Algorithm For Polar Codes Research Articles

A Deterministic Algorithm for Computing the Weight Distribution of Polar Code

In this work, we present a deterministic algorithm for computing the entire weight distribution of polar codes. As the first step, we derive an efficient recursive procedure to compute the weight distribution that arises in successive cancellation decoding of polar codes along any decoding path. This solves the open problem recently posed by Polyanskaya, Davletshin, and Polyanskii. Using this recursive procedure, at code length <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> , we can compute the weight distribution of any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">polar cosets</i> in time <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). We show that any polar code can be represented as a disjoint union of such polar cosets; moreover, this representation extends to polar codes with dynamically frozen bits. However, the number of polar cosets in such representation scales exponentially with a parameter introduced herein, which we call the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mixing factor</i> . To upper bound the complexity of our algorithm for polar codes being decreasing monomial codes, we study the range of their mixing factors. We prove that among all decreasing monomial codes with rates at most 1/2, self-dual Reed-Muller codes have the largest mixing factors. To further reduce the complexity of our algorithm, we make use of the fact that, as decreasing monomial codes, polar codes have a large automorphism group. That automorphism group includes the block lower-triangular affine group (BLTA), which in turn contains the lower-triangular affine group (LTA). We prove that a subgroup of LTA acts transitively on certain subsets of decreasing monomial codes, thereby drastically reducing the number of polar cosets that we need to evaluate. This complexity reduction makes it possible to compute the weight distribution of polar codes at length <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> = 128.

Multiple Node Flip Fast-SSC Decoding Algorithm for Polar Codes Based on Node Reliability

Multiple Node Flip Fast-SSC Decoding Algorithm for Polar Codes Based on Node Reliability

Novel Low Complexity BP Decoding Algorithms for Polar Codes: Simplifying on Non-Linear Operations

The parallel nature of the belief propagation (BP) decoding algorithm for polar codes opens up a real possibility of high throughput and low decoding latency during hardware implementation. To address the problem that the BP decoding algorithm introduces high-complexity non-linear operations in the iterative messages update process, this paper proposes to simplify these operations and develops two novel low complexity BP decoding algorithms, namely, exponential BP (Exp-BP) decoding algorithm and quantization function BP (QF-BP) decoding algorithm. The proposed algorithms simplify the compound hyperbolic tangent function by using probability distribution fitting techniques. Specifically, the Exp-BP algorithm simplifies two types of non-linear operations into single non-linear operation using the piece-wise exponential model function, which can approximate the hyperbolic tangent function in the updating formula. The QF-BP algorithm eliminates non-linear operations using the non-uniform quantization in the updating formula, which is effective in reducing computational complexity. According to the simulation results, the proposed algorithms can reduce the computational complexity up to 50% in each iteration with a loss of less than 0.1 dB compared with the BP decoding algorithm, which can facilitate the hardware implementation.

Polar Codes: Encoding/Decoding and Rate-Compatible Jointly Design for HARQ System

Polar coding are the first class of provable capacity-achieving coding techniques for a wide range of channels. With an ideal recursive structure and many elegant mathematical properties, polar codes are inherently implemented with low complexity encoding and decoding algorithms. Since the block length of the original polar construction is limited to powers of two, rate-compatible polar codes (RCPC) are presented to meet the flexible length/rate transmission requirements in practice. The RCPC codes are well-conditioned to combine with the hybrid automatic repeat request (HARQ) system, providing high throughput efficiency and such RCPC-HAPQ scheme is commonly used in delay-insensitive communication system. This paper first gives a survey of both the classical and state-of-the-art encoding/decoding algorithms for polar codes. Then the RCPC construction methods are discussed, including the puncturing, shortening, multi-kernel construction, etc. Finally, we investigate several RCPC-HARQ jointly design systems and discuss their encoding gain and re-transmission diversity gain.

Bidirekciono stek dekodovanje polarnih kodova

Introduction/purpose: The paper introduces a reduced latency stack decoding algorithm of polar codes, inspired by the bidirectional stack decoding of convolutional codes and based on the folding technique. Methods: The stack decoding algorithm (also known as stack search) that is useful for decoding tree codes, the list decoding technique introduced by Peter Elias and the folding technique for polar codes which is used to reduce the latency of the decoding algorithm. The simulation was done using the Monte Carlo procedure. Results: A new polar code decoding algorithm, suitable for parallel implementation, is developed and the simulation results are presented. Conclusions: Polar codes are a class of capacity achieving codes that have been adopted as the main coding scheme for control channels in 5G New Radio. The main decoding algorithm for polar codes is the successive cancellation decoder. This algorithm performs well at large blocklengths with a low complexity, but has very low reliability at short and medium blocklengths. Several decoding algorithms have been proposed in order to improve the error correcting performance of polar codes. The successive cancellation list decoder, in conjunction with a cyclic redundancy check, provides very good error-correction performance, but at the cost of a high implementation complexity. The successive cancellation stack decoder provides similar error-correction performance at a lower complexity. Future machine-type and ultra reliable low latency communication applications require high-speed low latency decoding algorithms with good error correcting performance. In this paper, we propose a novel decoding algorithm, inspired by the bidirectional stack decoding of classical convolutional codes, with reduced latency that achieves similar performance as the classical successive cancellation list and successive cancellation stack decoding algorithms. The results are presented analytically and verified by simulation.

Improved NSC decoding algorithm for polar codes based on multi-in-one neural network

Neural network-based decoding algorithms have potential value to be researched in the field of polar codes due to their low decoding latency. The neural successive cancellation (NSC) algorithm, combining deep learning and successive cancellation (SC) algorithm of polar codes, was proposed to reduce the latency of decoding. In terms of overall latency, the NSC algorithm does not fully consider the parallel decoding of special nodes in SC decoding tree, which limits the reduction of system delay to a certain extent. In this paper, we propose a multi-in-one neural simplified successive cancellation (MIO-NSSC) decoding algorithm for polar codes based on deep learning. The proposed MIO-NSSC algorithm, which is suitable for general nodes, mainly improves the existing fast simplified successive cancellation (FSSC) and the NSC algorithms to obtain a multi-in-one neural network instead of multiple neural networks in the NSC algorithm by using a new training strategy. Through applying the FSSC algorithm to a special node, the decoding delay of the proposed algorithm is further reduced. The experimental results demonstrate that the proposed MIO-NSSC algorithm can achieve significant latency reduction and resource consumption efficiency improvement compared with the NSC algorithm. The latency of the proposed MIO-NSSC decoding algorithm is about 21% lower than that of the NSC algorithm, and approximately seven neural networks are saved compared with the NSC algorithm. Furthermore, the MIO-NSSC algorithm can reduce the computational complexity without loss of performance.

Optimization and Hardware Implementation of Learning Assisted Min-Sum 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 adapts the offset min-sum (OMS) algorithm for polar codes, and both algorithms are improved via learning. For all message updates, conventional min-sum decoding algorithms use the same scaling factor or offset, which is usually obtained by numerical simulations. By modeling the data flow of min-sum algorithms as a deep neural network, the parameters used in the message passing updates of min-sum decoders can be different for each message update, and are obtained by training and optimizing the corresponding deep neural network. The simulation results show that the proposed SOMS algorithm based on deep learning performs better than all existing BP-based algorithms. Moreover, this paper presents an efficient hardware architecture of the proposed SOMS algorithm. The K-Means clustering algorithm is applied to reduce the number of possible parameters of the neural network, leading to reduced energy consumption and memory requirement with negligible error performance degradation. The proposed architecture of the SOMS algorithm for a (256,128) polar code is implemented and validated on the Xilinx Artix-7 field-programmable gate array.

Efficient-Memory and Low-Latency BP Decoding Algorithm for Polar Codes

In order to reduce memory and latency of the belief propagation (BP) decoding algorithm, we use new constituent codes to simplify the message passing of factor graph for polar codes. Since the left-to-right (right) messages of top level of constituent code are directly updated by the right-to-left (left) messages without visiting its sub-factor graph, which saves the corresponding memory entries and decoding latency. Therefore, based on our proposed constituent codes, we can obtain a memory-efficient and low-latency BP decoding algorithm. Numerical results show that the proposed decoding algorithm saves about 4.6%-17.6% memory entries of the XJ-BP decoding and the latency is reduced by 19%-23.5%.

A Simplified Decoding Method of Polar Codes Based on Hypothesis Testing

In this letter, a simplified successive cancellation decoding algorithm of polar codes is proposed, where a hard decision rule is adopted by exploiting the prior information in frozen bits for better decoding performance. Considering the difference of the node reliability, a hypothesis-testing-based strategy is designed to select reliable unstructured nodes for hard decision. Then the reliable nodes are decoded by the proposed hard decision method without serial recursion, which reduces the decoding latency. Simulation results show the advantages of the proposed method in terms of the decoding latency and error-correction ability compared with the existing methods.

Transformation of Binary Linear Block Codes to Polar Codes With Dynamic Frozen

In this paper, a general transformation of binary linear block codes (BLBCs) to (possibly, multi-kernel) polar codes with dynamic frozen bits is proposed. Through a simple matrix permutation operation, a one-to-one connection between the codewords of a BLBC and its transformed polar code can be established. This transformation allows the usage of any decoding algorithm of polar codes for efficient soft decoding of BLBCs, including the powerful successive cancellation list (SCL) decoding algorithm. Simulations show that the soft SCL polar decoding of BLBCs can achieve a comparative performance to the order statistic decoding (OSD), as well as the maximum-likelihood decoding (MLD) in certain cases, with a much lower computational complexity.

Fast Encoding of Polar Codes With Reed–Solomon Kernel

A low-complexity systematic encoding algorithm for polar codes with Reed–Solomon (RS) kernel is presented. The proposed method relies on fast Fourier transform-based RS encoding techniques. An application of polar codes in storage systems is considered.

유한한 길이에서 성능이 향상된 BP 극 복호기

In this paper, we discuss the belief propagation decoding algorithm for polar codes. The performance of Polar codes for shorter lengths is not satisfactory. Motivated by this, we propose a novel technique to improve its performance at short lengths. We showed that the probability of messages passed along the factor graph of polar codes, can be increased by multiplying the current message of nodes with their previous message. This is like a feedback path in which the present signal is updated by multiplying with its previous signal. Thus the experimental results show that performance of belief propagation polar decoder can be improved using this proposed technique. Simulation results in binary-input additive white Gaussian noise channel (BI-AWGNC) show that the proposed belief propagation polar decoder can provide significant gain of 2 dB over the original belief propagation polar decoder with code rate 0.5 and code length 128 at the bit error rate (BER) of .

A High Throughput List Decoder Architecture for Polar Codes

While long polar codes can achieve the capacity of arbitrary binary-input discrete memoryless channels when decoded by a low complexity successive cancelation (SC) algorithm, the error performance of the SC algorithm is inferior for polar codes with finite block lengths. The cyclic redundancy check (CRC) aided successive cancelation list (SCL) decoding algorithm has better error performance than the SC algorithm. However, current CRC aided SCL (CA-SCL) decoders still suffer from long decoding latency and limited throughput. In this paper, a reduced latency list decoding (RLLD) algorithm for polar codes is proposed. Our RLLD algorithm performs the list decoding on a binary tree, whose leaves correspond to the bits of a polar code. In existing SCL decoding algorithms, all the nodes in the tree are traversed and all possibilities of the information bits are considered. Instead, our RLLD algorithm visits much fewer nodes in the tree and considers fewer possibilities of the information bits. When configured properly, our RLLD algorithm significantly reduces the decoding latency and hence improves throughput, while introducing little performance degradation. Based on our RLLD algorithm, we also propose a high throughput list decoder architecture, which is suitable for larger block lengths due to its scalable partial sum computation unit. Our decoder architecture has been implemented for different block lengths and list sizes using the TSMC 90nm CMOS technology. The implementation results demonstrate that our decoders achieve significant latency reduction and area efficiency improvement compared with other list polar decoders in the literature.

Reduced Complexity Belief Propagation Decoding Algorithm for Polar Codes Based on the Principle of Equal Spacing

Reduced Complexity Belief Propagation Decoding Algorithm for Polar Codes Based on the Principle of Equal Spacing

A Generalization Belief Propagation Decoding Algorithm for Polar Codes Based on Particle Swarm Optimization

We propose a generalization belief propagation (BP) decoding algorithm based on particle swarm optimization (PSO) to improve the performance of the polar codes. Through the analysis of the existing BP decoding algorithm, we first introduce a probability modifying factor to each node of the BP decoder, so as to enhance the error correcting capacity of the decoding. Then, we generalize the BP decoding algorithm based on these modifying factors and drive the probability update equations for the proposed decoding. Based on the new probability update equations, we show the intrinsic relationship of the existing decoding algorithms. Finally, in order to achieve the best performance, we formulate an optimization problem to find the optimal probability modifying factors for the proposed decoding algorithm. Furthermore, a method based on the modified PSO algorithm is also introduced to solve that optimization problem. Numerical results show that the proposed generalization BP decoding algorithm achieves better performance than that of the existing BP decoding, which suggests the effectiveness of the proposed decoding algorithm.