With the rapid growth of deep learning in many fields, machine learning-assisted communication systems had attracted lots of researches with many eye-catching initial results. At the present stage, most of the methods still have great demand of massive labeled data for supervised learning. However, obtaining labeled data in the practical applications is not feasible, which may result in severe performance degradation due to channel variations. To overcome such a constraint, syndrome loss has been proposed to penalize non-valid decoded codewords and achieve unsupervised learning for neural network-based decoder. However, it cannot be applied to polar decoder directly. In this work, by exploiting the nature of polar codes, we propose a modified syndrome loss. From simulation results, the proposed method demonstrates that domain-specific knowledge and know-how in code structure can enable unsupervised learning for neural network-based polar decoder.
翻译:随着许多领域深层学习的迅速发展,机器学习辅助通信系统吸引了许多研究,并取得了许多初步结果。在目前阶段,大多数方法仍然需要大量标签数据来监督学习。然而,在实际应用中获取标签数据并不可行,这可能导致因频道变异而导致性能严重退化。为了克服这种制约,提议了综合症损失,以惩罚无效的解码词,并实现神经网络解码器不受监督的学习。然而,它不能直接适用于极地解码器。在这项工作中,我们通过利用极地码的性质,提议了一种经修改的综合症损失。从模拟结果中,拟议的方法表明,代码结构中特定领域的知识和诀窍能够使以神经网络为基础的极地解码器获得不受监督的学习。