تقرير
Neural Decoding with Optimization of Node Activations
| العنوان: | Neural Decoding with Optimization of Node Activations |
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| المؤلفون: | Nachmani, Eliya, Be'ery, Yair |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Computer Science - Information Theory, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing |
| الوصف: | The problem of maximum likelihood decoding with a neural decoder for error-correcting code is considered. It is shown that the neural decoder can be improved with two novel loss terms on the node's activations. The first loss term imposes a sparse constraint on the node's activations. Whereas, the second loss term tried to mimic the node's activations from a teacher decoder which has better performance. The proposed method has the same run time complexity and model size as the neural Belief Propagation decoder, while improving the decoding performance by up to $1.1dB$ on BCH codes. Comment: IEEE Communications Letters |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2206.00786 |
| رقم الأكسشن: | edsarx.2206.00786 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |