基于神经网络的极化解码器和联合优化盲解码器的Syndrome使能无监督学习
Syndrome-Enabled Unsupervised Learning for Neural Network-Based Polar Decoder and Jointly Optimized Blind Equalizer
摘要：
最近，伴随式丢失已经被提出来实现基于神经网络的BCH/LDPC解码器的“无监督学习”。然而，该设计方法不能直接应用于极化码，并且尚未在变化的信道下进行评估。在这项工作中，我们提出了两个修改的综合征损失，以促进无监督学习的接收器。然后，我们首先将其应用于基于神经网络的置信传播（BP）极性解码器。在CRC启用的综合征丢失的帮助下，BP解码器甚至可以在误块率方面优于传统的监督学习方法。其次，我们提出了一种联合优化的综合征使能盲均衡器，它可以避免传输的训练序列，并达到全球最佳的1.3 dB增益比非盲最小均方误差（MMSE）均衡器。
英文摘要：
Recently, the syndrome loss has been proposed to achieve "unsupervised learning" for neural network-based BCH/LDPC decoders. However, the design approach cannot be applied to polar codes directly and has not been evaluated under varying channels. In this work, we propose two modified syndrome losses to facilitate unsupervised learning in the receiver. Then, we first apply it to a neural network-based belief propagation (BP) polar decoder. With the aid of CRC-enabled syndrome loss, the BP decoder can even outperform conventional supervised learning methods in terms of block error rate. Secondly, we propose a jointly optimized syndrome-enabled blind equalizer, which can avoid the transmission of training sequences and achieve global optimum with 1.3 dB gain over non-blind minimum mean square error (MMSE) equalizer.
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