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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 40041 (05)
- University: Sharif University of Technology
- Department: Electrical Engineering
- Advisor(s): Behnia, Fereydoon
- Abstract:
- This thesis investigates various modulation recognition systems both in analogue and digital forms. We are looking for a recognition technique considering practical implementation issues. To recognize the modulation, the career frequency is supposed to be known and a bandpass signal is received. Besides, we investigate some digital Automatic Modulation Recognition (AMR) methods. The techniques based on the features of the signal have less computational complexity compared to Maximum Likelihood (ML) techniques; hence they are more desirable for practical implementation. In this project, analog modulations are implemented using C++ software. For the convenience of users, here, two graphical interfaces are prepared for both digital and analogue. Besides, we study the estimation of modulation rate when an undetermined signal is being transmitted through the channel with the unknown rate. As in contrast to analogue signal, the digital signal has circular stationary characteristic, we can have a blind estimate of the symbol rate. The problem of symbol rate estimation can be addressed from two points of view: i) using wavelet transform, ii) employing the circular stationary characteristic of the signal. We have investigated both schemes and proposed new ideas to improve their performance. Moreover, we have suggested a scheme for blind separation of digital and analogue signals. This scheme can be used as the preprocessing block in AMR systems which results in more accuracy and speed of the recognition systems
- Keywords:
- Decision Making Tree ; Maximum Likelihood Estimation ; Cyclic Correlation ; Automatic Modulation Recognition
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