Iterative Message-passing Algorithm Research Articles

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.

Reliability Analysis of the Sum-Product Decoding Algorithm for the PSK Modulation Scheme

Iteratively decoding and reconstruction of encoded data has been considered in recent decades. Most of these iterative schemes are based on graphical codes. Messages are passed through space graphs to reach a reliable belief of the original data. This paper presents a performance analysis of the Low-Density Parity-Check (LDPC) code design method which approach the capacity of the Additive White Gaussian Noise (AWGN) model for communication channels. We investigate the reliability of the system under Phase Shift Keying (PSK) modulation. We study the effects and advantages of variation in the codeword length, the rate of parity-check matrix of the LDPC codes, and the number of iterations in the Sum-Product Algorithm (SPA). By employing an LDPC encoder prior to the PSK modulation block and the SPA in the decoding part, the Bit Error Rate (BER) performance of the PSK modulation system can improve significantly. The BER performance improvement of a point-to-point communication system is measured in different cases. Our analysis is capable for applying any other iterative message-passing algorithm. The code design process of the communication systems and parameter selection of the encoding and decoding algorithms are accomplished by considering hardware limitations in a communication system. Our results help to design and select paramours efficiently.

Binary Wyner–Ziv code design based on compound LDGM–LDPC structures

In this study, a practical coding scheme is designed for the binary Wyner–Ziv (WZ) problem by using nested low-density generator-matrix (LDGM) and low-density parity-check (LDPC) codes. This scheme contains two steps in the encoding procedure. The first step involves applying the binary quantisation by employing LDGM codes and the second one is using the syndrome-coding technique by utilising LDPC codes. The decoding algorithm of the proposed scheme is based on the sum-product algorithm with the help of a side information available at the decoder side. It is theoretically shown that the compound structure has the capability of achieving the WZ bound. The proposed method approaches this bound by utilising the iterative message-passing algorithms in both encoding and decoding, although theoretical results show that it is asymptotically achievable.

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.

Mean-field message-passing equations in the Hopfield model and its generalizations.

Motivated by recent progress in using restricted Boltzmann machines as preprocessing algorithms for deep neural network, we revisit the mean-field equations [belief-propagation and Thouless-Anderson Palmer (TAP) equations] in the best understood of such machines, namely the Hopfield model of neural networks, and we explicit how they can be used as iterative message-passing algorithms, providing a fast method to compute the local polarizations of neurons. In the "retrieval phase", where neurons polarize in the direction of one memorized pattern, we point out a major difference between the belief propagation and TAP equations: The set of belief propagation equations depends on the pattern which is retrieved, while one can use a unique set of TAP equations. This makes the latter method much better suited for applications in the learning process of restricted Boltzmann machines. In the case where the patterns memorized in the Hopfield model are not independent, but are correlated through a combinatorial structure, we show that the TAP equations have to be modified. This modification can be seen either as an alteration of the reaction term in TAP equations or, more interestingly, as the consequence of message passing on a graphical model with several hidden layers, where the number of hidden layers depends on the depth of the correlations in the memorized patterns. This layered structure is actually necessary when one deals with more general restricted Boltzmann machines.

Fast and simple decycling and dismantling of networks.

Decycling and dismantling of complex networks are underlying many important applications in network science. Recently these two closely related problems were tackled by several heuristic algorithms, simple and considerably sub-optimal, on the one hand, and involved and accurate message-passing ones that evaluate single-node marginal probabilities, on the other hand. In this paper we propose a simple and extremely fast algorithm, CoreHD, which recursively removes nodes of the highest degree from the 2-core of the network. CoreHD performs much better than all existing simple algorithms. When applied on real-world networks, it achieves equally good solutions as those obtained by the state-of-art iterative message-passing algorithms at greatly reduced computational cost, suggesting that CoreHD should be the algorithm of choice for many practical purposes.

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.

AID: An Energy Efficient Decoding Scheme for LDPC Codes in Wireless Body Area Sensor Networks

One of the major challenges in Wireless Body Area Networks (WBANs) is to prolong the lifetime of network. Tra- ditional research work focuses on minimizing transmit power; however, in the case of short range communication the consumption power in decoding is significantly larger than transmit power. This paper investigates the minimization of total power consumption by reducing the decoding power consumption. For achieving a desired Bit Error Rate (BER), we introduce some fundamental results on the basis of iterative message-passing algorithms for Low Density Parity Check Code (LDPC). To reduce energy dissipation in decoder, LDPC based coded communications between sensors are considered. Moreover, we evaluate the performance of LDPC at different code rates and introduce Adaptive Itera- tive Decoding (AID) by exploiting threshold on the number of iterations for a certain BER (10−4). In iterative LDPC decoding, the total energy consumption of network is reduced by 20 - 25%.

Performance Analysis of Iterative Decoding Algorithms with Memory over Memoryless Channels

Density evolution is often used to determine the performance of an ensemble of low-density parity-check (LDPC) codes under iterative message-passing algorithms. Conventional density evolution techniques over memoryless channels are based on the assumption that messages at iteration l are only a function of the messages at iteration l -1 and possibly the channel output. This assumption is valid for many algorithms such as standard belief propagation (BP) and min-sum (MS) algorithms. However, there are other important iterative algorithms such as successive relaxation (SR) versions of BP and MS, and differential decoding with binary message passing (DD-BMP) algorithm of Mobini et al., for which this assumption is not valid. The reason is the introduction of memory in these algorithms. In this work, we propose a model for iterative decoding algorithms with memory which covers SR and DD-BMP algorithms as special cases. Based on this model, we derive a Bayesian network for iterative algorithms with memory over memoryless channels and use this representation to analyze the performance of the algorithms using density evolution. The density evolution technique is developed based on truncating the memory of the decoding process and approximating it with a finite order Markov process, and can be implemented efficiently. As an example, we apply our technique to analyze the performance of DD-BMP on regular LDPC code ensembles, and make a number of interesting observations with regard to the performance/complexity tradeoff of DD-BMP in comparison with BP and MS algorithms. The model presented in this paper is based on certain simplifying assumptions about the memory structure of iterative algorithms such as the existence of memory only at the output of variable nodes in the code's Tanner graph rather than at both outputs of variable and check nodes. The Bayesian network framework introduced here however, can still be used to analyze the more general scenarios.

A matrix-theoretic approach for analyzing quasi-cyclic low-density parity-check codes

A matrix-theoretic approach for studying quasi-cyclic codes based on matrix transformations via Fourier transforms and row and column permutations is developed. These transformations put a parity-check matrix in the form of an array of circulant matrices into a diagonal array of matrices of the same size over an extension field. The approach is amicable to the analysis and construction of quasi-cyclic low-density parity-check codes since it takes into account the specific parity-check matrix used for decoding with iterative message-passing algorithms. Based on this approach, the dimension of the codes and parity-check matrices for the dual codes can be determined. Several algebraic and geometric constructions of quasi-cyclic codes are presented as applications along with simulation results showing their performance over additive white Gaussian noise channels decoded with iterative message-passing algorithms.

Ring Theoretic Approach to Reversible Codes Based on Circulant Matrices

Recently, Haley and Grant introduced the concept of reversible codes — a class of binary linear codes that can reuse the decoder architecture as the encoder and encodable by the iterative message-passing algorithm based on the Jacobi method over $\\mathbb{F}_2$. They also developed a procedure to construct parity check matrices of a class of reversible codes named type-I reversible codes by utilizing properties specific to circulant matrices. In this paper, we refine a mathematical framework for reversible codes based on circulant matrices through a ring theoretic approach. This approach enables us to clarify the necessary and sufficient condition on which type-I reversible codes exist. Moreover, a systematic procedure to construct all circulant matrices that constitute parity check matrices of type-I reversible codes is also presented.

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.

Bounds on the Expansion Properties of Tanner Graphs

This work focuses on the expansion properties of a Tanner Graph because they are known to be related to the performance of associated iterative message-passing algorithms over various channels. By analyzing the eigenvalues and corresponding eigenvectors of the normalized incidence matrix representing a Tanner Graph, lower bounds on these expansion properties are derived. Specifically, for the binary erasure channel, these results lead to two lower bounds on stopping distance for any given binary linear code and an upper bound on stopping redundancy for the family of difference-set codes (type-I 2-D projective geometry low-density parity-check (LDPC) codes).

Kikuchi approximation method for joint decoding of LDPC codes and partial-response channels

In this letter, we apply the Kikuchi approximation method to the problem of joint decoding of a low-density parity-check code and a partial-response channel. The Kikuchi method is, in general, more powerful than the conventional loopy belief propagation (BP) algorithm, and can produce better approximations to an underlying inference problem. We will first review the Kikuchi approximation method and the generalized BP algorithm, which is an iterative message-passing algorithm based on this method. We will then report simulation results which show that the Kikuchi method outperforms the best conventional iterative method

Decoding of low-density parity-check codes in non-Gaussian channels

This paper studies the performance of low-density parity-check codes decoded by the iterative message-passing algorithm (MPA) in heavy-tailed, non-Gaussian noise channels. Through detailed examination on the decoding procedure and observation on the decoding trajectory, impulsive noise is found to constitute a major channel impairment for the Gaussian-optimised MPA. Two main factors contributing to this non-robustness, which lead to error propagation and produce uncorrectable and undetected errors, are identified. To compensate for this shortfall, an effective countermeasure is outlined and a low-complexity robust MPA (RMPA) in both probability and log-domains is proposed. The RMPA can be implemented by appending the standard MPA with a nonlinear filter bank, thus requiring no major modifications. The nonlinear filter bank performs impulsive noise suppression and prevents the formation of overly strong priors. This gives room for performance improvements via iterative decoding. The nonlinear functions embedded in the filter bank can be stored as a lookup table or implemented efficiently using the CORDIC algorithm, which is very suitable for VLSI implementation. For severe impulsive noise, the RMPA significantly outperforms the MPA with performance gains typically exceeding 10 dB for bit error probabilities below 10−2 with moderate codeword lengths. The performance of the sign-MPA (SMPA), which imposes a hard-limiting procedure on the received codewords, is also investigated.

Unified design of iterative receivers using factor graphs

Iterative algorithms are an attractive approach to approximating optimal, but high-complexity, joint channel estimation and decoding receivers for communication systems. We present a unified approach based on factor graphs for deriving iterative message-passing receiver algorithms for channel estimation and decoding. For many common channels, it is easy to find simple graphical models that lead directly to implementable algorithms. Canonical distributions provide a new, general framework for handling continuous variables. Example receiver designs for Rayleigh fading channels with block or Markov memory, and multipath fading channels with fixed unknown coefficients illustrate the effectiveness of our approach.