Iterative Message-passing Decoding Research Articles

Optimizing quasi-cyclic spatially coupled LDPC codes by eliminating harmful objects

It is well known that some harmful objects in the Tanner graph of low-density parity-check (LDPC) codes have a negative impact on their error correction performance under iterative message-passing decoding. Depending on the channel and the decoding algorithm, these harmful objects are different in nature and can be stopping sets, trapping sets, absorbing sets, or pseudocodewords. Differently from LDPC block codes, the design of spatially coupled LDPC codes must take into account the semi-infinite nature of the code, while still reducing the number of harmful objects as much as possible. We propose a general procedure, based on edge spreading, enabling the design of good quasi-cyclic spatially coupled LDPC (QC-SC-LDPC) codes. These codes are derived from quasi-cyclic LDPC (QC-LDPC) block codes and contain a considerably reduced number of harmful objects with respect to the original QC-LDPC block codes. We use an efficient way of enumerating harmful objects in QC-SC-LDPCCs to obtain a fast algorithm that spans the search space of potential candidates to select those minimizing the multiplicity of the target harmful objects. We validate the effectiveness of our method via numerical simulations, showing that the newly designed codes achieve better error rate performance than codes presented in previous literature.

A new HARQ scheme for 5G systems via interleaved superposition retransmission

Within the framework of the 5G new radio (NR), we propose a new hybrid automatic repeat request (HARQ) scheme to improve the throughput performance. The difference between the proposed scheme and the conventional one lies in the first retransmission, where the erroneous coded block group is interleaved and superimposed (XORed) onto a fresh coded block group. At the receiver, an iterative message-passing decoding algorithm can be employed to recover the target erroneous code block group (CBG). Only when the superposed retransmission fails, the conventional incremental redundancy (IR) or repetition redundancy (RR) retransmission is initiated. In any case, since the first retransmission is along with but has negligible effect on the fresh CBG, it costs neither transmitted power nor bandwidth. Monte-Carlo simulation results reveal that the presented HARQ schemes can achieve throughput improvements up to 10% over block fading channels and up to 50% over fast fading channels in comparison with the original 5G CBG-level HARQ scheme but without excessively increasing the implementation complexity.

Error Floor Estimation of LDPC Decoders — A Code Independent Approach to Measuring the Harmfulness of Trapping Sets

The linear state-space model is a well-known code-independent method to estimate the contribution of a trapping set (TS) structure to the error floor of low-density parity-check (LDPC) codes. In this paper, we provide an in-depth analysis of this method by incorporating a more accurate model for the incoming messages to the TS structure that takes into account the randomness and the correlation among such messages. Based on this analysis, we demonstrate that both randomness and correlation result in the over-estimation of the failure probability of the TS. We then propose an alternate code-independent technique for the error floor estimation of iterative LDPC decoders that can accurately estimate the contribution of different TS structures in the error floor. Compared to the linear state-space model, the proposed method is not only more accurate, but also more general, in that, it is applicable to any saturating iterative message-passing decoder, symmetrically quantized or unquantized, over any memoryless binary-input output-symmetric channel. The proposed technique can be viewed as the local application of importance sampling (IS) to the message-passing algorithm over the subgraph induced by the TS in the code’s Tanner graph. In the message-passing process, to account for the effect of the rest of the Tanner graph, density evolution along with a simple correlation model is used to generate the messages coming into the TS from the rest of the Tanner graph. Extensive simulations demonstrate that the proposed technique can accurately estimate the error floor of LDPC codes over both additive white Gaussian noise (AWGN) channel and binary symmetric channel (BSC), for a variety of iterative decoding algorithms and quantization schemes.

LDPC-based Joint Source Channel Coding and Decoding Strategies for single relay cooperative communications

This paper investigates new cooperative communication strategies based on Low-Density Parity-Check codes (LDPC) with Joint Source-Channel (JSC) coding and decoding algorithms to efficiently deliver a correlated content to a destination node. The proposed strategies rely on double LDPC codes where the source and the channel coding are similar to standard LDPC operations, and can be mapped on Tanner graphs for message-passing decoding. Three coding strategies are proposed where different LDPC source and channel coding setups are applied at the source node and the relay node. For each transmission strategy, we propose a graph mapping the whole network, over which iterative message-passing decoding is applied, and where the probability of errors in the source–relay link are taken into account. We showed, based on computer simulations, that the proposed strategies can provide substantial improvements compared to equivalent rates point-to-point systems. Simulation results also showed that the system performance depends on the relay position, and that adapting the source and the relay channel coding rates accordingly can enhance the overall system performance for a time-varying network.

LDPC Code Design for Two-Layer Spatial Modulation

In Spatial Modulation (SM), the information bits are split into two streams: one is mapped to a conventional complex signal modulation, and the other is used to select the active transmit antenna. Transmission SM schemes can be implemented with a single RF chain, allowing for a low-complexity transmitterwith relatively large spectral efficient. In this paper, we are concerned with coded SM schemes. In the literature, the usual approach considers a single encoder that jointly encodes both bit streams. In this work, we propose a coded SM system in which each of the information streams is encoded by a possibly different low-density parity-check (LDPC) code. The two LDPC codes are designed after selecting the code rates according to the mutual information associated with the two corresponding subchannels, for a fixed, reference signal-to-noise ratio (SNR). A two-layer, iterative message-passing decoder is developed, where the two component decoders exchange soft information. Through Monte Carlo simulations, we show that the bit error rate of the proposed scheme is low when the SNR is greater or equal to the reference SNR, demonstrating the strength of the proposed two-layer approach.

Selection of Parity Check Equations For the Iterative Message-Passing Detection of M-Sequences

We consider the joint detection and decoding of m-sequences. The receiver has to decide whether an m-sequence is received and possibly to decode its initial state. To do so, it implements an iterative message-passing decoding algorithm that operates on a parity check matrix, built upon a number of reference parity-check equations satisfied by the m-sequence. This matrix concatenates several elementary parity check matrices which are derived from reference equations. Unlike the conventional decoding case, the detection problem imposes to consider false alarms that may occur when the decoder is only fed with noise. While absorbing sets are known to be responsible for the error floor phenomenon of iterative message-passing decoders, we show that they may have a beneficial effect on the detection performance, in that they may prevent the decoder to produce false alarms. We further compute the number of hybrid cycles of length six and eight in the Tanner graph of the decoder and use the minimization of this number as criterion to derive an algorithm for selecting the reference parity check equations. This algorithm was found to be efficient for minimizing the probability of false alarm and decreases also the probability of wrong detection in the very small SNR region. This has been achieved at the cost of a reduction of the probability of correct detection.

Reduced Complexity Node-Wise Scheduling of ADMM Decoding for LDPC Codes

Similar to the belief propagation decoder, linear programming decoding based on the alternating direction method of multipliers (ADMM) can also be seen as an iterative message-passing decoding algorithm. How to schedule messages efficiently is an important aspect since it will influence the convergence rate of iterative decoders. In this letter, we investigate the node-wise scheduling for ADMM decoders, named NS-ADMM. In particular, we propose a reduced-complexity method for the NS-ADMM decoder by avoiding Euclidean projections involved in the calculation of message residuals. Simulation results show that the proposed method converges much faster than the flooding and layered scheduling while keeping a lower complexity when compared with the NS-ADMM decoder.

On the Total Power Capacity of Regular-LDPC Codes With Iterative Message-Passing Decoders

Motivated by recently derived fundamental limits on total (transmit + decoding) power for coded communication with VLSI decoders, this paper investigates the scaling behavior of the minimum total power needed to communicate over AWGN channels as the target bit-error-probability tends to zero. We focus on regular-LDPC codes and iterative message-passing decoders. We analyze scaling behavior under two VLSI complexity models of decoding. One model abstracts power consumed in processing elements ("node model"), and another abstracts power consumed in wires which connect the processing elements ("wire model"). We prove that a coding strategy using regular-LDPC codes with Gallager-B decoding achieves order-optimal scaling of total power under the node model. However, we also prove that regular-LDPC codes and iterative message-passing decoders cannot meet existing fundamental limits on total power under the wire model. Further, if the transmit energy-per-bit is bounded, total power grows at a rate that is worse than uncoded transmission. Complementing our theoretical results, we develop detailed physical models of decoding implementations using post-layout circuit simulations. Our theoretical and numerical results show that approaching fundamental limits on total power requires increasing the complexity of both the code design and the corresponding decoding algorithm as communication distance is increased or error-probability is lowered.

Construction and Performance Analysis of BIBD-QC-LDPC Codes for Correcting Erasure-Bursts

This paper presents a novel approach for constructing structured regular QC-LDPC codes based on a special type of combinatorial designs,known as the balanced incomplete block design (BIBD).The code design approach based on the technology of block cyclic shift and dispertion (BCSD) for correcting erasure-bursts over the binary bursts erasure channel (BBEC). Furthermore, the Tanner graph of the constructed codes is free of 4cycles, hence the BCSD-QC-LDPC codes have girth at least 6. The simulation results show that the BCSD-QC-LDPC codes perform well over the BBEC channel with iterative message-passing decoding.

Generalised array low‐density parity‐check codes

In this study, using Group Permutation Low-Density Parity-Check (GP-LDPC) codes, the authors generalise the concept of array Low-Density Parity-Check (LDPC) codes from fields of prime order to those of prime power order. In fact, they consider the additive group of the finite field GF( q ), q a prime power, as the underlying group for the GP-LDPC code construction and since when q is a prime, the author's code construction method coincides with that of quasi-cyclic array LDPC codes, they call their codes, generalised array LDPC (GA-LDPC) codes. First, they prove that, like array LDPC codes, GA-LDPC codes are quasi-cyclic codes. Then, they analyse the girth of GA-LDPC codes in a way similar to that for array LDPC codes and introduce some shortened GA-LDPC codes with girths 8, 10 and 12. For many values of g , J and L , the lengths of ( J , L )-regular shortened GA-LDPC codes of girth g and rate at least 1 - J / L , constructed in this study, are smaller than the lengths of ( J , L )-regular LDPC codes of girth g and rate at least 1 - J / L , constructed in the literature. Also, simulation results show that GA-LDPC codes perform well with the iterative message-passing decoding.

On Decoding Irregular Tanner Codes With Local-Optimality Guarantees

We consider decoding of binary linear Tanner codes using message-passing iterative decoding and linear-programming (LP) decoding in memoryless binary-input output-symmetric (MBIOS) channels. We present new certificates that are based on a combinatorial characterization for the local optimality of a codeword in irregular Tanner codes with respect to any MBIOS channel. This characterization is a generalization of (Arora , Proc. ACM Symp. Theory of Computing, 2009) and (Vontobel, Proc. Inf. Theory and Appl. Workshop, 2010) and is based on a conical combination of normalized weighted subtrees in the computation trees of the Tanner graph. These subtrees may have any finite height h (even equal or greater than half of the girth of the Tanner graph). In addition, the degrees of local-code nodes in these subtrees are not restricted to two (i.e., these subtrees are not restricted to skinny trees). We prove that local optimality in this new characterization implies maximum-likelihood (ML) optimality and LP optimality, and show that a certificate can be computed efficiently. We also present a new message-passing iterative decoding algorithm, called normalized weighted min-sum (NWMS). NWMS decoding is a belief-propagation (BP) type algorithm that applies to any irregular binary Tanner code with single parity-check local codes (e.g., low-density and high-density parity-check codes). We prove that if a locally optimal codeword with respect to height parameter h exists (whereby notably h is not limited by the girth of the Tanner graph), then NWMS decoding finds this codeword in h iterations. The decoding guarantee of the NWMS decoding algorithm applies whenever there exists a locally optimal codeword. Because local optimality of a codeword implies that it is the unique ML codeword, the decoding guarantee also provides an ML certificate for this codeword. Finally, we apply the new local-optimality characterization to regular Tanner codes, and prove lower bounds on the noise thresholds of LP decoding in MBIOS channels. When the noise is below these lower bounds, the probability that LP decoding fails to decode the transmitted codeword decays doubly exponentially in the girth of the Tanner graph.

Counting in Graph Covers: A Combinatorial Characterization of the Bethe Entropy Function

We present a combinatorial characterization of the Bethe entropy function of a factor graph, such a characterization being in contrast to the original, analytical, definition of this function. We achieve this combinatorial characterization by counting valid configurations in finite graph covers of the factor graph. Analogously, we give a combinatorial characterization of the Bethe partition function, whose original definition was also of an analytical nature. As we point out, our approach has similarities to the replica method, but also stark differences. The above findings are a natural backdrop for introducing a decoder for graph-based codes that we will call symbolwise graph-cover decoding, a decoder that extends our earlier work on blockwise graph-cover decoding. Both graph-cover decoders are theoretical tools that help toward a better understanding of message-passing iterative decoding, namely blockwise graph-cover decoding links max-product (min-sum) algorithm decoding with linear programming decoding, and symbolwise graph-cover decoding links sum-product algorithm decoding with Bethe free energy function minimization at temperature one. In contrast to the Gibbs entropy function, which is a concave function, the Bethe entropy function is in general not concave everywhere. In particular, we show that every code picked from an ensemble of regular low-density parity-check codes with minimum Hamming distance growing (with high probability) linearly with the block length has a Bethe entropy function that is convex in certain regions of its domain.

Graphical Characterizations of Linear Programming Pseudocodewords for Cycle Codes

The performance of linear programming decoding is determined by the set of nonzero linear programming pseudocodewords. The minimum pseudoweight of this set of linear programming pseudocodewords is also generally accepted as a good predictor of the performance of iterative message-passing decoding. Since the linear programming decoder has a natural description based on the Tanner graph of a code, linear programming pseudocodewords can be analyzed from a graphical viewpoint. In this paper, two graphical characterizations of linear programming pseudocodewords of cycle codes are provided: one for the entire set of linear programming pseudocodewords, and one for the set of minimal linear programming pseudocodewords. The first of these characterizations is used to determine a formula for the minimum degree of a graph cover necessary to realize a linear programming pseudocodeword of a cycle code, and the second is used to prove a result concerning the asymptotic performance of the linear programming decoder when considering transmission over either the additive white Gaussian noise channel or the binary symmetric channel. Finally, a discussion contrasting these two characterizations both with each other and with Horn's characterization of bad pseudocycles is provided.

Minimum Distance and Trapping Set Analysis of Protograph-Based LDPC Convolutional Codes

Low-density parity-check (LDPC) convolutional codes have been shown to be capable of achieving capacity-approaching performance with iterative message-passing decoding. In the first part of this paper, using asymptotic methods to obtain lower bounds on the free distance to constraint length ratio, we show that several ensembles of regular and irregular LDPC convolutional codes derived from protograph-based LDPC block codes have the property that the free distance grows linearly with respect to the constraint length, i.e., the ensembles are asymptotically good. In particular, we show that the free distance to constraint length ratio of the LDPC convolutional code ensembles exceeds the minimum distance to block length ratio of the corresponding LDPC block code ensembles. A large free distance growth rate indicates that codes drawn from the ensemble should perform well at high signal-to-noise ratios under maximum-likelihood decoding. When suboptimal decoding methods are employed, there are many factors that affect the performance of a code. Recently, it has been shown that so-called trapping sets are a significant factor affecting decoding failures of LDPC codes over the additive white Gaussian noise channel with iterative message-passing decoding. In the second part of this paper, we study the trapping sets of the asymptotically good protograph-based LDPC convolutional codes considered earlier. By extending the theory presented in part one and using similar bounding techniques, we show that the size of the smallest non-empty trapping set grows linearly with the constraint length for these ensembles.

Local-Optimality Guarantees Based on Paths for Optimal Decoding

This paper presents a unified analysis framework that captures recent advances in the study of local-optimality characterizations for codes on graphs. These local-optimality characterizations are based on combinatorial structures embedded in the Tanner graph of the code. Local optimality implies both unique maximum likelihood optimality and unique linear programming (LP) decoding optimality. Also, an iterative message-passing decoding algorithm is guaranteed to find the unique locally optimal codeword if one exists. We demonstrate an instance of this proof technique by considering a definition of local optimality that is based on the simplest combinatorial structures in Tanner graphs, namely, paths of length $h$. We apply the technique of local optimality to binary Tanner codes (including any low-density parity-check code, and in particular any irregular repeat-accumulate code with both even and odd repetition factors). Inverse polynomial bounds in the code length are proved on the word error probability of LP decoding for binary Tanner codes. When the local codes are restricted to single parity-check codes, these bounds also hold for decoding by a certain iterative message-passing decoding algorithm.

A New Construction of Structured Binary Regular LDPC Codes Based on Steiner Systems with Parameter t>2

This paper presents a novel method for constructing structured regular low-density parity-check (LDPC) codes based on a special type of combinatorial designs, known as Steiner systems. This code design approach can be considered as a generalization of the well-known method which uses the point-block incidence matrix of a Steiner 2-design for the code construction. Though the given method can be applied on any Steiner system S(t, k, v), in this paper we focus only on Steiner systems with t ≥ 3. Furthermore, we show that not only a Steiner system (X,B) itself, but also its residual design with respect to an arbitrary point x ∈ X can be employed for code construction. We also present a technique for constructing binary and non-binary QC-LDPC codes from Steiner systems. The Tanner graph of the constructed codes is free of 4-cycles and hence the codes have girth at least six. Simulation results show that the so constructed codes perform well over the AWGN channel with iterative message-passing decoding.

Efficient Implementation of Linear Programming Decoding

While linear programming (LP) decoding provides more flexibility for finite-length performance analysis than iterative message-passing (IMP) decoding, it is computationally more complex to implement in its original form, due to both the large size of the relaxed LP problem and the inefficiency of using general-purpose LP solvers. This paper explores ideas for fast LP decoding of low-density parity-check (LDPC) codes. By modifying the previously reported Adaptive LP decoding scheme to allow removal of unnecessary constraints, we first prove that LP decoding can be performed by solving a number of LP problems that each contains at most one linear constraint derived from each of the parity-check constraints. By exploiting this property, we study a sparse interior-point implementation for solving this sequence of linear programs. Since the most complex part of each iteration of the interior-point algorithm is the solution of a (usually ill-conditioned) system of linear equations for finding the step direction, we propose a preconditioning algorithm to facilitate solving such systems iteratively. The proposed preconditioning algorithm is similar to the encoding procedure of LDPC codes, and we demonstrate its effectiveness via both analytical methods and computer simulation results.

Lifting the Fundamental Cone and Enumerating the Pseudocodewords of a Parity-Check Code

The performance of message-passing iterative decoding and linear programming decoding depends on the Tanner graph representation of the code. If the underlying graph contains cycles, then such algorithms could produce a noncodeword output. The study of pseudocodewords aims to explain this noncodeword output. We examine the structure of the pseudocodewords and show that there is a one-to-one correspondence between graph cover pseudocodewords and integer points in a lifted fundamental cone. This gives a simple proof that the generating function of the pseudocodewords for a general parity-check code is rational (a fact first proved by Li, Lu, and Wang (Lecture Notes in Computer Science, vol. 5557, 2009) via other methods). Our approach yields algorithms for producing this generating function and provides tools for studying the irreducible pseudocodewords. Specifically, Barvinok's algorithm and the Barvinok-Woods projection algorithm are applied, and irreducible pseudocodewords are found via a Hilbert basis for the lifted fundamental cone.

On the Dynamics of the Error Floor Behavior in (Regular) LDPC Codes

It is shown that dominant trapping sets of regular low-density parity-check (LDPC) codes, so-called absorption sets, undergo a two-phased dynamic behavior in the iterative message-passing decoding algorithm. Using a linear dynamic model for the iteration behavior of these sets, it is shown that they undergo an initial geometric growth phase which stabilizes in a final bit-flipping behavior where the algorithm reaches a fixed point. This analysis is shown to lead to very accurate numerical calculations of the error floor bit error rates down to error rates that are inaccessible by simulation. The topology of the dominant absorption sets of an example code, the IEEE 802.3an (2048,1723) regular LDPC code, are identified and tabulated using topological relationships in combination with search algorithms.

Design of irregular LDPC codes with optimized performance-complexity tradeoff

The optimal performance-complexity tradeoff for error-correcting codes at rates strictly below the Shannon limit is a central question in coding theory. This paper proposes a numerical approach for the minimization of decoding complexity for long-block-length irregular low-density parity-check (LDPC) codes. The proposed design methodology is applicable to any binary-input memoryless symmetric channel and any iterative message-passing decoding algorithm with a parallel-update schedule. A key feature of the proposed optimization method is a new complexity measure that incorporates both the number of operations required to carry out a single decoding iteration and the number of iterations required for convergence. This paper shows that the proposed complexity measure can be accurately estimated from a density-evolution and extrinsic-information transfer chart analysis of the code. A sufficient condition is presented for convexity of the complexity measure in the variable edge-degree distribution; when it is not satisfied, numerical experiments nevertheless suggest that the local minimum is unique. The results presented herein show that when the decoding complexity is constrained, the complexity-optimized codes significantly outperform threshold-optimized codes at long block lengths, within the ensemble of irregular codes.