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.

中文翻译：

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基于神经网络的极性解码器和联合优化盲均衡器的综合征启用无监督学习

最近，已经提出了综合征损失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，也没有在不同信道下进行评估。在这项工作中，我们提出了两个修正的综合征损失，以促进接收器中的无监督学习。然后，我们首先将其应用于基于神经网络的信念传播 (BP) 极性解码器。在启用 CRC 的综合症丢失的帮助下，BP 解码器甚至可以在块错误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启用综合症的盲均衡器，它可以避免训练序列的传输，并在非盲最小均方误差 (MMSE) 均衡器上以 1.3 dB 的增益实现​​全局最优。