Doubly Residual Neural Decoder: Towards Low-Complexity High-Performance Channel Decoding
Doubly Residual Neural Decoder: Towards Low-Complexity High-Performance Channel Decoding
复制标题
双残差神经解码器：迈向低复杂度高性能通道解码
DOI：
10.1609/aaai.v35i10.17040
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Bo Yuan
中科院分区：
文献类型：
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作者：
Siyu Liao;Chunhua Deng;Miao Yin;Bo Yuan
最近，深度神经网络已成功应用于信道编码，以提高解码性能。
然而，现有的神经信道解码器不能同时实现高解码性能和低复杂度。为了克服这一挑战，在本文中，我们提出了双残差神经（DRN）解码器。通过将残差输入和残差学习集成到神经通道解码器的设计中，DRN能够在保持低复杂度的同时显着提高解码性能。大量的实验结果表明，在不同类型的信道编码，我们的DRN解码器始终优于国家的最先进的解码器在解码性能，模型大小和计算成本。
Recently deep neural networks have been successfully applied in channel coding to improve the decoding performance.
However, the state-of-the-art neural channel decoders cannot achieve high decoding performance and low complexity simultaneously. To overcome this challenge, in this paper we propose doubly residual neural (DRN) decoder. By integrating both the residual input and residual learning to the design of neural channel decoder, DRN enables significant decoding performance improvement while maintaining low complexity. Extensive experiment results show that on different types of channel codes, our DRN decoder consistently outperform the state-of-the-art decoders in terms of decoding performance, model sizes and computational cost.
DOI：
10.1109/jsait.2020.2986752
发表时间：
2018-07
期刊：
IEEE Journal on Selected Areas in Information Theory
影响因子：
--
作者：
Hyeji Kim;Yihan Jiang;Sreeram Kannan;Sewoong Oh;P. Viswanath
通讯作者：
Hyeji Kim;Yihan Jiang;Sreeram Kannan;Sewoong Oh;P. Viswanath
DOI：
10.1109/jsait.2020.2991562
发表时间：
2020-05
期刊：
IEEE Journal on Selected Areas in Information Theory
影响因子：
--
作者：
Hyeji Kim;Sewoong Oh;P. Viswanath
通讯作者：
Hyeji Kim;Sewoong Oh;P. Viswanath