Deep-learning-based forward-error-correction decoding techniques and optimizations for hardware implementation
In recent years, Deep-Learning has been adopted by a wide spectrum of applications, as it is a powerful problem-solving methodology which can be applied in extremely diverse fields. Various types of Artificial Neural Networks can be trained to perform a task with high accuracy. The effectiveness of...
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| Format: | Thesis |
| Language: | English |
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2020
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| Online Access: | http://hdl.handle.net/10889/13787 |