In previous papers, decoding schemes which did not use machine learning considered
additive white Gaussian noise or memoryless impulse noise. The decoding methods …

This paper presents deep learning (DL) methods to optimize polar belief propagation (BP)
decoding and concatenated LDPC-polar codes. First, two-dimensional offset Min-Sum (2-D …

Polar codes are known as the first capacity-achievable codes with low encoding and
decoding complexity. The sequential decoding nature of traditional polar decoding …

Polar code has the characteristics of simple coding and high reliability, and it has been used
as the control channel coding scheme of 5G wireless communication. However, its decoding …

Recently, the dynamic-successive cancellation list flip (D-SCLF) decoder has been
proposed to improve the high-order flipping performance of existing successive cancellation …

Polar codes have been adopted as the control channel coding scheme in the fifth generation
new radio (5G NR) standard due to its capacity-achievable property. Traditional polar …

Neural network (NN) based decoders have appeared as potential candidates to replace
successive cancellation (SC) based and belief propagation (BP) decoders for polar codes …

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 …

Recently, a generalized successive cancellation list (SCL) decoder implemented with shifted-
pruning (SP) scheme, namely the SCL-SP-ω decoder, is presented for polar codes, which is …

In this paper, we apply neural networks to the encoding and decoding of polar codes. Polar
codes in 5G communication which meets the simple and ultra-low latency can also be …