FilipeLopesPires/64-QAM-Classification
Optical Communications: 64-QAM classification with neural networks.
This project helps optical communications engineers analyze how well 64-QAM signals are being received by attempting to classify individual symbols within a signal stream. It takes in raw received optical signal data and outputs a prediction for each symbol's intended class, helping to identify and understand interference. Optical communications engineers working with Quadrature Amplitude Modulation would find this useful.
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Use this if you are an optical communications engineer researching how neural networks can classify 64-QAM symbols and identify interference in received signals.
Not ideal if you require an extremely high accuracy classifier (above 99%) for live optical communication systems, as this model did not meet that threshold.
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Language
MATLAB
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Last pushed
Apr 15, 2021
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