Asynchronous Linear Modulation Classification With Multiple Sensors via Generalized EM Algorithm

In this paper, we consider the problem of automatic modulation classification with multiple sensors in the presence of unknown time offset, phase offset and received signal amplitude. We develop a novel hybrid maximum likelihood (HML) classification scheme based on a generalized expectation maximiza...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on wireless communications Jg. 14; H. 11; S. 6389 - 6400
Hauptverfasser: Ozdemir, Onur, Wimalajeewa, Thakshila, Dulek, Berkan, Varshney, Pramod K., Wei Su
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.11.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Classification
data fusion
generalized expectation maximization algorithm
hybrid maximum likelihood
Maximization
Maximum likelihood estimation
Modulation
Modulation classification
multiple sensors
Nonuniform
Offsets
Receivers
Sensors
Signal to noise ratio
Synchronism
Synchronization
data fusion
multiple sensors
Modulation classification
hybrid maximum likelihood
generalized expectation maximization algorithm
ISSN:1536-1276, 1558-2248
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In this paper, we consider the problem of automatic modulation classification with multiple sensors in the presence of unknown time offset, phase offset and received signal amplitude. We develop a novel hybrid maximum likelihood (HML) classification scheme based on a generalized expectation maximization (GEM) algorithm. GEM is capable of finding ML estimates numerically that are extremely hard to obtain otherwise. Assuming a good initialization technique is available for GEM, we show that the classification performance (in terms of the probability of error) can be greatly improved with multiple sensors compared to that with a single sensor, especially when the signal-to-noise ratio (SNR) is low. We further demonstrate the superior performance of our approach when simulated annealing (SA) with uniform as well as nonuniform grids is employed for initialization of GEM in low SNR regions. The proposed GEM based approach employs only a small number of samples (in the order of hundreds) at a given sensor node to perform both time and phase synchronization, signal power estimation, followed by modulation classification. We provide simulation results to show the efficiency and effectiveness of the proposed algorithm.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2015.2453269