论文标题
极地代码的神经网络串联
Neural network concatenation for Polar Codes
论文作者
论文摘要
当使用神经网络（NN）解码极性代码时，其训练复杂性呈指数级缩放，因为代码块大小（或确切的，作为消息位的数量）增加。因此，使用神经网络用于极性解码器的现有解决方案被固定在16或32之类的短块大小上。尽管对于长极性代码而言，NN训练非常复杂，但NN解码可提供更好的延迟，其性能可能接近最大可能性（ML）。在本文中，我们描述了一种有效的算法，以创建具有与连续取消（SC）相等或更好的任何大小的极性代码的NN解码。因此，它创造了一个机会，可以在训练时间允许的情况下设计基于NN的解码。
When a neural network (NN) is used to decode a polar code, its training complexity scales exponentially as the code block size (or to be precise, as a number of message bits) increases. Therefore, existing solutions that use a neural network for polar decoders are stuck with short block sizes like 16 or 32. Despite the fact that the NN training is very complex for long polar codes, the NN decoding gives the better latency and its performance is potentially close to the maximum likelihood (ML). In this paper, we describe an efficient algorithm to create the NN decoding for a polar code of any size with the initial performance that is equal or better than that of successive cancelation (SC). Therefore, it creates an opportunity to design the NN based decoding with the performance that is as close to the ML, as the training time allows.
