etc. Most papers do not sufficiently consider these impairments and so do not directly target practical applications. The AMC system proposed herein is trained with probable input signals, and the appropriate decision tree should be chosen to achieve robust classification. Six unique features are used to classify eight analog and digital modulation schemes which are widely used by low frequency mobile emergency radios around the globe. The Proposed algorithm improves the classification performance of AMC especially for the low SNR regime.

Publication: IEICE TRANSACTIONS on Communications Vol.E96-B No.4 pp.1030-1038

Publication Date: 2013/04/01

Publicized

Online ISSN: 1745-1345

DOI: 10.1587/transcom.E96.B.1030

Type of Manuscript: PAPER

Category: Wireless Communication Technologies

Cite this

Copy

Md. Abdur RAHMAN, Azril HANIZ, Minseok KIM, Jun-ichi TAKADA, "Robustness in Supervised Learning Based Blind Automatic Modulation Classification" in IEICE TRANSACTIONS on Communications, vol. E96-B, no. 4, pp. 1030-1038, April 2013, doi: 10.1587/transcom.E96.B.1030.
Abstract: Automatic modulation classification (AMC) involves extracting a set of unique features from the received signal. Accuracy and uniqueness of the features along with the appropriate classification algorithm determine the overall performance of AMC systems. Accuracy of any modulation feature is usually limited by the blindness of the signal information such as carrier frequency, symbol rate etc. Most papers do not sufficiently consider these impairments and so do not directly target practical applications. The AMC system proposed herein is trained with probable input signals, and the appropriate decision tree should be chosen to achieve robust classification. Six unique features are used to classify eight analog and digital modulation schemes which are widely used by low frequency mobile emergency radios around the globe. The Proposed algorithm improves the classification performance of AMC especially for the low SNR regime.
URL: https://globals.ieice.org/en_transactions/communications/10.1587/transcom.E96.B.1030/_p

Copy

@ARTICLE{e96-b_4_1030,
author={Md. Abdur RAHMAN, Azril HANIZ, Minseok KIM, Jun-ichi TAKADA, },
journal={IEICE TRANSACTIONS on Communications},
title={Robustness in Supervised Learning Based Blind Automatic Modulation Classification},
year={2013},
volume={E96-B},
number={4},
pages={1030-1038},
abstract={Automatic modulation classification (AMC) involves extracting a set of unique features from the received signal. Accuracy and uniqueness of the features along with the appropriate classification algorithm determine the overall performance of AMC systems. Accuracy of any modulation feature is usually limited by the blindness of the signal information such as carrier frequency, symbol rate etc. Most papers do not sufficiently consider these impairments and so do not directly target practical applications. The AMC system proposed herein is trained with probable input signals, and the appropriate decision tree should be chosen to achieve robust classification. Six unique features are used to classify eight analog and digital modulation schemes which are widely used by low frequency mobile emergency radios around the globe. The Proposed algorithm improves the classification performance of AMC especially for the low SNR regime.},
keywords={},
doi={10.1587/transcom.E96.B.1030},
ISSN={1745-1345},
month={April},}

Copy

TY - JOUR
TI - Robustness in Supervised Learning Based Blind Automatic Modulation Classification
T2 - IEICE TRANSACTIONS on Communications
SP - 1030
EP - 1038
AU - Md. Abdur RAHMAN
AU - Azril HANIZ
AU - Minseok KIM
AU - Jun-ichi TAKADA
PY - 2013
DO - 10.1587/transcom.E96.B.1030
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E96-B
IS - 4
JA - IEICE TRANSACTIONS on Communications
Y1 - April 2013
AB - Automatic modulation classification (AMC) involves extracting a set of unique features from the received signal. Accuracy and uniqueness of the features along with the appropriate classification algorithm determine the overall performance of AMC systems. Accuracy of any modulation feature is usually limited by the blindness of the signal information such as carrier frequency, symbol rate etc. Most papers do not sufficiently consider these impairments and so do not directly target practical applications. The AMC system proposed herein is trained with probable input signals, and the appropriate decision tree should be chosen to achieve robust classification. Six unique features are used to classify eight analog and digital modulation schemes which are widely used by low frequency mobile emergency radios around the globe. The Proposed algorithm improves the classification performance of AMC especially for the low SNR regime.
ER -