论文标题
基于神经网络的极性解码器和共同优化的盲均衡器，支持综合征的无监督学习
Syndrome-Enabled Unsupervised Learning for Neural Network-Based Polar Decoder and Jointly Optimized Blind Equalizer
论文作者
论文摘要
最近，已经提出了综合征损失，以实现基于神经网络的BCH/LDPC解码器的“无监督学习”。但是，该设计方法不能直接应用于极地代码，也不能在不同的通道下进行评估。在这项工作中，我们提出了两种改良的综合征损失，以促进接收者中的无监督学习。然后，我们首先将其应用于基于神经网络的信念传播（BP）极地解码器。借助支持CRC的综合征损失，BP解码器甚至可以在块错误率方面超过常规监督学习方法。其次，我们提出了一个共同优化综合征的盲目均衡器，该均衡器可以避免训练序列的传输并以1.3 dB的增益超过非盲型最小平方误差（MMSE）均衡器，并实现全局最佳。
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.
