Unsupervised Learning for Neural Network-based Polar Decoder via Syndrome Loss

TL;DR

This paper introduces a modified syndrome loss enabling unsupervised learning for neural network-based polar decoders, leveraging the unique structure of polar codes to reduce reliance on labeled data and improve decoding performance.

Contribution

It proposes a novel modified syndrome loss tailored for polar codes, facilitating unsupervised training of neural network decoders without labeled data.

Findings

The method effectively trains polar decoders without labeled data.

Domain knowledge of polar codes enhances unsupervised learning.

Simulation results show improved decoding accuracy.

Abstract

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.