In this paper, we propose to use the fourth order cumulants to distinguish OFDM from single carrier signals. We show analytically the value of C40(N) and C42(N) as a function of the number of subcarriers of the OFDM signal and its SNR. By taking these values as an estimate of these cumulants, we compare it to the estimate of difTerent single carrier modulation signals including SC-BC as it was given in the literature [1] or by experiments we performed as for others. From these values we create thresholds detectors use either one cumulant or both depending on the assumed environments which include timing ofTset, phase rotation, frequency ofTset of the received signals, and pulse shaping. Last, we use Monte Carlo simulations under different scenarios to examine the probability of detecting OFDM from different single carrier signals.
Electroencephalography (EEG) is a complex bioelectrical signal. Analysis of which can provide researchers with useful physiological information. In order to recognize and classify EEG signals, a pattern recognition method for optimizing the support vector machine (SVM) by using improved squirrel search algorithm (ISSA) is proposed. The EEG signal is preprocessed, with its time domain features being extracted and directed to the SVM as feature vectors for classification and identification. In this paper, the method of good point set is used to initialize the population position, chaos and reverse learning mechanism are introduced into the algorithm. The performance test of the improved squirrel algorithm (ISSA) is carried out by using the benchmark function. As can be seen from the statistical analysis of the results, the exploration ability and convergence speed of the algorithm are improved. This is then used to optimize SVM parameters. ISSA-SVM model is established and built for classification of EEG signals, compared with other common SVM parameter optimization models. For data sets, the average classification accuracy of this method is 85.9%. This result is an improvement of 2–5% over the comparison method.
Cyclostationarity is a significant feature of numerous physical and man-made processes. Specially in wireless communications, it has been explored and applied for many processing, like timing and frequency synchronization, channel estimation, and modulation classification. In this paper, we notice that the second order time varying auto-correlation of the received communication signal contains a cyclostationary real and a non-cyclostationary complex part. We propose a simple method to combine the positive and negative part of the cyclo-spectrum (the Fourier transform of the time varying autocorrelation) to enhance the performance of detecting the second order cyclostationarity of these signals. The idea redis related to the concept of cyclic Wiener filter put forth by Gardner in 1993 [4]. Monte Carlo simulations are given to verify the results.Index Terms-2nd order Cyclostationarity, communication signal.
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