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 …

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

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 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 …

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 …

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 …

Capacity-achieving polar codes have gained significant attention in recent years. In general,
polar codes can be decoded by either successive cancellation (SC) or the belief …

Although iterative decoding of polar codes has recently made huge progress based on the
idea of permuted factor graphs, it still suffers from a non-negligible performance degradation …

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 …

Polar codes are a channel coding scheme for the next generation of wireless
communications standard (5G). The belief propagation (BP) decoder allows for parallel …