Iterative Encoding of Low-Density Parity-Check Codes
|
|
- Gabriella Holt
- 5 years ago
- Views:
Transcription
1 Iterative Encoding of Low-Density Parity-Check Codes David Haley, Alex Grant and John Buetefuer Institute for Telecommunications Research University of South Australia Mawson Lakes Blvd Mawson Lakes SA 5095 Australia Abstract Motivated by the potential to reuse the decoder architecture, and thus reduce circuit space, we explore the use of iterative encoding techniques which are based upon the graphical representation of the code We design codes by identifying associated encoder convergence constraints and also eliminating some well known undesirable properties for sum-product decoding such as 4-cycles In particular we show how the Jacobi method for iterative matrix inversion can be viewed as message passing and employed as the core of an iterative encoder Example constructions of both regular and irregular LDPC codes that are encodable using this method are investigated I Introduction Since the rediscovery of low-density parity-check (LDPC) codes [1] some effort has been directed into finding computationally efficient encoders This has been motivated by the fact that in general, matrix-vector multiplication has complexity O(n 2 ) for block length n Following on from [2], [3], a class of codes built from irregular cascaded graphs, was introduced in [4] A message passing algorithm for erasure decoding these codes was presented The cascaded graph structure allows these codes to be encoded using an algorithm similar to the erasure decoder In [5] it was proposed that the parity check matrix for irregular LDPC codes be constructed such that it is in approximately lower triangular form For this case an appropriate encoder architecture could exploit the fact that most of the parity bits are computable using sparse operations, leading to approximately linear time encoding complexity In fact, it is known that the parity check matrix for most LDPC codes can be manipulated into approximately triangular form, such that the coefficient of the quadratic term in the encoding complexity is made quite small Furthermore, performance optimized LDPC codes actually admit linear time encoding [6] Consider a binary systematic (n, k) code with codewords arranged as row vectors x = [x p x u ], where x u are the information bits and x p are the parity bits Likewise partition the parity check matrix, H = [H p H u ] Thus x is a codeword iff [H p H u ][x p x u ] t = 0, or equivalently H p x t p = H u x t u Defining b = H u x t u, encoding becomes This work was supported by Southern-Poro Communications and the Australian Government under ARC SPIRT C equivalent to solving H p x t p = b (1) For m m non-singular H p, we have x t p = H 1 p b In this paper we investigate iterative solution methods for (1) and the corresponding convergence criteria and constraints imposed on H p Our goal is to develop encoding techniques which converge quickly and which re-use the sum-product message passing decoder architecture described in [7] The idea behind using the code constraints to perform encoding on the graph is not new and was originally suggested by Tanner [8] The work presented here forms a link between this concept and classical iterative matrix inversion techniques, allowing the design of good codes that encode quickly By reusing the decoder architecture for encoding, both operations can be performed by the same circuit on a time switched basis Hence, by eliminating the need for a separate dedicated encoder circuit we aim to reduce the overall size of the communication system Encoding and decoding operations are represented in the usual way as message passing on bipartite graphs Variable nodes will be labelled v i and check nodes labelled c j (the nodes can also have values associated with them, and we shall re-use the symbols v i and c j for this purpose) The matrix H specifies the edges of the graph We define A(v i ) as the set of all check nodes adjacent to variable node v i, specified by column i of H Similarly, A(c j ) is specified by row j in H Variable-check messages will be denoted λ vi c j and check-variable messages λ cj v i II The Sum-Product Encoder It is well known that if H p is upper triangular then encoding (solution of (1)) may be performed in m steps by simply performing back substitution, which implies solution for each of the parity bits in a particular order Hence upper triangular matrices are of interest For any upper triangular A with elements from F 2 (the binary Galois field), A non-singular diaga = I (2) (since the diagonal elements are the eigenvalues, none of which may be 0) Let T be the set of all non-singular
2 m m matrices that may be made upper triangular using only row and column permutations Consider a binary erasure channel with the mapping M of the output: M(0) = +1, M(1) = 1, M(erasure) = 0 The message passing erasure decoder cf [4] operates as follows (all arithmetic is real) Algorithm 1 1 Initialization: Set v i {0, 1,+1} to be the received symbol corresponding to variable v i Initialize all messages to 0 2 Variable - Check: From each variable v i to each c A(v i ) send λ vi c = v i + c j A(v i)\c λ cj v i 3 Check - Variable: From each check c j to each variable v A(c j ) send λ cj v = v i A(c j)\v λ vi c j 4 Stop/Continue: If at least one λ cj v i 0 for all v i then exit, otherwise return to 2 The decoder of Algorithm 1 can be used for encoding certain types of LDPC codes as we shall now show Theorem 1 Let A T and b { 1,+1} m be given Algorithm 1 solves Ax = b in at most m iterations, without regard to the actual order of node updates Proof Without loss of generality assume A upper triangular Let x = A 1 b Construct the bipartite graph with variable nodes v i connected to checks c j according to A Also connected to each check c j is the additional variable node v j Initialize the nodes (step 1) with v i = M(b i) { 1,+1} and v i = 0 Call v i active if at least one λ cj v i 0 An active v i is correct if λ cj v i {sgn M(x i ),0} for all c j A(v i ) For any correct v i, sgn λ vi c j {sgn M(x i ),0} In the case that every node is either correct or not active, nodes can only be made correct, left correct, or left inactive at the next Step 3, since each new λ cj v i {M(x i ),0} After the first Step 3, v 1 will be correct (since the only non-zero incoming message will be M(b 1 )) Similarly, any other nodes activated will be correct Assume there is a set of k 1 correct nodes C and that every node v C is inactive It remains only to show that at least one correct node is created at the next Step 3 This is true since there will exist an integer j 1 such that v 1,,v j are correct At the next Step 3, v j+1 C will be correct since by A triangular and (2), c i A(v i ) and c j A(v i ) for j < i Likewise, v j A(c j ) and v i A(c j ) for i > j The induction requires at most m steps before every node is correct Hence, if H p T, we may perform encoding by applying Algorithm 1 to (1), initializing the variables representing x u with ±1 and those representing x p with 0 The idea of Theorem 1 is certainly not new, but we have not seen it made explicit The number of iterations required for convergence may be greatly reduced below the upper bound of m for LDPC codes as they are represented by sparse matrices It is possible to design H p T using a tiered approach, similar to that described in [5] In this construction, the parity bits for one or more tiers will be evaluated at each iteration, and therefore the total number of iterations may be set by the designer The selection of H u is always arbitrary with respect to the sum-product encodability of H III Encoding via Iterative Matrix Inversion Having reduced the encoding problem statement to one of matrix inversion, it is natural to wonder whether classical iterative matrix inversion techniques such as those described in [9] can be applied Suppose we wish to solve Ax = b Split A according to A = S T We can then write Sx = Tx + b, and try the iteration Sx k+1 = Tx k + b (3) for some initial guess x 0 In order to compute x k+1 easily, S should be easily invertible The Gauss-Seidel method chooses S triangular, so for A T, we see that the method of the previous section actually implements Gauss-Seidel (in this case simply back-substitution) The classical Jacobi method chooses S = diag(a) and converges for any initial guess provided the spectral radius of the real or complex matrix S 1 T is less than 1 We will consider the use of this method for F 2 matrices, necessitating different convergence criteria Over F 2, S invertible implies S = I and diaga = I Hence (3) becomes x k+1 = (A I)x k + b (4) Theorem 2 For arbitrary x 0, the iteration (4) yields x k = A 1 b for k k iff (A I) k = 0 Proof Let the error term at iteration k be e k = (x x k ) Subtracting x k+1 = Tx k + b from x = Tx + b gives e k+1 = Te k So e k = T k e 0, where e 0 is the error of the initialization x 0 Hence the error term vanishes for iterations k k if T k = 0 Conversely, if T k 0 for all k, the algorithm will fail to universally converge since the error will be zero only if e 0 is in the null space of T k, which cannot be guaranteed independently of the initial guess
3 Based on Theorem 2, we can in principle construct reversible LDPC codes that are iteratively encodable in k iterations using (4) by selecting H p such that (H p I) k = 0 We call such codes Jacobi encodable It is interesting to note that the codes with H p T mentioned in the last section are also Jacobi encodable v 1 µ v1 c 2 c 1 v 1 Theorem 3 Any code with upper triangular H p is Jacobi encodable over F 2 Proof Let T = H p I Hence diagt = 0 Each successive power of T will therefore be upper triangular, with its first non-zero entry of each row occurring at least one place later Thus T k = 0 for some k We may view the Jacobi iteration as message passing on a bipartite graph formed as follows Let variable node v i correspond to x i and let nodes v j correspond to b j The v i are connected to checks c j according to A and the v j are connected to c j This is the same connection structure as required for sum-product decoding The Jacobi message passing schedule, for a binary mapping: M(0) = +1, M(1) = 1 is defined as follows Algorithm 2 1 Initialization: Set all v i = +1 and v j = b j 2 Variable - Check: Send µ vi c = v i to all c A(v i )\c i 3 Check - Variable: Each check c j sends µ cj v j = v i A(c j)\v j µ vi c j to v j only Let v i = µ ci v i Return to step 2 An example of how this algorithm operates on the graph is shown in Fig 1 During each iteration variables may be updated in parallel For clarity Fig 1 shows only those messages used to update v 2 We note that Algorithm 2 has a strong resemblance to the sum-product decoder In fact, the update process for µ cj v j in the Jacobi method is identical to that used in the update λ cj v j in the sum-product case, so the decoder architecture may be re-used It is also worth noting that only one operation per node needs to be performed in each step of the Jacobi method, compared to one per connected edge for each of the nodes in the sum-product case Therefore the Jacobi encoder implementation offers the potential for reduced power consumption IV Reversible LDPC Codes In this section we demonstrate the use of the F 2 Jacobi convergence rule, to design codes which are iteratively encodable in two iterations of the Jacobi method We therefore seek a matrix H p with (H p I) 2 = 0 = H 2 p = I There are many rules that can be applied to build a matrix H p of this form Here we build an example code using some of the simplest We may begin with any matrix A for which A 2 = I, for example A = I, and grow v 2 v 3 v 4 µ v4 c 2 µ c2 v 2 µ v 2 c 2 c 2 c 3 c 4 Fig 1 Jacobi algorithm as message passing it to the desired size of H p by recursively applying either of the following two rules [ ] [ ] A I A 0 B = B = 0 A 0 A In both cases if A 2 = I then B 2 = I The first method provides some flexibility in growing the column and row density distribution of A whereas the second method allows us to expand A without altering the distribution Neither method introduces new cycles of length 4 into the graph of H We complete H for a rate 1/2 code, building H u by adding randomly generated columns of weight 4 to the right hand side of H p and rejecting columns that would introduce a 4-cycle We have constructed an n = 512 code using this method and observed that its BER performance compares well to that of a regular code However, this simple code contains some single weight columns which are undesirable for sum-product decoding For such columns, only a single edge connects the variable node to the remainder of the graph If the variable becomes corrupted then it will always pass the corrupted message value along this edge, thus connected checks may not be satisfied, preventing their use as a stopping criteria The codes constructed in the following sections do not contain any single weight columns V Regular Construction In this section we allow four encoder iterations and build (3,6)-regular codes, constructing H p as an m m v 2 v 3 v 4
4 circulant matrix The first row of a circulant matrix is specified by the polynomial c(x), where the coefficients of x j 1 represent the j th column entry The i th row of the matrix is then specified by the polynomial p(x) = x i 1 c(x) mod (x m + 1) Theorem 4 If C is a binary m m circulant matrix, where m = 2 q for q > 2, built from cyclic rotations of the first row polynomial c(x) = 1+x+x 2q 2 +1, then C is an invertible (3,3)-regular matrix, satisfying the condition (C I) 4 = 0 Proof Given that the weight of c(x) is 3 and the transpose of a circulant matrix is also circulant it follows that C is (3,3)-regular Without loss of generality, we note that the statement (C I) 4 = 0 is equivalent to C 4 = I and that if this holds then C must be invertible The algebra of C over F 2 is isomorphic to the algebra of polynomials modulo x m + 1 having coefficients from F 2 [10] It therefore remains only to show that c 4 (x) 1 modulo x m + 1 c 4 (x) = x m+4 + x = 1 mod (x m + 1) An example circulant matrix for m = 8, which satisfies Theorem 4, having the first row polynomial c(x) = 1 + x + x 3 follows C = Matrices built using the above method are also 4-cycle free, the proof being omitted here to preserve space We complete H by randomly building a (3,3) distributed H u, whilst blocking the introduction of 4-cycles In Fig 2 the performance of two reversible codes constructed using the above technique is shown to compare well with that of randomly generated (3,6)-regular codes (from [11]) These experiments were performed over an AWGN channel, using a sum-product decoder with a maximum of 1000 iterations VI Irregular Construction In this section we investigate the construction of irregular reversible LDPC codes, again allowing four encoder iterations BER Uncoded BPSK n=1008 random n=1024 reversible n=4000 random n=4096 reversible E b /N 0 (db) Fig 2 Random and reversible regular LDPC codes To build H p we start with a 4-cycle free seed matrix A which has the property (A I) 4 = A = We then grow it to the desired size of H p by recursively applying either of the following two rules, where kron represents the matrix Kronecker product [ ] I (A I) B = kron(a,i) B = (A I) I In both cases (B I) 4 = 0 and the column and row density distribution of B is equal to that of A Neither method introduces new cycles of length 4 Richardson et al [12], [13] have shown how density evolution can be used to compute the capacity of a given ensemble of randomly constructed LDPC codes They define the threshold as the maximum level of noise such that the probability of error tends to zero as both the block length and number of decoder iterations tends to infinity Chung et al [14] have since presented a less complex Gaussian approximation algorithm for determining the threshold over AWGN channels and sum-product decoding Using these algorithms the authors provide optimized irregular distribution sequences for good irregular codes We note that both algorithms are based upon random LDPC constructions, and depend upon the local tree assumption that the girth of the graph will be large enough to sustain cycle free local subgraphs during decoding [14] Here H p is structured and we are interested in observing the effect that this has on the decoder performance
5 BER Uncoded BPSK n=1000 random n=1008 reversible E b /N 0 (db) Fig 3 Random and reversible irregular LDPC codes The matrix H p created above has equal column and row density distributions λ(x) = ρ(x) = 06667x x 2 using the notation from [13], with respect to edges We build H u randomly for maximum column weight λ max = 9 so that the overall distribution of H is close to that for the density evolution optimized code (5) from [13], which has a noise threshold σ = (Eb /N 0 = 04090dB) λ(x) = x x x 8 ρ(x) = x x x 7 (5) The noise threshold for the designed distribution (6) of H, evaluated using Chung s Gaussian approximation calculator [15] is σ = (E b /N 0 = 05260dB) λ(x) = x x x 8 ρ(x) = x x 7 (6) The performance of the optimized n = 1008 reversible code, using a sum-product decoder for 1000 iterations over an AWGN channel, is compared to that of the optimized n = 1000 random code from [13] in Fig 3 We see that it compares well until around E b /N 0 = 18dB A possible explanation for the divergence after this point is the fact that the seed matrix, although 4-cycle free, contains several cycles of length 6 The methods used to grow the seed above also grow the number of 6-cycles As a result, this particular structure of H p violates the local tree assumption in many instances illustrated how codes may be designed to encode within a guaranteed number of iterations We have drawn a link between iterative encoding/decoding and classical iterative matrix inversion techniques The Jacobi method was proposed as an iterative encoding algorithm with very low complexity Examples of both regular and irregular reversible codes were constructed and their performance analyzed The example regular reversible LDPC codes compare well to those constructed randomly, while it appears that there is still work to be done in building optimized irregular reversible structures The efficient re-use of circuit space and potential for reduced power consumption presented by the low complexity Jacobi encoder is of obvious practical relevance References [1] R G Gallager, Low-density parity-check codes MIT Press, 1963 [2] M Sipser and D A Spielman, Expander codes, IEEE Trans Inform Theory, vol 42, pp , Nov 1996 [3] D A Spielman, Linear-time encodable and decodable errorcorrecting codes, IEEE Trans Inform Theory, vol 42, pp , Nov 1996 [4] M J Luby, M Mitzenmacher, M A Shokrollahi, D A Spielman, and V Stemann, Practical loss-resilient codes, in Proc, 29th Symp Theory Computing, pp , Aug 1997 [5] D J C MacKay, S T Wilson, and M C Davey, Comparison of construction of irregular Gallager codes IEEE Trans Commun, vol 47, pp , Oct 1999 [6] T J Richardson and R L Urbanke, Efficient encoding of low-density parity-check codes, IEEE Trans Inform Theory, vol 47, pp , Feb 2001 [7] F R Kschischang, B J Frey, and H-A Loeliger, Factor graphs and the sum-product algorithm, IEEE Trans Inform Theory, vol 47, pp , Feb 2001 [8] R M Tanner, A recursive approach to low complexity codes, IEEE Trans Inform Theory, vol 27, pp , Sep 1981 [9] G Strang, Linear algebra and its applications Saunders College Publishing, 3 ed, 1988 [10] M Karlin, New binary coding results by circulants, IEEE Trans Inform Theory, vol 15, pp 81 92, 1969 [11] D J C MacKay, Encyclopedia of sparse graph codes, http: //wolraphycamacuk/mackay/codes/ [12] T J Richardson and R L Urbanke, The capacity of lowdensity parity-check codes under message passing decoding, IEEE Trans Inform Theory, vol 47, pp , Feb 2001 [13] T J Richardson and R L Urbanke, Design of capacityapproaching irregular low-density parity-check codes, IEEE Trans Inform Theory, vol 47, pp , Feb 2001 [14] S-Y Chung, T J Richardson and R L Urbanke, Analysis of sum-product decoding of low-density parity-check codes using a Gaussian approximation, IEEE Trans Inform Theory, vol 47, pp , Feb 2001 [15] S-Y Chung, Threshold calculation using a Gaussian approximation, VII Conclusions We have presented practical algorithms for iterative encoding of LDPC codes which make use of the architecture in place for a sum-product decoder In each case we have
Constructions of Nonbinary Quasi-Cyclic LDPC Codes: A Finite Field Approach
Constructions of Nonbinary Quasi-Cyclic LDPC Codes: A Finite Field Approach Shu Lin, Shumei Song, Lan Lan, Lingqi Zeng and Ying Y Tai Department of Electrical & Computer Engineering University of California,
More informationConstruction of low complexity Array based Quasi Cyclic Low density parity check (QC-LDPC) codes with low error floor
Construction of low complexity Array based Quasi Cyclic Low density parity check (QC-LDPC) codes with low error floor Pravin Salunkhe, Prof D.P Rathod Department of Electrical Engineering, Veermata Jijabai
More informationPerformance Analysis and Code Optimization of Low Density Parity-Check Codes on Rayleigh Fading Channels
Performance Analysis and Code Optimization of Low Density Parity-Check Codes on Rayleigh Fading Channels Jilei Hou, Paul H. Siegel and Laurence B. Milstein Department of Electrical and Computer Engineering
More informationStructured Low-Density Parity-Check Codes: Algebraic Constructions
Structured Low-Density Parity-Check Codes: Algebraic Constructions Shu Lin Department of Electrical and Computer Engineering University of California, Davis Davis, California 95616 Email:shulin@ece.ucdavis.edu
More informationAn Introduction to Low Density Parity Check (LDPC) Codes
An Introduction to Low Density Parity Check (LDPC) Codes Jian Sun jian@csee.wvu.edu Wireless Communication Research Laboratory Lane Dept. of Comp. Sci. and Elec. Engr. West Virginia University June 3,
More informationConstruction of LDPC codes
Construction of LDPC codes Telecommunications Laboratory Alex Balatsoukas-Stimming Technical University of Crete July 1, 2009 Telecommunications Laboratory (TUC) Construction of LDPC codes July 1, 2009
More informationLDPC Codes. Intracom Telecom, Peania
LDPC Codes Alexios Balatsoukas-Stimming and Athanasios P. Liavas Technical University of Crete Dept. of Electronic and Computer Engineering Telecommunications Laboratory December 16, 2011 Intracom Telecom,
More informationConvergence analysis for a class of LDPC convolutional codes on the erasure channel
Convergence analysis for a class of LDPC convolutional codes on the erasure channel Sridharan, Arvind; Lentmaier, Michael; Costello Jr., Daniel J.; Zigangirov, Kamil Published in: [Host publication title
More informationLow-density parity-check codes
Low-density parity-check codes From principles to practice Dr. Steve Weller steven.weller@newcastle.edu.au School of Electrical Engineering and Computer Science The University of Newcastle, Callaghan,
More informationEE229B - Final Project. Capacity-Approaching Low-Density Parity-Check Codes
EE229B - Final Project Capacity-Approaching Low-Density Parity-Check Codes Pierre Garrigues EECS department, UC Berkeley garrigue@eecs.berkeley.edu May 13, 2005 Abstract The class of low-density parity-check
More informationCodes designed via algebraic lifts of graphs
p./40 Codes designed via algebraic lifts of graphs Clemson Mini-Conference on Discrete Mathematics Oct. 3, 2008. Christine A. Kelley Department of Mathematics University of Nebraska-Lincoln email: ckelley2@math.unl.edu
More informationIntroduction to Low-Density Parity Check Codes. Brian Kurkoski
Introduction to Low-Density Parity Check Codes Brian Kurkoski kurkoski@ice.uec.ac.jp Outline: Low Density Parity Check Codes Review block codes History Low Density Parity Check Codes Gallager s LDPC code
More informationDesign of Non-Binary Quasi-Cyclic LDPC Codes by Absorbing Set Removal
Design of Non-Binary Quasi-Cyclic LDPC Codes by Absorbing Set Removal Behzad Amiri Electrical Eng. Department University of California, Los Angeles Los Angeles, USA Email: amiri@ucla.edu Jorge Arturo Flores
More informationDecoding of LDPC codes with binary vector messages and scalable complexity
Downloaded from vbn.aau.dk on: marts 7, 019 Aalborg Universitet Decoding of LDPC codes with binary vector messages and scalable complexity Lechner, Gottfried; Land, Ingmar; Rasmussen, Lars Published in:
More informationON THE MINIMUM DISTANCE OF NON-BINARY LDPC CODES. Advisor: Iryna Andriyanova Professor: R.. udiger Urbanke
ON THE MINIMUM DISTANCE OF NON-BINARY LDPC CODES RETHNAKARAN PULIKKOONATTU ABSTRACT. Minimum distance is an important parameter of a linear error correcting code. For improved performance of binary Low
More informationOptimal Rate and Maximum Erasure Probability LDPC Codes in Binary Erasure Channel
Optimal Rate and Maximum Erasure Probability LDPC Codes in Binary Erasure Channel H. Tavakoli Electrical Engineering Department K.N. Toosi University of Technology, Tehran, Iran tavakoli@ee.kntu.ac.ir
More informationMessage-Passing Decoding for Low-Density Parity-Check Codes Harish Jethanandani and R. Aravind, IIT Madras
Message-Passing Decoding for Low-Density Parity-Check Codes Harish Jethanandani and R. Aravind, IIT Madras e-mail: hari_jethanandani@yahoo.com Abstract Low-density parity-check (LDPC) codes are discussed
More informationQuasi-cyclic Low Density Parity Check codes with high girth
Quasi-cyclic Low Density Parity Check codes with high girth, a work with Marta Rossi, Richard Bresnan, Massimilliano Sala Summer Doctoral School 2009 Groebner bases, Geometric codes and Order Domains Dept
More informationConstruction of Protographs for QC LDPC Codes With Girth Larger Than 12 1
Construction of Protographs for QC LDPC Codes With Girth Larger Than 12 1 Sunghwan Kim, Jong-Seon No School of Electrical Eng. & Com. Sci. Seoul National University, Seoul, Korea Email: {nodoubt, jsno}@snu.ac.kr
More informationTime-invariant LDPC convolutional codes
Time-invariant LDPC convolutional codes Dimitris Achlioptas, Hamed Hassani, Wei Liu, and Rüdiger Urbanke Department of Computer Science, UC Santa Cruz, USA Email: achlioptas@csucscedu Department of Computer
More informationPractical Polar Code Construction Using Generalised Generator Matrices
Practical Polar Code Construction Using Generalised Generator Matrices Berksan Serbetci and Ali E. Pusane Department of Electrical and Electronics Engineering Bogazici University Istanbul, Turkey E-mail:
More informationLecture 4 : Introduction to Low-density Parity-check Codes
Lecture 4 : Introduction to Low-density Parity-check Codes LDPC codes are a class of linear block codes with implementable decoders, which provide near-capacity performance. History: 1. LDPC codes were
More informationOn Generalized EXIT Charts of LDPC Code Ensembles over Binary-Input Output-Symmetric Memoryless Channels
2012 IEEE International Symposium on Information Theory Proceedings On Generalied EXIT Charts of LDPC Code Ensembles over Binary-Input Output-Symmetric Memoryless Channels H Mamani 1, H Saeedi 1, A Eslami
More informationCodes on Graphs. Telecommunications Laboratory. Alex Balatsoukas-Stimming. Technical University of Crete. November 27th, 2008
Codes on Graphs Telecommunications Laboratory Alex Balatsoukas-Stimming Technical University of Crete November 27th, 2008 Telecommunications Laboratory (TUC) Codes on Graphs November 27th, 2008 1 / 31
More informationAnalysis of Sum-Product Decoding of Low-Density Parity-Check Codes Using a Gaussian Approximation
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 47, NO. 2, FEBRUARY 2001 657 Analysis of Sum-Product Decoding of Low-Density Parity-Check Codes Using a Gaussian Approximation Sae-Young Chung, Member, IEEE,
More informationECEN 655: Advanced Channel Coding
ECEN 655: Advanced Channel Coding Course Introduction Henry D. Pfister Department of Electrical and Computer Engineering Texas A&M University ECEN 655: Advanced Channel Coding 1 / 19 Outline 1 History
More informationLDPC Codes. Slides originally from I. Land p.1
Slides originally from I. Land p.1 LDPC Codes Definition of LDPC Codes Factor Graphs to use in decoding Decoding for binary erasure channels EXIT charts Soft-Output Decoding Turbo principle applied to
More informationBifurcations in iterative decoding and root locus plots
Published in IET Control Theory and Applications Received on 12th March 2008 Revised on 26th August 2008 ISSN 1751-8644 Bifurcations in iterative decoding and root locus plots C.M. Kellett S.R. Weller
More informationQuasi-Cyclic Asymptotically Regular LDPC Codes
2010 IEEE Information Theory Workshop - ITW 2010 Dublin Quasi-Cyclic Asymptotically Regular LDPC Codes David G. M. Mitchell, Roxana Smarandache, Michael Lentmaier, and Daniel J. Costello, Jr. Dept. of
More informationOn the minimum distance of LDPC codes based on repetition codes and permutation matrices 1
Fifteenth International Workshop on Algebraic and Combinatorial Coding Theory June 18-24, 216, Albena, Bulgaria pp. 168 173 On the minimum distance of LDPC codes based on repetition codes and permutation
More informationLDPC codes based on Steiner quadruple systems and permutation matrices
Fourteenth International Workshop on Algebraic and Combinatorial Coding Theory September 7 13, 2014, Svetlogorsk (Kaliningrad region), Russia pp. 175 180 LDPC codes based on Steiner quadruple systems and
More informationAsynchronous Decoding of LDPC Codes over BEC
Decoding of LDPC Codes over BEC Saeid Haghighatshoar, Amin Karbasi, Amir Hesam Salavati Department of Telecommunication Systems, Technische Universität Berlin, Germany, School of Engineering and Applied
More informationOn the Block Error Probability of LP Decoding of LDPC Codes
On the Block Error Probability of LP Decoding of LDPC Codes Ralf Koetter CSL and Dept. of ECE University of Illinois at Urbana-Champaign Urbana, IL 680, USA koetter@uiuc.edu Pascal O. Vontobel Dept. of
More informationMaking Error Correcting Codes Work for Flash Memory
Making Error Correcting Codes Work for Flash Memory Part I: Primer on ECC, basics of BCH and LDPC codes Lara Dolecek Laboratory for Robust Information Systems (LORIS) Center on Development of Emerging
More informationRECURSIVE CONSTRUCTION OF (J, L) QC LDPC CODES WITH GIRTH 6. Communicated by Dianhua Wu. 1. Introduction
Transactions on Combinatorics ISSN (print: 2251-8657, ISSN (on-line: 2251-8665 Vol 5 No 2 (2016, pp 11-22 c 2016 University of Isfahan wwwcombinatoricsir wwwuiacir RECURSIVE CONSTRUCTION OF (J, L QC LDPC
More informationSlepian-Wolf Code Design via Source-Channel Correspondence
Slepian-Wolf Code Design via Source-Channel Correspondence Jun Chen University of Illinois at Urbana-Champaign Urbana, IL 61801, USA Email: junchen@ifpuiucedu Dake He IBM T J Watson Research Center Yorktown
More informationIntroducing Low-Density Parity-Check Codes
Introducing Low-Density Parity-Check Codes Sarah J. Johnson School of Electrical Engineering and Computer Science The University of Newcastle Australia email: sarah.johnson@newcastle.edu.au Topic 1: Low-Density
More informationLow-Density Parity-Check codes An introduction
Low-Density Parity-Check codes An introduction c Tilo Strutz, 2010-2014,2016 June 9, 2016 Abstract Low-density parity-check codes (LDPC codes) are efficient channel coding codes that allow transmission
More informationWeaknesses of Margulis and Ramanujan Margulis Low-Density Parity-Check Codes
Electronic Notes in Theoretical Computer Science 74 (2003) URL: http://www.elsevier.nl/locate/entcs/volume74.html 8 pages Weaknesses of Margulis and Ramanujan Margulis Low-Density Parity-Check Codes David
More informationEfficient design of LDPC code using circulant matrix and eira code Seul-Ki Bae
Efficient design of LDPC code using circulant matrix and eira code Seul-Ki Bae The Graduate School Yonsei University Department of Electrical and Electronic Engineering Efficient design of LDPC code using
More informationAdaptive Cut Generation for Improved Linear Programming Decoding of Binary Linear Codes
Adaptive Cut Generation for Improved Linear Programming Decoding of Binary Linear Codes Xiaojie Zhang and Paul H. Siegel University of California, San Diego, La Jolla, CA 9093, U Email:{ericzhang, psiegel}@ucsd.edu
More informationDesign of regular (2,dc)-LDPC codes over GF(q) using their binary images
Design of regular (2,dc)-LDPC codes over GF(q) using their binary images Charly Poulliat, Marc Fossorier, David Declercq To cite this version: Charly Poulliat, Marc Fossorier, David Declercq. Design of
More informationLower Bounds on the Graphical Complexity of Finite-Length LDPC Codes
Lower Bounds on the Graphical Complexity of Finite-Length LDPC Codes Igal Sason Department of Electrical Engineering Technion - Israel Institute of Technology Haifa 32000, Israel 2009 IEEE International
More informationAn Efficient Maximum Likelihood Decoding of LDPC Codes Over the Binary Erasure Channel
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 5, NO. 11, NOVEMBER 24 1 An Efficient Maximum Likelihood Decoding of LDPC Codes Over the Binary Erasure Channel David Burshtein and Gadi Miller School of Electrical
More informationLow-density parity-check (LDPC) codes
Low-density parity-check (LDPC) codes Performance similar to turbo codes Do not require long interleaver to achieve good performance Better block error performance Error floor occurs at lower BER Decoding
More informationBounds on Achievable Rates of LDPC Codes Used Over the Binary Erasure Channel
Bounds on Achievable Rates of LDPC Codes Used Over the Binary Erasure Channel Ohad Barak, David Burshtein and Meir Feder School of Electrical Engineering Tel-Aviv University Tel-Aviv 69978, Israel Abstract
More informationAchieving Flexibility in LDPC Code Design by Absorbing Set Elimination
Achieving Flexibility in LDPC Code Design by Absorbing Set Elimination Jiajun Zhang, Jiadong Wang, Shayan Garani Srinivasa, Lara Dolecek Department of Electrical Engineering, University of California,
More informationImplementing the Belief Propagation Algorithm in MATLAB
Implementing the Belief Propagation Algorithm in MATLAB Björn S. Rüffer Christopher M. Kellett Technical Report Version as of November 13, 2008 We provide some example Matlab code as a supplement to the
More informationLow Density Parity Check (LDPC) Codes and the Need for Stronger ECC. August 2011 Ravi Motwani, Zion Kwok, Scott Nelson
Low Density Parity Check (LDPC) Codes and the Need for Stronger ECC August 2011 Ravi Motwani, Zion Kwok, Scott Nelson Agenda NAND ECC History Soft Information What is soft information How do we obtain
More informationFountain Uncorrectable Sets and Finite-Length Analysis
Fountain Uncorrectable Sets and Finite-Length Analysis Wen Ji 1, Bo-Wei Chen 2, and Yiqiang Chen 1 1 Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology, Chinese
More informationIntegrated Code Design for a Joint Source and Channel LDPC Coding Scheme
Integrated Code Design for a Joint Source and Channel LDPC Coding Scheme Hsien-Ping Lin Shu Lin and Khaled Abdel-Ghaffar Department of Electrical and Computer Engineering University of California Davis
More information5. Density evolution. Density evolution 5-1
5. Density evolution Density evolution 5-1 Probabilistic analysis of message passing algorithms variable nodes factor nodes x1 a x i x2 a(x i ; x j ; x k ) x3 b x4 consider factor graph model G = (V ;
More informationOn the Construction and Decoding of Cyclic LDPC Codes
On the Construction and Decoding of Cyclic LDPC Codes Chao Chen Joint work with Prof. Baoming Bai from Xidian University April 30, 2014 Outline 1. Introduction 2. Construction based on Idempotents and
More informationPolar Codes: Graph Representation and Duality
Polar Codes: Graph Representation and Duality arxiv:1312.0372v1 [cs.it] 2 Dec 2013 M. Fossorier ETIS ENSEA/UCP/CNRS UMR-8051 6, avenue du Ponceau, 95014, Cergy Pontoise, France Email: mfossorier@ieee.org
More informationGirth Analysis of Polynomial-Based Time-Invariant LDPC Convolutional Codes
IWSSIP 212, 11-13 April 212, Vienna, Austria ISBN 978-3-2-2328-4 Girth Analysis of Polynomial-Based Time-Invariant LDPC Convolutional Codes Hua Zhou and Norbert Goertz Institute of Telecommunications Vienna
More informationCommunications II Lecture 9: Error Correction Coding. Professor Kin K. Leung EEE and Computing Departments Imperial College London Copyright reserved
Communications II Lecture 9: Error Correction Coding Professor Kin K. Leung EEE and Computing Departments Imperial College London Copyright reserved Outline Introduction Linear block codes Decoding Hamming
More informationTurbo Codes are Low Density Parity Check Codes
Turbo Codes are Low Density Parity Check Codes David J. C. MacKay July 5, 00 Draft 0., not for distribution! (First draft written July 5, 998) Abstract Turbo codes and Gallager codes (also known as low
More informationAn Introduction to Low-Density Parity-Check Codes
An Introduction to Low-Density Parity-Check Codes Paul H. Siegel Electrical and Computer Engineering University of California, San Diego 5/ 3/ 7 Copyright 27 by Paul H. Siegel Outline Shannon s Channel
More informationCodes on graphs and iterative decoding
Codes on graphs and iterative decoding Bane Vasić Error Correction Coding Laboratory University of Arizona Prelude Information transmission 0 0 0 0 0 0 Channel Information transmission signal 0 0 threshold
More informationDecoding Codes on Graphs
Decoding Codes on Graphs 2. Probabilistic Decoding A S Madhu and Aditya Nori 1.Int roduct ion A S Madhu Aditya Nori A S Madhu and Aditya Nori are graduate students with the Department of Computer Science
More informationOn Bit Error Rate Performance of Polar Codes in Finite Regime
On Bit Error Rate Performance of Polar Codes in Finite Regime A. Eslami and H. Pishro-Nik Abstract Polar codes have been recently proposed as the first low complexity class of codes that can provably achieve
More informationECC for NAND Flash. Osso Vahabzadeh. TexasLDPC Inc. Flash Memory Summit 2017 Santa Clara, CA 1
ECC for NAND Flash Osso Vahabzadeh TexasLDPC Inc. 1 Overview Why Is Error Correction Needed in Flash Memories? Error Correction Codes Fundamentals Low-Density Parity-Check (LDPC) Codes LDPC Encoding and
More informationExtended MinSum Algorithm for Decoding LDPC Codes over GF (q)
Extended MinSum Algorithm for Decoding LDPC Codes over GF (q) David Declercq ETIS ENSEA/UCP/CNRS UMR-8051, 95014 Cergy-Pontoise, (France), declercq@ensea.fr Marc Fossorier Dept. Electrical Engineering,
More informationWhich Codes Have 4-Cycle-Free Tanner Graphs?
Which Codes Have 4-Cycle-Free Tanner Graphs? Thomas R. Halford Communication Sciences Institute University of Southern California Los Angeles, CA 90089-565 USA Alex J. Grant Institute for Telecommunications
More informationResearch Letter Design of Short, High-Rate DVB-S2-Like Semi-Regular LDPC Codes
Research Letters in Communications Volume 2008, Article ID 324503, 4 pages doi:0.55/2008/324503 Research Letter Design of Short, High-Rate DVB-S2-Like Semi-Regular LDPC Codes Luca Barletta and Arnaldo
More informationGraph-based codes for flash memory
1/28 Graph-based codes for flash memory Discrete Mathematics Seminar September 3, 2013 Katie Haymaker Joint work with Professor Christine Kelley University of Nebraska-Lincoln 2/28 Outline 1 Background
More informationChapter 6 Reed-Solomon Codes. 6.1 Finite Field Algebra 6.2 Reed-Solomon Codes 6.3 Syndrome Based Decoding 6.4 Curve-Fitting Based Decoding
Chapter 6 Reed-Solomon Codes 6. Finite Field Algebra 6. Reed-Solomon Codes 6.3 Syndrome Based Decoding 6.4 Curve-Fitting Based Decoding 6. Finite Field Algebra Nonbinary codes: message and codeword symbols
More informationFrom Stopping sets to Trapping sets
From Stopping sets to Trapping sets The Exhaustive Search Algorithm & The Suppressing Effect Chih-Chun Wang School of Electrical & Computer Engineering Purdue University Wang p. 1/21 Content Good exhaustive
More informationBounds on the Performance of Belief Propagation Decoding
Bounds on the Performance of Belief Propagation Decoding David Burshtein and Gadi Miller Dept. of Electrical Engineering Systems Tel-Aviv University Tel-Aviv 69978, Israel Email: burstyn@eng.tau.ac.il,
More informationPerformance Comparison of LDPC Codes Generated With Various Code-Construction Methods
Performance Comparison of LDPC Codes Generated With Various Code-Construction Methods Zsolt Polgar, Florin rdelean, Mihaly Varga, Vasile Bota bstract Finding good LDPC codes for high speed mobile transmissions
More informationA Class of Quantum LDPC Codes Constructed From Finite Geometries
A Class of Quantum LDPC Codes Constructed From Finite Geometries Salah A Aly Department of Computer Science, Texas A&M University College Station, TX 77843, USA Email: salah@cstamuedu arxiv:07124115v3
More informationSTUDY OF PERMUTATION MATRICES BASED LDPC CODE CONSTRUCTION
EE229B PROJECT REPORT STUDY OF PERMUTATION MATRICES BASED LDPC CODE CONSTRUCTION Zhengya Zhang SID: 16827455 zyzhang@eecs.berkeley.edu 1 MOTIVATION Permutation matrices refer to the square matrices with
More informationEnhancing Binary Images of Non-Binary LDPC Codes
Enhancing Binary Images of Non-Binary LDPC Codes Aman Bhatia, Aravind R Iyengar, and Paul H Siegel University of California, San Diego, La Jolla, CA 92093 0401, USA Email: {a1bhatia, aravind, psiegel}@ucsdedu
More informationLow-Density Parity-Check Codes
Department of Computer Sciences Applied Algorithms Lab. July 24, 2011 Outline 1 Introduction 2 Algorithms for LDPC 3 Properties 4 Iterative Learning in Crowds 5 Algorithm 6 Results 7 Conclusion PART I
More informationAn Introduction to Algorithmic Coding Theory
An Introduction to Algorithmic Coding Theory M. Amin Shokrollahi Bell Laboratories Part : Codes - A puzzle What do the following problems have in common? 2 Problem : Information Transmission MESSAGE G
More informationRandom Redundant Soft-In Soft-Out Decoding of Linear Block Codes
Random Redundant Soft-In Soft-Out Decoding of Linear Block Codes Thomas R. Halford and Keith M. Chugg Communication Sciences Institute University of Southern California Los Angeles, CA 90089-2565 Abstract
More informationWhich Codes Have 4-Cycle-Free Tanner Graphs?
Which Codes Have 4-Cycle-Free Tanner Graphs? Thomas R. Halford and Keith M. Chugg Communication Sciences Institute University of Southern California Los Angeles, CA 90089-565, USA Email: {halford, chugg}@usc.edu
More informationAnalytical Performance of One-Step Majority Logic Decoding of Regular LDPC Codes
Analytical Performance of One-Step Majority Logic Decoding of Regular LDPC Codes Rathnakumar Radhakrishnan, Sundararajan Sankaranarayanan, and Bane Vasić Department of Electrical and Computer Engineering
More informationTHE seminal paper of Gallager [1, p. 48] suggested to evaluate
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 50, NO. 11, NOVEMBER 2004 2657 Extrinsic Information Transfer Functions: Model and Erasure Channel Properties Alexei Ashikhmin, Member, IEEE, Gerhard Kramer,
More informationAn algorithm to improve the error rate performance of Accumulate-Repeat-Accumulate codes Tae-Ui Kim
An algorithm to improve the error rate performance of Accumulate-Repeat-Accumulate codes Tae-Ui Kim The Graduate School Yonsei University Department of Electrical and Electronic Engineering An algorithm
More informationQuasi-Cyclic Low-Density Parity-Check Codes With Girth Larger Than
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL 53, NO 8, AUGUST 2007 2885 n possible values If the parity check is satisfied, the error probability is closely approximated by the probability of two bit errors,
More informationRecent Results on Capacity-Achieving Codes for the Erasure Channel with Bounded Complexity
26 IEEE 24th Convention of Electrical and Electronics Engineers in Israel Recent Results on Capacity-Achieving Codes for the Erasure Channel with Bounded Complexity Igal Sason Technion Israel Institute
More informationModern Coding Theory. Daniel J. Costello, Jr School of Information Theory Northwestern University August 10, 2009
Modern Coding Theory Daniel J. Costello, Jr. Coding Research Group Department of Electrical Engineering University of Notre Dame Notre Dame, IN 46556 2009 School of Information Theory Northwestern University
More informationLecture 4: Linear Codes. Copyright G. Caire 88
Lecture 4: Linear Codes Copyright G. Caire 88 Linear codes over F q We let X = F q for some prime power q. Most important case: q =2(binary codes). Without loss of generality, we may represent the information
More informationGALLAGER S binary low-density parity-check (LDPC)
1560 IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 57, NO. 6, JUNE 2009 Group-Theoretic Analysis of Cayley-Graph-Based Cycle GF(2 p )Codes Jie Huang, Shengli Zhou, Member, IEEE, Jinkang Zhu, Senior Member,
More informationarxiv:cs/ v2 [cs.it] 1 Oct 2006
A General Computation Rule for Lossy Summaries/Messages with Examples from Equalization Junli Hu, Hans-Andrea Loeliger, Justin Dauwels, and Frank Kschischang arxiv:cs/060707v [cs.it] 1 Oct 006 Abstract
More informationAN INTRODUCTION TO LOW-DENSITY PARITY-CHECK CODES
AN INTRODUCTION TO LOW-DENSITY PARITY-CHECK CODES Item Type text; Proceedings Authors Moon, Todd K.; Gunther, Jacob H. Publisher International Foundation for Telemetering Journal International Telemetering
More informationLDPC Code Ensembles that Universally Achieve Capacity under BP Decoding: A Simple Derivation
LDPC Code Ensembles that Universally Achieve Capacity under BP Decoding: A Simple Derivation Anatoly Khina EE Systems Dept., TAU Tel Aviv, Israel Email: anatolyk@eng.tau.ac.il Yair Yona Dept. of EE, UCLA
More informationLow-Density Parity-Check Code Design Techniques to Simplify Encoding
IPN Progress Report 42-171 November 15, 27 Low-Density Parity-Check Code Design Techniques to Simplify Encoding J. M. Perez 1 and K. Andrews 2 This work describes a method for encoding low-density parity-check
More informationTrapping Set Enumerators for Specific LDPC Codes
Trapping Set Enumerators for Specific LDPC Codes Shadi Abu-Surra Samsung Telecommunications America 1301 E. Lookout Dr. Richardson TX 75082 Email: sasurra@sta.samsung.com David DeClercq ETIS ENSEA/UCP/CNRS
More informationSuccessive Cancellation Decoding of Single Parity-Check Product Codes
Successive Cancellation Decoding of Single Parity-Check Product Codes Mustafa Cemil Coşkun, Gianluigi Liva, Alexandre Graell i Amat and Michael Lentmaier Institute of Communications and Navigation, German
More informationLow-complexity error correction in LDPC codes with constituent RS codes 1
Eleventh International Workshop on Algebraic and Combinatorial Coding Theory June 16-22, 2008, Pamporovo, Bulgaria pp. 348-353 Low-complexity error correction in LDPC codes with constituent RS codes 1
More informationOn the Typicality of the Linear Code Among the LDPC Coset Code Ensemble
5 Conference on Information Sciences and Systems The Johns Hopkins University March 16 18 5 On the Typicality of the Linear Code Among the LDPC Coset Code Ensemble C.-C. Wang S.R. Kulkarni and H.V. Poor
More informationConstruction of Type-II QC LDPC Codes Based on Perfect Cyclic Difference Set
Chinese Journal of Electronics Vol24, No1, Jan 2015 Construction of Type-II QC LDPC Codes Based on Perfect Cyclic Difference Set ZHANG Lijun 1,LIBing 2 and CHENG Leelung 3 (1 School of Electronic and Information
More informationOn the minimum distance of LDPC codes based on repetition codes and permutation matrices
On the minimum distance of LDPC codes based on repetition codes and permutation matrices Fedor Ivanov Email: fii@iitp.ru Institute for Information Transmission Problems, Russian Academy of Science XV International
More informationA Class of Quantum LDPC Codes Derived from Latin Squares and Combinatorial Design
A Class of Quantum LDPC Codes Derived from Latin Squares and Combinatorial Design Salah A Aly Department of Computer Science, Texas A&M University, College Station, TX 77843-3112, USA Email: salah@cstamuedu
More informationOn the Design of Raptor Codes for Binary-Input Gaussian Channels
1 On the Design of Raptor Codes for Binary-Input Gaussian Channels Zhong Cheng, Jeff Castura, and Yongyi Mao, Member, IEEE Abstract This paper studies the problem of Raptor-code design for binary-input
More informationIEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 57, NO. 2, FEBRUARY
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL 57, NO 2, FEBRUARY 2011 835 Deriving Good LDPC Convolutional Codes from LDPC Block Codes Ali E Pusane, Member, IEEE, Roxana Smarandache, Member, IEEE, Pascal
More informationAn Efficient Algorithm for Finding Dominant Trapping Sets of LDPC Codes
An Efficient Algorithm for Finding Dominant Trapping Sets of LDPC Codes Mehdi Karimi, Student Member, IEEE and Amir H. Banihashemi, Senior Member, IEEE Abstract arxiv:1108.4478v2 [cs.it] 13 Apr 2012 This
More informationMATH3302 Coding Theory Problem Set The following ISBN was received with a smudge. What is the missing digit? x9139 9
Problem Set 1 These questions are based on the material in Section 1: Introduction to coding theory. You do not need to submit your answers to any of these questions. 1. The following ISBN was received
More information