PROTOTIPE MODULATION CLASSIFIERBERBASIS MACHINE LEARNINGMENGGUNAKAN SOFTWARE DEFINED RADIO
In a modern communication system, a wireless receiver with Automatic Modulation Classification (AMC) ability has a very vast application potential. A combination of Machine Learning (ML) and AMC will increase the system performance significantly. The flexible Software Defined Radio (SDR) plays an...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/55329 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | In a modern communication system, a wireless receiver with Automatic Modulation
Classification (AMC) ability has a very vast application potential. A combination
of Machine Learning (ML) and AMC will increase the system performance
significantly. The flexible Software Defined Radio (SDR) plays an important role
in combining ML with AMC. ML traits which improve classification performance
through training processes will benefit greatly by the usage of SDR. In this project,
the three concepts are implemented for ADALM-PLUTO SDR. For the developed
prototype, modulation types are limited to BPSK, QPSK, 8-PSK, 16-QAM, and 8-
PAM. A successful classification requires a selection of features, therefore in this
project 6 spectral, high order statistics, and wavelet transform based features are
selected. This project also uses a QAM synchronization scheme to mitigate the
effects phase and frequency shifts to the feature extraction process. With the help
of Support Vector Machine (SVM), we created a classification model for SDR
based QAM Receiver with 91.4% accuracy. The Classification model is
implemented for the ADALM-PLUTO SDR.
|
|---|