Efficient Design of Machine Learning Models for Radio Frequency (RF) Signal Modulation Recognition
Author(s)
Woo, Jongseok
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Abstract
The object of the research is to design an efficient Deep Neural Network (DNN)
accelerator and denoising algorithm for the application of modulation recognition of the
received Radio Frequency (RF) signals.
In order to reduce computational demand of the
hardware and increase the operation frequency, a low complexity DNN model employing
ternary weight quantization is demonstrated with co-analysis of the classification
accuracy and the hardware design. To maximize the benefits of the ternary weight
quantization, I propose a new hardware design dedicated to the ternary weight type. Also
the new training method to compensate the limited training capability of the ternary
weight quantization is proposed to improve the classification accuracy of the ternary
weight based network. Considering the fact that the RF transmission channel is exposed
to various type/level of the noise, I propose the DNN-based denoising mechanisms
optimized for suppressing the noise of the RF signal, which are the reconstructing of the
signal with untrained DNN and the noise-adaptive autoencoder. The proposed methods
improve the classification accuracy of the low Signal to Noise Ratio (SNR) signal
efficiently with minimal impact on the hardware design of the entire DNN.
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Date
2024-07-02
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Text
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Dissertation