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سی و سومین کنفرانس بین المللی مهندسی برق
Performance Evaluation of a Deep Neural Network Joint Equalizer-Decoder in AWGN-ISI Channels
نویسندگان :
Zahra Joleini
1
Ali Jamshidi
2
1- دانشگاه شیراز
2- دانشگاه شیراز
کلمات کلیدی :
Deep learning techniques،deep neural network decoder،equalizer،polar codes،channel estimation error
چکیده :
We propose a novel joint equalizer and decoder system based on deep neural networks for linear time invariant multipath channels. We use polar codes as the channel code and train two neural networks to perform blind joint equalization and decoding without requiring channel state information or encoder parameters. The first deep neural network recovers the signal from channel distortion, and the second deep neural network decodes the polar code. We demonstrate the superior performance of our system over both conventional and deep convolutional neural network methods, especially under large channel estimation errors.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 44.7.2
