University of Oulu

S. Majhi, R. Gupta, W. Xiang and S. Glisic, "Hierarchical Hypothesis and Feature-Based Blind Modulation Classification for Linearly Modulated Signals," in IEEE Transactions on Vehicular Technology, vol. 66, no. 12, pp. 11057-11069, Dec. 2017. doi: 10.1109/TVT.2017.2727858

Hierarchical hypothesis and feature-based blind modulation classification for linearly modulated signals

Saved in:
Author: Majhi, Sudhan1; Gupta, Rahul1; Xiang, Weidong2;
Organizations: 1Department of Electrical Engineering, Indian Institute of Technology Patna, Patna 801103, India
2Department of Electrical and Computer Engineering, University of Michigan, Dearborn, MA 48128 USA
3Centre for Wireless Communication, University of Oulu, Oulu 90550, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.6 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2018080233277
Language: English
Published: Institute of Electrical and Electronics Engineers, 2017
Publish Date: 2018-08-02
Description:

Abstract

This paper presents a hierarchical hypothesis test and a feature-based blind modulation classification (BMC) algorithm for linearly modulated signals. The proposed BMC method is based on the combination of elementary cumulant (EC) and cyclic cumulants. The EC is used to decide whether the constellations are from real, circular, or rectangular class, which is referred to as macro classifier. The cyclic cumulant is used to classify modulation within a subclass, which is referred to as micro classifier. For the micro classification, we use positions of nonzero cyclic frequencies (symbol rate frequency or carrier frequency) of the received signals. A hierarchical hypothesis-based theoretical framework has been developed to find the probability of error for the proposed classification. The method works over a flat fading channel without any knowledge of the signal parameters. The proposed method is more robust than the one based on EC and at the same time it requires lower complexity than the maximum likelihood approach. To validate the proposed scheme, measurement is carried out in realistic scenarios. The performance of the new algorithm is compared with the existing methods. In this paper, we have considered a six-class problem including binary phase-shift keying, quadrature phase-shift keying (QPSK), offset-QPSK, π/4-QPSK, minimum shift keying, and 16-quadrature amplitude modulation.

see all

Series: IEEE transactions on vehicular technology
ISSN: 0018-9545
ISSN-E: 1939-9359
ISSN-L: 0018-9545
Volume: 66
Issue: 12
Pages: 11057 - 11069
DOI: 10.1109/TVT.2017.2727858
OADOI: https://oadoi.org/10.1109/TVT.2017.2727858
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Copyright information: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.