Modulation classification for orthogonal frequency-division multiplexing (OFDM) signals
This report proposed a comprehensive modulation classification system for recognizing the Orthogonal Frequency Division Mult iplexing (OFDM) signal and extracting its parameters. Since, OFDM is asymptotically Gaussian, a Gaussianity test is introduced first to distinguish multi-carrier signa...
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Format: | Final Year Project |
Language: | English |
Published: |
2010
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Subjects: | |
Online Access: | http://hdl.handle.net/10356/40198 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | This report proposed a comprehensive modulation classification system for
recognizing the Orthogonal Frequency Division Mult iplexing (OFDM) signal and
extracting its parameters. Since, OFDM is asymptotically Gaussian, a Gaussianity
test is introduced first to distinguish multi-carrier signal (OFDM) from single carrier
signal and Additive White Gaussian Noise (AWGN) by analysing the signal
distribut ion. Modulation classifiers, including Maximum-Likelihood approach and
the Sixth Order Cumulants method, are then presented to ident ify the modulat ion
type for each sub-carrier of OFDM signal. The parameter extraction methods are also
addressed: cyclic-correlation based sampling frequency estimation, correlation test to
estimate the Cyclic Prefix duration, iterative approach to detect the frequency offset,
as well as the Gaussianity test for detecting subcarriers number.
The main contributions of report are to illustrate and to implement the three methods
for estimating the symbol rate which are the Classical method, the Weighted
approach, and the Filter-based cyclic correlation method. Simulations are provided to
compare and evaluate these three methods. It is shown in simulation results that the
Filter-based method is the most effective and accurate one in sampling frequency
estimation for signals of different modulation types, such as QAM, 4QAM, 16QAM
and 64QAM.
All the simulations are carried out using Matlab. |
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