PEG algorithm based interleavers design for systematic IRA codes

In order to meet the reliability requirements of 6G mobile communication, this paper proposes a method of constructing time-invariant spatially coupled low-density parity-check (SC-LDPC) codes based on the progressive edge growth (PEG) algorithm. The base matrix for coupling is constructed by the PEG algorithm. We design a new coupling algorithm by constraining the coupling edges, and use circular permutation matrix (CPM) algorithm to eliminate 4-cycles and reduce the number of 6-cycles. Compared to 5G NR LDPC codes, simulation results show the design has obvious coding gain over AWGN channel.

It is known that the error rate can be used as a measure of reliability and security over the wire-tap channel when practical, finite length codes are used for transmission, and the security gap is an error rate based metric able to jointly treat these two aspects. In this paper, we consider several low-density parity-check (LDPC) coded transmissions, which represent the state of the art for transmissions over the wire-tap channel and we assess and compare their security gap performance. We consider two kinds of wire-tap channels: the flat and the fast fading wire-tap channels with additive white Gaussian noise. As a reference, we use the progressive edge growth (PEG) algorithm for the design of unstructured LDPC codes and compare its performance with that of four approaches for designing structured LDPC codes. We analyze both systematic and non-systematic transmissions and show that some structured code design techniques are able to achieve comparable or even better performance than the PEG algorithm over the considered channels, while taking advantage of their simpler encoding and decoding procedures.

Channel coding is an important building block in communication systems since it ensures the quality of service. Irregular repeat-accumulate (IRA) codes belong to the class of low-density parity-check (LDPC) codes and even outperform the recently introduced turbo-codes of current communication standards. The advantage of IRA codes over LDPC codes is that they come with a linear-time encoding complexity. IRA codes can be represented by a Tanner graph with arbitrary connections between nodes of given degrees. The implementation complexity of IRA decoders is dominated by the randomness of these connections. In this paper, we present a scalable partly parallel IRA decoder architecture. We present a joint graph-decoder design to parallelize IRA codes which can be efficiently processed by this decoder without any RAM access conflicts. We show design examples of these IRA codes which outperform the UMTS turbo-code by 0.2 dB.

For a given base graph, the lifted graph can be obtained by a copy-and-permute procedure. If the permutation is cyclic, the lifted graph corresponds to a quasi-cyclic (QC) protograph low-density parity-check (LDPC) code. The girth of the QC protograph LDPC code is determined by the girth of the base graph and the permutation shifts. In this paper, we first derive a lower bound on the lifting degree to achieve a large girth lifted graph. Then, motivated by the cycle searching and girth maximizing features of the progressive edge-growth (PEG) algorithm, we introduce the permutation shifts determining (PSD) PEG algorithm, which can construct large girth base graph and determine the optimal permutation shifts, simultaneously. It is shown that the computational complexity of PSD-PEG algorithm is much lower than that of the PEG algorithm and the PEG-QC algorithm for the same codeword length. Furthermore, we show that the PSD-PEG algorithm can also be used to construct nonbinary QC protograph LDPC codes without low weight codes. Simulation results show that the binary and nonbinary QC protograph LDPC codes constructed by the PSD-PEG algorithm have good bit error rate performance and frame error rate performance over the additive white Gaussian noise channel.

We present an extrinsic information transfer (EXIT) chart-based design technique for irregular repeat-accumulate (IRA) codes used in 2-D magnetic recording (TDMR) turbo-equalization systems. The channel model includes Voronoi magnetic grains, 2-D intersymbol interference (2D-ISI) and additive white Gaussian noise (AWGN). The receiver uses a 2D-ISI BCJR equalizer and an IRA decoder. For one outer equalizer-decoder iteration, we propose theory and simulation-based methods for computing EXIT curves. The simulation method calculates experimental EXIT curves for the check node decoder (CND) and the combination of the variable node decoder (VND) and an equalizer. The theoretical approach recursively calculates CND and VND Gaussian mixture model parameters in order to calculate EXIT curves. We then fit the VND and CND EXIT curves to find optimized variable node degree distributions. Simulation results show that the TDMR-optimized IRA codes achieve up to a 6.2% density increase in user bits/grain (U/G) compared with IRA codes designed for AWGN channels. The theory-based code designs achieve the same or better U/G as the simulation-based designs, but require 98% less design computation time. We also derive optimized IRA codes for iterative turbo-equalization; these codes can achieve simultaneous U/G gains and SNR savings compared with AWGN-optimized codes.

In this paper we propose an innovative channel coding scheme called 'Accumulate Repeat Accumulate codes' (ARA). This class of codes can be viewed as serial turbo-like codes, or as a subclass of Low Density Parity Check (LDPC) codes, thus belief propagation can be used for iterative decoding of ARA codes on a graph. The structure of encoder for this class can be viewed as precoded Repeat Accumulate (RA) code or as precoded Irregular Repeat Accumulate (IRA) code, where simply an accumulator is chosen as a precoder. Thus ARA codes have simple, and very fast encoder structure when they representing LDPC codes. Based on density evolution for LDPC codes through some examples for ARA codes, we show that for maximum variable node degree 5 a minimum bit SNR as low as 0.08 dB from channel capacity for rate 1/2 can be achieved as the block size goes to infinity. Thus based on fixed low maximum variable node degree, its threshold outperforms not only the RA and IRA codes but also the best known LDPC codes with the dame maximum node degree. Furthermore by puncturing the accumulators any desired high rate codes close to code rate 1 can be obtained with thresholds that stay close to the channel capacity thresholds uniformly. Iterative decoding simulation results are provided. The ARA codes also have projected graph or protograph representation that allows for high speed decoder implementation.

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This paper proposes two new cycle-optimized algorithms, called the multi-step progressive edge-growth (PEG) algorithm and the multi-step approximate cycle extrinsic message degree (ACE) constrained PEG algorithm, to design an LDPC code with larger girth and better cycle-connectivity. In addition, an efficient method is developed to calculate the distances between the variable nodes (VNs) and the check nodes (CNs) in the proposed algorithms. Simulation results show that compared to the conventional cycle-optimized algorithms, the proposed algorithms can achieve both better cycle-connectivity and foreseeable lower error floors.

Turbo Codes and Tail Biting Convolutional Codes are used for channel coding in LTE, but these codes are not pertinent in 5G. For Channel coding, advanced error correction techniques are required for the 5G. Polar codes are adopted for control information and LDPC codes are required for Data information for the 5G. Various coding techniques such as LDPC codes, Quasi Cyclic (QC)-LDPC codes, Non-Binary (NB)-LDPC codes, Spatially Coupled (SC)-LDPC codes, Irregular Repeat Accumulate (IRA) codes, Polar codes, Distributed CRC Aided (DCA) codes, and Fountain codes are illustrated. For Decoding Min-Sum Algorithm and Belief Propagation are used for LDPC codes. Min-Sum, OMS, NMS, BP methods are applied in QC-LDPC codes. SC, SCL, FSCL, CASCL, BP algorithms implemented for Polar codes. Simulation of QC-LDPC codes, Polar codes, and LDPC codes are generated by using MATLAB Software. The performance of all channel coding techniques with consideration to BER and BLER is mentioned.

In pulsed Time-of-Flight (ToF) systems the pulse width establishes a tradeoff between depth resolution and range. Ideally, one would wish to emit a pulse that is as short as allowed by the hardware, while keeping the depth range arbitrarily large, being able to sense more than one return per pixel. This suggests the use of compressive sensing (CS) as sensing paradigm, in order to exploit the sparsity of the light echo, s. Unfortunately, the best CS sensing matrices are dense matrices of random nature, thus expensive to generate and store when the signal dimensionality n (desired range over desired depth resolution) is high. The computational cost of the recovery also becomes prohibitive for large n. Additionally, dense matrices lead to measurements with poor SNR if the ratio s/n is too low or, conversely, require prohibitive amplitudes of the nonzero components. Deterministically-generated low-density parity-check (LDPC) sensing matrices can leverage these problems, but perform worse than random matrices if the density is too low compared to the sparsity. In this paper we present a simple method for deterministic construction of LDPC sensing matrices that builds upon progressive edge-growth (PEG) methods and integrates adaptiveness, that is, each new LDPC code is generated using information on the sparse signal captured by the previously-generated codes. Sensing matrices constructed via our adaptive PEG (APEG) algorithm exhibit superior sparse recovery performance than their PEG counterparts. If the density is appropriately adjusted (still low), APEG-LDPC matrices outperform classical CS random matrices.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper examines near-capacity dirty-paper code designs based on source–channel coding. We first point out that the performance loss in signal-to-noise ratio (SNR) in our code designs can be broken into the sum of the packing loss from channel coding and a modulo loss, which is a function of the granular loss from source coding and the target dirty-paper coding rate (or SNR). We then examine practical designs by combining trellis-coded quantization (TCQ) with both systematic and nonsystematic irregular repeat–accumulate (IRA) codes. Like previous approaches, we exploit the extrinsic information transfer (EXIT) chart technique for capacity-approaching IRA code design; but unlike previous approaches, we emphasize the role of strong source coding to achieve as much granular gain as possible using TCQ. Instead of systematic doping, we employ two relatively shifted TCQ codebooks, where the shift is optimized (via tuning the EXIT charts) to facilitate the IRA code design. Our designs synergistically combine TCQ with IRA codes so that they work together as well as they do individually. By bringing together TCQ (the best quantizer from the source coding community) and EXIT chart-based IRA code designs (the best from the channel coding community), we are able to approach the theoretical limit of dirty-paper coding. For example, at 0.25 bit per symbol (b/s), our best code design (with 2048-state TCQ) performs only 0.630 dB away from the Shannon capacity. </para>

In this paper, we investigate the construction of time-varying convolutional low-density paritycheck (LDPC) codes derived from block LDPC codes based on improved progressive edge growth (PEG) method. Different from the conventional PEG algorithm, the parity-check matrix is initialized by inserting certain patterns. More specifically, the submatrices along the main diagonal are fixed to be the identity matrix that ensures the fast encoding feature of the LDPC convolutional codes. Second, we insert a nonzero pattern into the secondary diagonal submatrices that ensures the encoding memory length of the time-varying LDPC convolutional codes as large as possible. With this semi-random structure, we have analyzed the code performance by evaluating the number of short cycles as well as the the bound of free distance. Simulation results show that the constructed LDPC convolutional codes perform well over additive white Gaussian noise (AWGN) channels.

We consider coded multiple-input-multiple-output (MIMO) systems in block-fading channels employing iterative receivers, where the MIMO channels are scrambled by unitary matrices. We show that the unitary scrambling improves the outage performance with discrete input when the outer code rate is high, i.e., R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sub> > 0.5. We also demonstrate that the performance improvement from scrambling can be achieved by a small number of random unitary matrices. Moreover, with unitary scrambling, the signal model can be simplified, and the performance of the MIMO demodulator can be characterized by the singular values of the channel matrix. We then consider the design of irregular repeat-accumulate (IRA) codes for unitary scrambled MIMO block-fading channels. We propose a code design method by optimizing the IRA codes for extrinsic information transfer (EXIT) average with a given effective mutual information. We also propose a pessimistic code design for some system settings to improve the frame error rate performance.

In this paper, we consider the finite-length performance of a capacity-achieving sequence of irregular repeat-accumulate (IRA) code ensembles. We focus on a sequence of bit-regular ensembles with degree 3 which was shown to achieve capacity with bounded complexity [Pfister, 2005]. To characterize how fast the block length of the code must grow with respect to the truncation point of the degree distribution (i.e., maximum check degree), we compute an upper bound on the average weight enumerator. Based on this analysis, we present a particular truncation sequence that could achieve a minimum distance which grows like n/sup 1/3/ even as the gap to capacity goes to zero. We also consider the performance of these codes in the waterfall region by extending the finite-length scaling law [Amraoui] from low-density parity-check codes to IRA codes. This shows that the performance near the iterative decoding threshold is well characterized by a suitably scaled Q-function for large enough block length. Numerical results are given for the scaling parameters of this ensemble sequence and for a few other IRA codes. Unfortunately, the simulation results for the capacity-achieving sequence start to match the scaling law only for very large block lengths.

This dissertation presents a systematic exposition on finite-block-length coding theory and practice. We begin with the task of identifying the maximum achievable rates over noisy, finite-block-length constrained channels, referred to as (e, n)-capacity Cen, with e denoting target block-error probability and n the block length. We characterize how the optimum codes look like over finite-block-length constrained channels. Constructing good, short-block-length error-correcting codes defined on sparse graphs is the focus of the thesis. We propose a new, general method for constructing Tanner graphs having a large girth by progressively establishing edges or connections between symbol and check nodes in an edge-by-edge manner, called progressive edge-growth (PEG) construction. Lower bounds on the girth of PEG Tanner graphs and on the minimum distance of the resulting low-density parity-check (LDPC) codes are derived in terms of parameters of the graphs. The PEG construction attains essentially the same girth as Gallager's explicit construction for regular graphs, both of which meet or exceed an extension of the Erdos-Sachs bound. The PEG construction proves to be powerful for generating good, short-block-length binary LDPC codes. Furthermore, we show that the binary interpretation of GF(2b) codes on the cycle Tanner graph TG(2, dc), if b grows sufficiently large, can be used over the binary-input additive white Gaussian noise (AWGN) channel as good code for optimum and good code for iterative decoding. Codes on sparse graphs are often decoded iteratively by a sum-product algorithm (SPA) with low complexity. We investigate efficient digital implementations of the SPA for decoding binary LDPC codes from both the architectural and algorithmic point of view, and describe new reduced-complexity derivatives thereof. The unified treatment of decoding techniques for LDPC codes provides flexibility in selecting the appropriate design point in high-speed applications from a performance, latency, and computational complexity perspective.