We revisit the idea of using deep neural networks for one-shot decoding of random and
structured codes, such as polar codes. Although it is possible to achieve maximum a …

Deep learning recently shows strong competitiveness to improve polar code decoding.
However, suffering from prohibitive training and computation complexity, the conventional …

The training complexity of deep learning-based channel decoders scales exponentially with
the codebook size and therefore with the number of information bits. Thus, neural network …

Channel coding is a fundamental building block in any communications system. High
performance codes, with low complexity encoding and decoding are a must-have for future …

Polar codes have drawn much attention and been adopted in 5G New Radio (NR) due to
their capacity-achieving performance. Recently, as the emerging deep learning (DL) …

We describe a novel approach to interpret a polar code as a low-density parity-check
(LDPC)-like code with an underlying sparse decoding graph. This sparse graph is based on …

The state-of-the-art soft-output decoder for polar codes is a message-passing algorithm
based on belief propagation, which performs well at the cost of high processing and storage …

We show that the performance of iterative belief propagation (BP) decoding of polar codes
can be enhanced by decoding over different carefully chosen factor graph realizations. With …

The decoding latency of polar codes can be reduced by implementing fast parallel decoders
in the last stages of decoding. In this letter, we present five such decoders corresponding to …

Polar codes are a class of codes versatile enough to achieve the Shannon bound in a large
array of source and channel coding problems. For that reason it is important to have efficient …