Abstract:
This is the final report of the project "Blind Modulation Classification for Non-Cooperative
Demodulation", conducted by the students of the Department of Computer System Engineering at
College Of Electrical & Mechanical Engineering (NUST).
The objective of the project was to design and implement Automatic Modulation
Recognition system with least dependency on the preprocessing. A feature-based method is used,
introducing new intuitive features for Real-Time Classification of Digitally modulated signals
without any prior knowledge of signal parameters. The incoming signal's basic modulation type is
detected i.e. FSK, PSK, ASK & QAM and then its order is identified. This hierarchical
classification can be considered a step towards a general modulation classifier in AWGN channel.
Linear Approximations are introduced in Instantaneous Amplitude and Non-Linear Component of
Instantaneous Phase which results in improved performance of the system at lower SNR values.
Simulations show that with the new feature set classification success rate is 99.9% at very low
SNR i.e. 5dB.
Estimation algorithms are introduced for signal parameters like Symbol Rate and Carrier
Frequency, a new wavelet transform based approach was devised to deal with all basic modulation
schemes for the purpose of Symbol Rate Estimation ,the algorithm showed promising result even
at low SNR values i.e OdB.
Carrier Frequency Estimation was carried out using Daniell filtered smoothed periodogram
approach, which is one of the latest techniques in frequency spectrum analysis.
The system was designed and Implemented in two phases, in the first phase,system was
designed using MATLAB and a GUI was created, in the second phase C language code was
developed for Pentium machine platform.
