The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Automatic modulation classification of communication signals
Author:	Yu, Zaihe
View Online:	njit-etd2006-115 (xv, 231 pages ~ 10.4 MB pdf)
Department:	Department of Electrical and Computer Engineering
Degree:	Doctor of Philosophy
Program:	Electrical Engineering
Document Type:	Dissertation
Advisory Committee:	Shi, Yun Q. (Committee chair) Abdi, Ali (Committee member) Ansari, Nirwan (Committee member) You, Roy R. (Committee member) Su, Wei (Committee member)
Date:	2006-08
Keywords:	Automatic modulation classification Carrier frequency estimation Analog communications signals Symbol rate estimation Feature based classification Cyclostationarity
Availability:	Unrestricted
Abstract:	The automatic modulation recognition (AMR) plays an important role in various civilian and military applications. Most of the existing AMR algorithms assume that the input signal is only of analog modulation or is only of digital modulation. In blind environments, however, it is impossible to know in advance if the received communication signal is analogue modulated or digitally modulated. Furthermore, it is noted that the applications of the currently existing AMR algorithms designed for handling both analog and digital communication signals are rather restricted in practice. Motivated by this, an AMR algorithm that is able to discriminate between analog communication signals and digital communication signals is developed in this dissertation. The proposed algorithm is able to recognize the concrete modulation type if the input is an analog communication signal and to estimate the number of modulation levels and the frequency deviation if the input is an exponentially modulated digital communication signal. For linearly modulated digital communication signals, the proposed classifier will classify them into one of several nonoverlapping sets of modulation types. In addition, in M-ary FSK (MFSK) signal classification, two classifiers have also been developed. These two classifiers are also capable of providing good estimate of the frequency deviation of a received MFSK signal. For further classification of linearly modulated digital communication signals, it is often necessary to blindly equalize the received signal before performing modulation recognition. This doing generally requires knowing the carrier frequency and symbol rate of the input signal. For this purpose, a blind carrier frequency estimation algorithm and a blind symbol rate estimation algorithm have been developed. The carrier frequency estimator is based on the phases of the autocorrelation functions of the received signal. Unlike the cyclic correlation based estimators, it does not require the transmitted symbols being non-circularly distributed. The symbol rate estimator is based on digital communication signals' cyclostationarity related to the symbol rate. In order to adapt to the unknown symbol rate as well as the unknown excess bandwidth, the received signal is first filtered by using a bank of filters. Symbol rate candidates and their associated confident measurements are extracted from the fourth order cyclic moments of the filtered outputs, and the final estimate of symbol rate is made based on weighted majority voting. A thorough evaluation of some well-known feature based AMR algorithms is also presented in this dissertation.