Short Block Codes Research Articles

A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes

The conventional soft decision decoding (SDD) methods require various hard decision decoders (HDDs) based on different codes or re-manipulate the generator matrix by the complicated Gaussian elimination technique according to the bit reliability. This paper presents a general multi-class neural network (NN)-based decoder for the short linear block codes, where no HDD and Gaussian elimination are required once the NN is constructed. This network architecture performs multi-classification to select the messages with high occurrence probabilities and chooses the best codeword on a maximum likelihood basis. Simulation results show that the developed approach outperforms the existing deep neural network (DNN)-based decoders in terms of decoding time and bit error rate (BER). The error-correcting performance is also superior to the conventional Chase-II algorithm and is close to the ordered statistics decoding (OSD) in most cases. For Bose–Chaudhuri–Hocquenghem (BCH) codes, the SNR is improved by 1dB to 4dB as the BER is 10−4. For the (23, 12) quadratic residue (QR) code, the SNR is improved by 2dB when the BER is 10−3. The developed NN-based decoder is quite general and applicable to various short linear block codes with good BER performance.

Density Evolution Analysis of the Iterative Joint Ordered-Statistics Decoding for NOMA

In this paper, we develop a density evolution (DE) framework for analyzing the iterative joint decoding (JD) for non-orthogonal multiple access (NOMA) systems, where the ordered-statistics decoding (OSD) is applied to decode short block codes. We first investigate the density-transform feature of the soft-output OSD (SOSD), by deriving the density of the extrinsic log-likelihood ratio (LLR) with known densities of the priori LLR. Then, we represent the OSD-based JD by bipartite graphs (BGs), and develop the DE framework by characterizing the density-transform features of nodes over the BG under the binary phase shift keying (BPSK) transmission. Numerical examples show that the proposed DE framework accurately tracks the evolution of LLRs during the iterative decoding, especially at moderate-to-high SNRs. Based on the DE framework, we further analyze the BER performance of the OSD-based JD, and the convergence points of the two-user and equal-power systems.

A Low-Complexity Ordered Statistic Decoding of Short Block Codes

This letter is concerned with a generalized ordered statistic decoding (OSD) algorithm, called locally constrained OSD (LC-OSD). Instead of order- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> reprocessing on the most reliable independent bits, the LC-OSD searches for test error patterns using the serial list Viterbi algorithm (SLVA) over a trellis specified by a local parity-check matrix. We derive several early stopping criteria that can be used to reduce the number of searches. Numerical results show that the LC-OSD algorithm with the proposed stopping criteria has much lower time complexity than the original OSD but incurs negligible performance loss.

Doped low-density parity-check codes

In this paper, we propose a doping approach to lower the error floor of Low-Density Parity-Check (LDPC) codes. The doping component is a short block code in which the information bits are selected from the coded bits of the dominant trapping sets of the LDPC code. Accordingly, an algorithm for selecting the information bits of the short code is proposed, and a specific two-stage decoding algorithm is presented. Simulation results demonstrate that the proposed doped LDPC code achieves up to 2.0 ​dB gain compared with the original LDPC code at a frame error rate of 10−6. Furthermore, the proposed design can lower the error floor of original LDPC codes.

Girth aware normalized min sum decoding algorithm for shorter length low density parity check codes

Recently, short block codes are in great demand due to the emergent applications requiring the transmission of a short data unit and can guarantee speedy communication, with a minimum of latency and complexity which are among the technical challenges in today’s wireless services and systems. In the context of channel coding using low density parity check (LDPC) codes, the shorter length LDPC block codes are more likely to have short cycles with lengths of 4 and 6. The effect of the cycle with the minimum size is that this one prevents the propagation of the information in the Tanner graph during the iterative process. Therefore, the message decoded by short block code is assumed to be of poor quality due to short cycles. In this work, we present a study of the evolution of the messages on check nodes during the iterative decoding process when using the LDPC decoding algorithm normalized min sum (NMS), to see the destructive effect of short cycles and justify the effectiveness of the girth aware normalized min sum (GA-NMS) decoding LDPC codes algorithm in terms of correction of the errors, particularly for the codes with short cycles 4 and 6. In addition to this, the GA-NMS algorithm is evaluated in terms of bit error rate performance and convergence behavior, using wireless regional area networks (WRAN) LDPC code, which is considered as a short block code.

SIMPLIFIED SUCCESSIVE-CANCELLATION LIST POLAR DECODING FOR BINARY LINEAR BLOCK CODES

This paper aims to take advantage of the performances of polar decoding techniques for the benefit of binary linear block codes (BLBCs) with the main objective is to study the performances of the SSCL decoding for short-length BLBCs. Polar codes are one of the most recent error-correcting codes to be invented, and they have been mathematically demonstrated to be able to correct all errors under a specific situation, using the successive-cancellation decoder. However, their performances for real-time wireless communications at short block lengths remain less attractive. To take advantage of the performance of these codes in favor of error correction codes of short block length, an adaptation of the simplified successive-cancellation list as a decoder for polar codes for the benefit of short block length binary linear block codes is presented in this paper. This adaptation makes it possible to take advantage of the performances of less complex decoding methods for polar codes for BLBCs with latency and complexity optimization of the standard successive-cancellation list decoder. The experiment shows that the method can achieve the performances of the most famous order statistic decoder for binary linear block codes, which can achieve the performances of maximum-likelihood decoding with computational complexity and memory constraints.

A Low-Complexity Probabilistic Amplitude Shaping With Short Linear Block Codes

We propose a new probabilistic amplitude shaping (PAS) scheme based on short linear block codes. In the proposed system, the capacity-approaching signal distribution is generated in the process of decoding linear block codes for a given information bit sequence. We associate the design problem of shaping codes with the classical covering problem, suggesting the use of good covering codes for shaping. From simulation results, it is demonstrated that by selecting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">perfect</i> binary codes as our shaping codes, the proposed scheme offers a shaping gain of around 0.3–1.0 dB. By comparing with the enumerative sphere shaping (ESS) of the same block length, we verify that the proposed scheme achieves significantly lower storage complexity and computational complexity at the receiver even with comparable block error rate performance.

Pruning and Quantizing Neural Belief Propagation Decoders

We consider near maximum-likelihood (ML) decoding of short linear block codes. In particular, we propose a novel decoding approach based on neural belief propagation (NBP) decoding recently introduced by Nachmani et al. in which we allow a different parity-check matrix in each iteration of the algorithm. The key idea is to consider NBP decoding over an overcomplete parity-check matrix and use the weights of NBP as a measure of the importance of the check nodes (CNs) to decoding. The unimportant CNs are then pruned. In contrast to NBP, which performs decoding on a given fixed parity-check matrix, the proposed pruning-based neural belief propagation (PB-NBP) typically results in a different parity-check matrix in each iteration. For a given complexity in terms of CN evaluations, we show that PB-NBP yields significant performance improvements with respect to NBP. We apply the proposed decoder to the decoding of a Reed-Muller code, a short low-density parity-check (LDPC) code, and a polar code. PB-NBP outperforms NBP decoding over an overcomplete parity-check matrix by 0.27-0.31 dB while reducing the number of required CN evaluations by up to 97%. For the LDPC code, PB-NBP outperforms conventional belief propagation with the same number of CN evaluations by 0.52 dB. We further extend the pruning concept to offset min-sum decoding and introduce a pruning-based neural offset min-sum (PB-NOMS) decoder, for which we jointly optimize the offsets and the quantization of the messages and offsets. We demonstrate performance 0.5 dB from ML decoding with 5-bit quantization for the Reed-Muller code.

LEARN Codes: Inventing Low-Latency Codes via Recurrent Neural Networks

Designing channel codes under low-latency constraints is one of the most demanding requirements in 5G standards. However, a sharp characterization of the performance of traditional codes is available only in the large block-length limit. Guided by such asymptotic analysis, code designs require large block lengths as well as latency to achieve the desired error rate. Tail-biting convolutional codes and other recent state-of-the-art short block codes, while promising reduced latency, are neither robust to channel-mismatch nor adaptive to varying channel conditions. When the codes designed for one channel (e.g., Additive White Gaussian Noise (AWGN) channel) are used for another (e.g., non-AWGN channels), heuristics are necessary to achieve non-trivial performance. In this paper, we first propose an end-to-end learned neural code, obtained by jointly designing a Recurrent Neural Network (RNN) based encoder and decoder. This code outperforms canonical convolutional code under block settings. We then leverage this experience to propose a new class of codes under low-latency constraints, which we call Low-latency Efficient Adaptive Robust Neural (LEARN) codes . These codes outperform state-of-the-art low-latency codes and exhibit robustness and adaptivity properties. LEARN codes show the potential to design new versatile and universal codes for future communications via tools of modern deep learning coupled with communication engineering insights.

Research on Error Correction Coding Closed Set Recognition Technology Based on Deep Learning Method

It is very difficult to obtain information in non-cooperative areas. Error correction coding analysis is one of the key and difficult links. The general analysis is highly demanding and highly dependent on the level of personal analysis. For this reason, we mainly study various deep learning methods, which are applied to scrambling codes, short constraint length convolutional codes, and short constraint length block codes and we also I want to rely on the development of deep learning now, to study and realize intelligence and automated analysis.

Near-Hashing-Bound Multiple-Rate Quantum Turbo Short-Block Codes

Quantum stabilizer codes (QSCs) suffer from a low quantum coding rate, since they have to recover the quantum bits (qubits) in the face of both bit-flip and phase-flip errors. In this treatise, we conceive a low-complexity concatenated quantum turbo code (QTC) design exhibiting a high quantum coding rate. The high quantum coding rate is achieved by combining the quantum-domain version of short-block codes (SBCs) also known as single parity check (SPC) codes as the outer codes and quantum unity-rate codes (QURCs) as the inner codes. Despite its design simplicity, the proposed QTC yields a near-hashing-bound error correction performance. For instance, compared to the best half-rate QTC known in the literature, namely the QIrCC-QURC scheme, which operates at the distance of $D = 0.037$ from the quantum hashing bound, our novel QSBC-QURC scheme can operate at the distance of $D = 0.029$. It is worth also mentioning that this is the first instantiation of QTCs capable of adjusting the quantum encoders according to the quantum coding rate required for mitigating the Pauli errors given the different depolarizing probabilities of the quantum channel.

High performance short-block binary regular LDPC codes

LDPC code shows a good performance with long-block codes. However, certain channels are constrained to use short-block codes due to latency. Therefore, concatenated LDPC codes with iterative decoding is a good choice to get a good performance. Concatenated binary LDPC codes were introduced as a class of concatenated codes in which the LDPC codes are irregular codes having different parameters. Although irregular LDPC codes are more efficient than regular codes, irregular LDPC codes have an error floor and a higher encoding complexity than regular code. In this paper, in order to get a good performance/complexity trade-off with a short-block code, we investigate a parallel concatenation of two identical regular binary LDPC codes, using an interleaver. Simulation results show that the proposed code outperforms a single LDPC code. The proposed code needs less time decoding delay than a single LDPC code with the same iteration number. Also, the proposed code needs a less number of iterations to achieve the same performance of a single LDPC code that leads to less decoding complexity and decoding delay. A simplified algorithm, the min-sum algorithm, that is used to decode the component codes shows a small performance loss with respect to the sum-product algorithm.

Construction of short-block nonbinary LDPC codes based on cyclic codes

In this paper, we focus on short-block nonbinary LDPC (NB-LDPC) codes based on cyclic codes. Based on Tanner graphs' isomorphism, we present an efficient search algorithm for finding non-isomorphic binary cyclic LDPC codes. Notice that the parity-check matrix H of the resulting code is square and not of full rank, and its row weight and column weight are the same. By replacing the ones in the same column of H with a nonzero element of finite fields GF(q), a class of NB-LDPC codes over GF(q) is obtained. Numerical results show that the constructed codes perform well over the AWGN channel and have fast decoding convergence. Therefore, the proposed NB-LDPC codes provide a promising coding scheme for low-latency and high-reliability communications.

An Effective Method of SNR Estimation for LDPC-CPM

The technique of SNR estimation is one of the key technologies in adaptive frequency hopping system. The methods of channel quality estimation for non-linear continuous phase modulation (CPM) signals have some limitations. Therefore, the algorithm of channel quality estimation for CPM signals is worthy of further study. Some similar phase characteristics between sampling CPM and MPSK motivate us to propose a channel estimation algorithm with applications to nonlinear CPM using linear modulation signal processing. A comprehensive analysis of LDPC-CPM schemes using proposed algorithm is presented, and simulation results indicate that the proposed method can not only estimate channel quality well but also make the normalized MSE (NMSE) of SNR estimate close to/less than 0.1 dB at SNR of 4 dB using short block codes. It shows that the algorithm in this paper is effective enough to estimate the signal to noise ratio (SNR). Meanwhile, the algorithm in this paper reduces the complexity of computation compared with other traditional algorithms.

On &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$A^{\ast}$&lt;/tex-math&gt;&lt;/inline-formula&gt; Algorithms for Decoding Short Linear Block Codes

The $A^{\ast}$ algorithm for decoding linear block codes is an efficient tree-search method for decoding short linear block codes. The information contained in the parity bits are potentially useful for reducing the edges needed in the tree search. We propose to extract the information embedded in the parity bits by considering sums of parity bits which can be decided by part of the message bits. Analysis for the number of required message bits for obtaining sums of parity bits is provided. The information contained in the parity bits can help to reduce the number of search tree edges.

An Efficient Construction and Low Complexity Collaborative Decoding of Reed-Solomon Concatenated with Modified Polar Codes

Polar codes are the first explicit code sequences that provably achieve capacity for discrete memory-less channels. However, their performance with successive cancellation (SC) decoding is unimpressive at practical block-lengths. In this paper, we propose a scheme of concatenating Reed-Solomon (RS) with modified polar codes to improve the performance of SC. Firstly, short repetition block codes are used as outer block codes to modify the construction of polar codes. This step can improve the performance of SC with a lower complexity compared with the other schemes. And secondly, to reduce the probability of the error propagation of SC, the modified polar codes are concatenated with RS codes. We divide the decoding process of the polar codes into several pieces. After one piece is decoded, the RS decoder is used to correct the residual errors from polar codes in this piece. By this collaborative method, the number of the error propagation can be reduced obviously. Simulation shows that the proposed scheme can obtain a good error-rate performance at finite block lengths and have a significantly better error-decay rate with a lower complexity.

Spatial Coupling of Generator Matrices: A General Approach to Design Good Codes at a Target BER

For any given short code (referred to as the basic code), block Markov superposition transmission (BMST) provides a simple way to obtain predictable extra coding gain by spatially coupling the generator matrix of the basic code. This paper presents a systematic design methodology for BMST systems to approach the channel capacity at any given target bit error rate (BER) of interest. To simplify the design, we choose the basic code as the Cartesian product of a short block code. The encoding memory is then inferred from the genie-aided lower bound according to the performance gap of the short block code to the corresponding Shannon limit at the target BER. In addition to the sliding-window decoding algorithm, we propose to perform one more phase decoding to remove residual (rare) errors. A new technique that assumes a noisy genie is proposed to upper bound the performance. Under some mild assumptions, these genie-aided bounds can be used to predict the performance of the proposed two-phase decoding algorithm in the extremely low BER region. Using the Cartesian product of a repetition code as the basic code, we construct a BMST system with an encoding memory 30 whose performance at the BER of $10^{-15}$ can be predicted within 1 dB away from the Shannon limit over the binary-input additive white Gaussian noise channel.

Optimal joint design of orthonormal real valued short time block code and linear transceiver

The main contribution of this paper is to propose an optimal joint design of an orthonormal real valued short time block code and a linear transceiver for multi-input/multi-output (MIMO) wireless digital communication systems. Firstly, a relaxed zero forcing condition based on both the short time block code and the linear transceiver is optimally derived via the Karhunen–Loève Transform (KLT) approach. The relaxed zero forcing condition guarantees that there is no transmission error under a noise free environment. Secondly, the linear transceiver is optimally designed via the orthogonal Procrustes approach. In particular, the transmission power gain is minimised subject to a specification on the ratio of the signal gain to the noise gain as well as to the relaxed zero forcing condition. Computer numerical simulation results show that our proposed optimal joint design of the orthonormal real valued short time block code and the linear transceiver can significantly improve the performances of MIMO wireless digital communication systems.

Advanced Information of Parity Bits for Decoding Short Linear Block Codes Using the A* Algorithm

The A* algorithm for decoding linear block codes is known to be an efficient tree-search method for decoding short linear block codes. We propose to employ the early acquired information of some parity bits to speed up the tree search. Analysis for the number of required information bits for obtaining the parity bits is provided. In addition, two versions of the modified A* algorithm which take advantage of the advanced information of parity bits are provided. Simulation results show that there is advantage in reducing the number of search tree edges in each proposed version as compared with two existing A* algorithms which do not consider the early information of parity bits.

Novel serial code concatenation strategies for error floor mitigation of low-density parity-check and turbo product codes

This paper presents a novel multiple serial code concatenation (SCC) strategy to combat the error-floor problem in iterated sparse graph-based error correcting codes such as turbo product-codes (TPC) and low-density parity-check (LDPC) codes. Although SCC has been widely used in the past to reduce the error-floor in iterative decoders, the main stumbling block for its practical application in high-speed communication systems has been the need for long and complex outer codes. Alternative, short outer block codes with interleaving have been shown to provide a good tradeoff between complexity and performance. Nevertheless, their application to next-generation high-speed communication systems is still a major challenge as a result of the careful design of long complex interleavers needed to meet the requirements of these applications. The SCC scheme proposed in this work is based on the use of short outer block codes. Departing from techniques used in previous proposals, the long outer code and interleaver are replaced by a simple block code combined with a novel encoding/decoding strategy. This allows the proposed SCC to provide a better tradeoff between performance and complexity than previous techniques. Several application examples showing the benefits of the proposed SCC are described. Particularly, a new coding scheme suitable for high-speed optical communication is introduced.