In this paper, we propose two novel min-sum (MS) decoding algorithms based on deep
learning for polar codes, an offset min-sum (OMS) and a scaling offset min-sum (SOMS) …

Polar codes have received a lot of attention due to their capacity-achieving performance and
low complexity. This paper proposes a novel scaling offset min-sum (SOMS) algorithm and …

Theoretically, conventional decoders for polar codes can be entirely replaced by neural
network (NN) with enough size and enough training, which called NN decoder. But the …

Polar codes have emerged as important channel codes because of their capacity-achieving
property. For low-complexity polar decoding, hardware architectures for successive …

Research on polar codes has been constantly gaining attention over the last decade, by
academia and industry alike, thanks to their capacity-achieving error-correction performance …

In this paper, we propose a simplified multi-bit successive cancellation list (SMSCL)
decoding method for polar codes. In the proposed SMSCL decoding, frozen bits are ignored …

Polar coding has found its way into many realms in communications and information theory.
In most implementation setups, they are accompanied with the list successive cancellation …

Polar codes are of great interests because they provably achieve the symmetric capacity of
discrete memoryless channels with arbitrary input alphabet sizes while having an explicit …

Polar codes are the first provable capacity-achieving forward error correction (FEC) codes.
In general polar codes can be decoded via either successive cancellation (SC) or belief …

A deep-learning-aided successive-cancellation list (DL-SCL) decoding algorithm for polar
codes is introduced with deep-learning-aided successive-cancellation (DL-SC) decoding …