Performance Evaluation of PAC Decoding with Deep Neural Networks
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913513306324992 |
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| author | Dai, Jingxin Yin, Hang Lv, Yansong Wang, Yuhuan Lv, Rui |
| author_facet | Dai, Jingxin Yin, Hang Lv, Yansong Wang, Yuhuan Lv, Rui |
| contents | By concatenating a polar transform with a convolutional transform, polarization-adjusted convolutional (PAC) codes can reach the dispersion approximation bound in certain rate cases. However, the sequential decoding nature of traditional PAC decoding algorithms results in high decoding latency. Due to the parallel computing capability, deep neural network (DNN) decoders have emerged as a promising solution. In this paper, we propose three types of DNN decoders for PAC codes: multi-layer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN). The performance of these DNN decoders is evaluated through extensive simulation. Numerical results show that the MLP decoder has the best error-correction performance under a similar model parameter number. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_02590 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Performance Evaluation of PAC Decoding with Deep Neural Networks Dai, Jingxin Yin, Hang Lv, Yansong Wang, Yuhuan Lv, Rui Information Theory By concatenating a polar transform with a convolutional transform, polarization-adjusted convolutional (PAC) codes can reach the dispersion approximation bound in certain rate cases. However, the sequential decoding nature of traditional PAC decoding algorithms results in high decoding latency. Due to the parallel computing capability, deep neural network (DNN) decoders have emerged as a promising solution. In this paper, we propose three types of DNN decoders for PAC codes: multi-layer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN). The performance of these DNN decoders is evaluated through extensive simulation. Numerical results show that the MLP decoder has the best error-correction performance under a similar model parameter number. |
| title | Performance Evaluation of PAC Decoding with Deep Neural Networks |
| topic | Information Theory |
| url | https://arxiv.org/abs/2405.02590 |