Asynchronous Linear Modulation Classification With Multiple Sensors via Generalized EM Algorithm

Onur Özdemir

doi:10.1109/TWC.2015.2453269

Asynchronous Linear Modulation Classification With Multiple Sensors via Generalized EM Algorithm

2015-11-01

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Onur Özdemir *

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Thakshila Wimalajeewa

DOI：10.1109/TWC.2015.2453269

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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.

Keyword:

Modulation classification

hybrid maximum likelihood

generalized expectation maximization algorithm

data fusion

multiple sensors

Asynchronous Linear Modulation Classification With Multiple Sensors via Generalized EM Algorithm

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