2014/09/30 by Onur Özdemir, Ozdemir, O., Thakshila Wimalajeewa +7
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced biosensing and bioanalysis techniques #Blind Source Separation Techniques #FOS: Computer and information sciences #Other Computer Science (cs.OH) #Wireless Signal Modulation Classification
paper · pdf · doi:10.48550/arxiv.1409.8370
openalex publication_date 2014/09/30 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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 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 computational efficiency and effectiveness of the proposed algorithm.