An Online and Distributed Approach for Modulation Classification Using Wireless Sensor Networks

Based on an online and distributed implementation of the expectation-maximization (EM) algorithm, a hybrid likelihood-based modulation classifier is proposed for a sensor network subject to unknown nonidentical flat fading. The proposed algorithm compares favorably in terms of computational complexi...

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Vydané v:IEEE sensors journal Ročník 17; číslo 6; s. 1781 - 1787
Hlavný autor: Dulek, Berkan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 15.03.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Classifiers
distributed
expectation-maximization
fading
Maximum likelihood estimation
Modulation
Modulation classification
online
Parameter estimation
Power efficiency
Receivers
sensor network
Sensors
Signal processing algorithms
Transmitters
Wireless sensor networks
ISSN:1530-437X, 1558-1748
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Shrnutí:Based on an online and distributed implementation of the expectation-maximization (EM) algorithm, a hybrid likelihood-based modulation classifier is proposed for a sensor network subject to unknown nonidentical flat fading. The proposed algorithm compares favorably in terms of computational complexity with respect to other maximum likelihood classifiers that rely on the batch (offline) EM algorithm for parameter estimation. Upon the reception of a new sample, each sensor computes the a posteriori probability of the corresponding symbol based on the average consensus algorithm, which relies on local communications among nearby sensors and promotes scalability and power efficiency. Then, each sensor updates its statistics with the innovation obtained from the samples received during the last symbol interval and new estimates for local sensor parameters are computed. Simulation results demonstrate that the proposed online and distributed EM-based classifier can achieve performance close that of a clairvoyant classifier equipped with perfect channel state information.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2017.2655543