The automatic modulation recognition (AMR) of a received signal 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, we have developed an AMR algorithm that is able to discriminate between analog communication signals and digital communication signals. 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, M, if the input is a frequency shift keying (FSK) signal. In addition, in M-ary FSK (MFSK) signal classification, two classifiers have also been developed. Each of the above-mentioned three AMR algorithms is capable of providing good estimate of the frequency deviation of a received FSK signal.

For further classification of digital communication signals that are not of FSK modulation, 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 received signal. For this purpose, a blind carrier frequency estimation algorithm and a blind symbol rate estimation algorithm for non-MFSK digital communication signals 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 complex envelopes of the filtered outputs, and the final estimate of symbol rate is made based on majority voting.

A thorough evaluation of some well-known feature based AMR algorithms is also presented in this dissertation.

Committee members:

Dr. Yun Q. Shi, Professor, ECE, NJIT, Dissertation Advisor

Dr. Ali Abdi, Assistant Professor, ECE, NJIT, Committee Member

Dr. Nirwan Ansari, Professor, ECE, NJIT, Committee Member

Dr. Roy R. You, RBS Greenwich Capital, New York, NY, Committee Member

Dr. Wei Su, GS-14, US Army RDECOM, Fort Monmouth, NJ, Committee Member