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Multi-Label and Concatenated Neural Block Decoders

机译:多标签和级联神经块解码器

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There has been a growing interest in designing neural-network based decoders (or neural decoders in short) for communication systems. In the prior work, we cast the problem of decoding an (n, k) block code as a single-label classification problem, and it is shown that the performance of such single-label neural decoders closely approaches that of the corresponding maximum likelihood soft-decision (ML-SD) decoders. The main issue is that the number of output nodes of single-label neural decoders increases exponentially with k, making it prohibitive to decode a code with medium or large dimension. To address this issue, we first explore a multi-label classification based neural decoder for block codes, in which the number of output nodes increases linearly with k. The complexity of the multi-label neural decoder is lower, but the performance is still close to that of the ML-SD decoder. We also consider concatenating a high-rate short-length outer code with the original code as the inner code. The proposed concatenated decoding architecture consists of a multi-label neural decoder for the inner code and a single label neural decoder for the outer code. The results demonstrate that the concatenated decoding approach leads to better bit and block error performance as compared to a benchmark soft-decision decoder. We note that the overall size of the concatenated neural decoder is close to that of the single-label neural decoder.
机译:对于设计基于神经网络的解码器(或简称神经解码器,对于通信系统,已经越来越感兴趣。在事先工作中,我们将解码(n,k)块代码解码为单个标签分类问题的问题,并且显示这种单标的神经解码器的性能密切接近相应的最大似然软件-decision(ml-sd)解码器。主要问题是单个标签神经解码器的输出节点的数量随k呈指数呈指数增长,使得禁止使用中等或大维度解码代码。为了解决这个问题,我们首先探索基于多标签分类的基于块代码的神经解码器,其中输出节点的数量与k线性增加。多标签神经解码器的复杂性较低,但性能仍然接近ML-SD解码器的性能。我们还考虑将具有原始代码的高速短度外部代码连接为内部代码。所提出的连接解码架构包括用于内部代码的多标题神经解码器和用于外部代码的单个标签神经解码器。结果表明,与基准软判决解码器相比,串联解码方法导致更好的位和块误差性能。我们注意到,连接的神经解码器的总体大小接近单标的神经解码器的整体大小。

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