In this work, we introduce a deep learning-based polar code construction
algorithm. The core idea is to represent the information/frozen bit indices of
a polar code as a binary vector which can be interpreted as trainable weights
of a neural network (NN). For this, we demonstrate how this binary vector can
be relaxed to a soft-valued vector, facilitating the learning process through
gradient descent and enabling an efficient code construction. We further show
how different polar code design constraints (e.g., code rate) can be taken into
account by means of careful binary-to-soft and soft-to-binary conversions,
along with rate-adjustment after each learning iteration. Besides its
conceptual simplicity, this approach benefits from having the
"decoder-in-the-loop", i.e., the nature of the decoder is inherently taken into
consideration while learning (designing) the polar code. We show results for
belief propagation (BP) decoding over both AWGN and Rayleigh fading channels
with considerable performance gains over state-of-the-art construction schemes.

通过使用深度学习建立极化码，我们将信息 / 冻结比特索引表示为二进制向量，并通过梯度下降优化这个向量，同时考虑译码器，实现了在 AWGN 和 Rayleigh 信道上的很好表现的极化码设计。

基于深度学习的极化码设计

Deep Learning-based Polar Code Design

In this letter we propose a new hybrid code called "RM-Polar" codes. This new
codes are constructed by combining the construction of Reed-Muller (RM) code
and Polar code. It has much larger minimum Hamming distance than Polar codes,
therefore it has much better error performance than Polar codes.

本文介绍了一种新的混合编码方案 RM-Polar codes，该编码方案采用了 Reed-Muller code 和 Polar code 的结构特点，其中，RM-Polar codes 具有比 Polar codes 更大的最小 Hamming 距离，因此具有更好的误码性能。