Detection and Classification of OFDM Waveforms Using Cepstral Analysis
Abstract—Detection and Classification of OFDM Waveforms Using Cepstral Analysis .Also presence of cyclic prefix in the orthogonal frequency division multiplexing induces periodicities. Thus the distributions of coefficients are derived for two schemes. As of all, significantly different from the additive white Gaussian noise (AWGN) and can be used to detect waveforms. Therefore OFDM is rich in features and to estimate the number of subcarriers and length of CP in a symbol. Secondly these parameters are used to automatically identify or classify different waveforms, which are important for the cognitive radios, coexistence of heterogeneous networks and signal intelligence. Since then it has been in various applications such as radar, sonar, marine and earth seismology, speech processing, image processing and deconvolution of the probability density functions. Probably two methods are suggested based on the primary user (PU). While the test information under two theory is created. Consequently Neyman–Pearson technique is used. Almost the algorithms for calculating the number of subcarriers and the length of the CP are introduced. Seems like their performances are studied through simulations. Later the schemes are extended to cooperative sensing events with the multiple secondary users < Final Year Projects 2016 >. As a result, it is shown that the combination between them importantly, improve the detection and analysis. After then it has been used in various applications such as radar, sonar, marine and also especially the earth seismology, image processing and of the probability density functions. May be the ability of the cepstrum to find the periodicities in the signal and to separate a glottal action , a vocal tract transfer is proven. Which resulted in its general use in the processing such as the voice pitch, vocoders.Furthermore it is mostly used in a very high communication applications. As a result the response of a selective channel is judged.
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