Distributed asynchronous modulation classification based on hybrid maximum likelihood approach

Thakshila Wimalajeewa; Jithin Jagannath; Pramod Kumar Varshney; Andrew Drozd; Wei Su

doi:10.1109/MILCOM.2015.7357660

Distributed asynchronous modulation classification based on hybrid maximum likelihood approach

Thakshila Wimalajeewa, Jithin Jagannath, Pramod Kumar Varshney, Andrew Drozd, Wei Su

Department of Electrical Engineering & Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

18 Scopus citations

Abstract

In this paper, we consider the problem of automatic modulation classification (AMC) with multiple sensors. A distributed hybrid maximum likelihood (HML) based algorithm in the presence of unknown time offset, phase offset and channel gain is presented. The proposed distributed algorithm that employs the generalized expectation maximization (GEM) algorithm is robust to initialization of unknown parameters, computationally efficient and require much less communication overhead compared to performing GEM in a centralized setting. Simulation and experimental results depict the efficacy of the proposed algorithm.

Original language	English (US)
Title of host publication	Proceedings - IEEE Military Communications Conference MILCOM
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1519-1523
Number of pages	5
Volume	2015-December
ISBN (Print)	9781509000739
DOIs	https://doi.org/10.1109/MILCOM.2015.7357660
State	Published - Dec 14 2015
Event	34th Annual IEEE Military Communications Conference, MILCOM 2015 - Tampa, United States Duration: Oct 26 2015 → Oct 28 2015

Other

Other	34th Annual IEEE Military Communications Conference, MILCOM 2015
Country/Territory	United States
City	Tampa
Period	10/26/15 → 10/28/15

Keywords

distributed decision fusion
generalized expectation maximization algorithm
hybrid maximum likelihood
Modulation classification

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/MILCOM.2015.7357660

Cite this

Wimalajeewa, T., Jagannath, J., Varshney, P. K., Drozd, A., & Su, W. (2015). Distributed asynchronous modulation classification based on hybrid maximum likelihood approach. In Proceedings - IEEE Military Communications Conference MILCOM (Vol. 2015-December, pp. 1519-1523). [7357660] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MILCOM.2015.7357660

Distributed asynchronous modulation classification based on hybrid maximum likelihood approach. / Wimalajeewa, Thakshila; Jagannath, Jithin; Varshney, Pramod Kumar et al.

Proceedings - IEEE Military Communications Conference MILCOM. Vol. 2015-December Institute of Electrical and Electronics Engineers Inc., 2015. p. 1519-1523 7357660.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wimalajeewa, T, Jagannath, J, Varshney, PK, Drozd, A & Su, W 2015, Distributed asynchronous modulation classification based on hybrid maximum likelihood approach. in Proceedings - IEEE Military Communications Conference MILCOM. vol. 2015-December, 7357660, Institute of Electrical and Electronics Engineers Inc., pp. 1519-1523, 34th Annual IEEE Military Communications Conference, MILCOM 2015, Tampa, United States, 10/26/15. https://doi.org/10.1109/MILCOM.2015.7357660

@inproceedings{feb2ea15c9424542ba62cfd5287f3bbf,

title = "Distributed asynchronous modulation classification based on hybrid maximum likelihood approach",

abstract = "In this paper, we consider the problem of automatic modulation classification (AMC) with multiple sensors. A distributed hybrid maximum likelihood (HML) based algorithm in the presence of unknown time offset, phase offset and channel gain is presented. The proposed distributed algorithm that employs the generalized expectation maximization (GEM) algorithm is robust to initialization of unknown parameters, computationally efficient and require much less communication overhead compared to performing GEM in a centralized setting. Simulation and experimental results depict the efficacy of the proposed algorithm.",

keywords = "distributed decision fusion, generalized expectation maximization algorithm, hybrid maximum likelihood, Modulation classification",

author = "Thakshila Wimalajeewa and Jithin Jagannath and Varshney, {Pramod Kumar} and Andrew Drozd and Wei Su",

year = "2015",

month = dec,

day = "14",

doi = "10.1109/MILCOM.2015.7357660",

language = "English (US)",

isbn = "9781509000739",

volume = "2015-December",

pages = "1519--1523",

booktitle = "Proceedings - IEEE Military Communications Conference MILCOM",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "34th Annual IEEE Military Communications Conference, MILCOM 2015 ; Conference date: 26-10-2015 Through 28-10-2015",

}

TY - GEN

T1 - Distributed asynchronous modulation classification based on hybrid maximum likelihood approach

AU - Wimalajeewa, Thakshila

AU - Jagannath, Jithin

AU - Varshney, Pramod Kumar

AU - Drozd, Andrew

AU - Su, Wei

PY - 2015/12/14

Y1 - 2015/12/14

N2 - In this paper, we consider the problem of automatic modulation classification (AMC) with multiple sensors. A distributed hybrid maximum likelihood (HML) based algorithm in the presence of unknown time offset, phase offset and channel gain is presented. The proposed distributed algorithm that employs the generalized expectation maximization (GEM) algorithm is robust to initialization of unknown parameters, computationally efficient and require much less communication overhead compared to performing GEM in a centralized setting. Simulation and experimental results depict the efficacy of the proposed algorithm.

AB - In this paper, we consider the problem of automatic modulation classification (AMC) with multiple sensors. A distributed hybrid maximum likelihood (HML) based algorithm in the presence of unknown time offset, phase offset and channel gain is presented. The proposed distributed algorithm that employs the generalized expectation maximization (GEM) algorithm is robust to initialization of unknown parameters, computationally efficient and require much less communication overhead compared to performing GEM in a centralized setting. Simulation and experimental results depict the efficacy of the proposed algorithm.

KW - distributed decision fusion

KW - generalized expectation maximization algorithm

KW - hybrid maximum likelihood

KW - Modulation classification

UR - http://www.scopus.com/inward/record.url?scp=84959265678&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84959265678&partnerID=8YFLogxK

U2 - 10.1109/MILCOM.2015.7357660

DO - 10.1109/MILCOM.2015.7357660

M3 - Conference contribution

AN - SCOPUS:84959265678

SN - 9781509000739

VL - 2015-December

SP - 1519

EP - 1523

BT - Proceedings - IEEE Military Communications Conference MILCOM

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 34th Annual IEEE Military Communications Conference, MILCOM 2015

Y2 - 26 October 2015 through 28 October 2015

ER -

Distributed asynchronous modulation classification based on hybrid maximum likelihood approach

Abstract

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this