Distributed Automatic Modulation Classification With Multiple Sensors

Jefferson L. Xu

doi:10.1109/JSEN.2010.2049487

Distributed Automatic Modulation Classification With Multiple Sensors

2010-11-01

61

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AI

DOI：10.1109/JSEN.2010.2049487

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Automatic modulation classification (AMC) has been intensively studied to enhance the successful classification rate, particularly for overcoming the physical limit that deals with weak signals received in a noncooperative communication environment. A wireless sensor network (WSN) has multiple geometrically distributed sensors to work cooperatively. The distributed signal sensing and classification performed by collaborated sensors is proven to be beneficial to increasing the modulation classification reliability. In this paper, we apply the likelihood ratio-based distributed detection fusion technique to address the issues of general binary modulation classifications. The data fusion algorithm performed in the primary node is presented. Its numerical performance with simulation results is demonstrated.

Keyword:

Cognitive radio

distributed classification

distributed detection

likelihood ratio test (LRT)

modulation classification

wireless sensor networks (WSN)

Distributed Automatic Modulation Classification With Multiple Sensors

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