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arXiv 1702.06901cs.ITmath.IT
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基于缩放深度学习的极化码分块解码

Scaling Deep Learning-based Decoding of Polar Codes via Partitioning

Sebastian Cammerer, Tobias Gruber, Jakob Hoydis, Stephan ten Brink

摘要:

基于深度学习的信道解码器的训练复杂度与码本大小成指数级,因此与信息比特的数量成指数级。因此,神经网络解码(NND)目前仅适用于非常短的块长度。在这项工作中,我们表明,传统的迭代解码算法的极化码时,解码器的子块被替换为神经网络(NN)为基础的组件可以得到增强。因此,我们将编码图划分成更小的子块,并分别对它们进行训练,接近每个子块的最大后验概率(MAP)性能。这些块然后经由剩余的常规置信传播解码级连接。所得到的解码算法是非迭代的,并且固有地实现了高水平的并行化,同时显示出有竞争力的比特出错率(BER)性能。我们通过分区检查退化,并将所得解码器与最先进的极化解码器(如连续取消列表和置信传播解码)进行比较。

英文摘要:

The training complexity of deep learning-based channel decoders scales exponentially with the codebook size and therefore with the number of information bits. Thus, neural network decoding (NND) is currently only feasible for very short block lengths. In this work, we show that the conventional iterative decoding algorithm for polar codes can be enhanced when sub-blocks of the decoder are replaced by neural network (NN) based components. Thus, we partition the encoding graph into smaller sub-blocks and train them individually, closely approaching maximum a posteriori (MAP) performance per sub-block. These blocks are then connected via the remaining conventional belief propagation decoding stage(s). The resulting decoding algorithm is non-iterative and inherently enables a high-level of parallelization, while showing a competitive bit error rate (BER) performance. We examine the degradation through partitioning and compare the resulting decoder to state-of-the-art polar decoders such as successive cancellation list and belief propagation decoding.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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