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Neural Network Aided SC Decoder for Polar Codes

机译：神经网络辅助极性码的SC解码器

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Theoretically, conventional decoders for polar codes can be entirely replaced by neural network (NN) with enough size and enough training, which called NN decoder. But the exponentially increasing training complexity becomes unacceptable when information length increases, which means only decoders for short codes can be trained practically. However, a successive cancellation (SC) decoder for long polar codes can be divided into several SC decoders for short codes, which can be replaced by several short codes NN decoders, then the whole decoder becomes our NN aided SC (NNSC) decoder. Besides, we defined Universal Set of NN, which can be combined into NNSC decoders for any long polar codes. In this paper, the main purpose of constructing NNSC decoder is increasing decoding efficiency of polar codes by taking advantage of NN, and in the meantime ensuring an acceptable performance compared to conventional decoding algorithms.
机译：从理论上讲，用于极性代码的常规解码器可以完全由具有足够大小和足够训练的神经网络（NN）代替，这被称为NN解码器。但是当信息长度增加时，成倍增加的训练复杂度变得不可接受，这意味着只有短码解码器才能实际训练。但是，长极性码的连续消除（SC）解码器可以分为几个短码的SC解码器，可以用几个短码NN解码器代替，然后整个解码器成为我们的NN辅助SC（NNSC）解码器。此外，我们定义了NN通用集，可以将其组合为任何长极性码的NNSC解码器。在本文中，构造NNSC解码器的主要目的是通过利用NN提高极性码的解码效率，同时确保与常规解码算法相比性能可接受。

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