Distributed classification under statistical dependence with application to automatic modulation classification

Hao He; Sora Choi; Pramod Kumar Varshney; Wei Su

Distributed classification under statistical dependence with application to automatic modulation classification

Hao He, Sora Choi, Pramod Kumar Varshney, Wei Su

Department of Electrical Engineering & Computer Science

Research output: Chapter in Book/Entry/Poem › Conference contribution

1 Scopus citations

Abstract

In this paper, we consider the distributed classification of discrete random signals in wireless sensor networks (WSNs). Observing the same random signal makes sensors' observations conditionally dependent which complicates the design of distributed classification systems. In the literature, this dependence has been ignored for simplicity although this may significantly affect the performance of the classification system. We derive the necessary conditions for the optimal decision rules at the sensors and the fusion center (FC) by introducing a 'hidden' random variable. Furthermore, we introduce an iterative algorithm to search for the optimal decision rules. The proposed scheme is applied to a distributed Automatic Modulation Classification (AMC) problem. It is shown to attain superior performance in comparison with other approaches which disregard the inter-sensor dependence.

Original language	English (US)
Title of host publication	2015 18th International Conference on Information Fusion, Fusion 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1597-1602
Number of pages	6
ISBN (Print)	9780982443866
State	Published - Sep 14 2015
Event	18th International Conference on Information Fusion, Fusion 2015 - Washington, United States Duration: Jul 6 2015 → Jul 9 2015

Other

Other	18th International Conference on Information Fusion, Fusion 2015
Country/Territory	United States
City	Washington
Period	7/6/15 → 7/9/15

Keywords

automatic modulation classification
dependent observations
distributed classification
wireless sensor networks

ASJC Scopus subject areas

Information Systems
Signal Processing
Computer Networks and Communications

Cite this

Distributed classification under statistical dependence with application to automatic modulation classification. / He, Hao; Choi, Sora; Varshney, Pramod Kumar et al.
2015 18th International Conference on Information Fusion, Fusion 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1597-1602 7266747.

Research output: Chapter in Book/Entry/Poem › Conference contribution

He, H, Choi, S, Varshney, PK & Su, W 2015, Distributed classification under statistical dependence with application to automatic modulation classification. in 2015 18th International Conference on Information Fusion, Fusion 2015., 7266747, Institute of Electrical and Electronics Engineers Inc., pp. 1597-1602, 18th International Conference on Information Fusion, Fusion 2015, Washington, United States, 7/6/15.

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N2 - In this paper, we consider the distributed classification of discrete random signals in wireless sensor networks (WSNs). Observing the same random signal makes sensors' observations conditionally dependent which complicates the design of distributed classification systems. In the literature, this dependence has been ignored for simplicity although this may significantly affect the performance of the classification system. We derive the necessary conditions for the optimal decision rules at the sensors and the fusion center (FC) by introducing a 'hidden' random variable. Furthermore, we introduce an iterative algorithm to search for the optimal decision rules. The proposed scheme is applied to a distributed Automatic Modulation Classification (AMC) problem. It is shown to attain superior performance in comparison with other approaches which disregard the inter-sensor dependence.

AB - In this paper, we consider the distributed classification of discrete random signals in wireless sensor networks (WSNs). Observing the same random signal makes sensors' observations conditionally dependent which complicates the design of distributed classification systems. In the literature, this dependence has been ignored for simplicity although this may significantly affect the performance of the classification system. We derive the necessary conditions for the optimal decision rules at the sensors and the fusion center (FC) by introducing a 'hidden' random variable. Furthermore, we introduce an iterative algorithm to search for the optimal decision rules. The proposed scheme is applied to a distributed Automatic Modulation Classification (AMC) problem. It is shown to attain superior performance in comparison with other approaches which disregard the inter-sensor dependence.

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Distributed classification under statistical dependence with application to automatic modulation classification

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Keywords

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