Link between Sum-Product and Gradient Projection Decoding of LDPC codes: an Intermediate Algorithm

Abstract-This paper investigates the connection between the classical Sum-Product (SP) decoder for Low Density Parity Check (LDPC) codes and the recently proposed Gradient Projection (GP) decoding scheme presented in [1]. A graphical model for GP is exhibited based on which we derive an intermediate algorithm which establishes a bridge between graphical based algorithms (SP and variants) and an optimization based algorithm (GP). A more practical decoding algorithm with improved performance and reduced complexity is also proposed. A complexity analysis is provided and performance are studied through Monte-Carlo simulations

A multistage scheduled decoder for short block length low-density parity-check codes

Recent advances in coding theory have uncovered the previously forgotten power of Low-Density Parity-Check (LDPC) codes. Their popularity can be related to their relatively simple iterative decoders and their potential to achieve high performance close to shannon limit. These make them an attractive candidate for error correcting application in communication systems. In this thesis, we focus our research on the iterative decoding algorithms for Low-Density Parity-Check codes and present an improved decoding algorithm. First, the graph structure of LDPC codes is studied and a graph-based search algorithm to find the shortest closed walk and shortest cycle for each node of the graph is proposed. Then, the Deterministic schedule is applied on nodes of the graph with the objective of preserving the optimality of the algorithms. Finally, Hybrid Switch-Type technique is applied on the improved algorithms to provide a desirable complexity/performance trade-off. Hybrid Technique and Deterministic schedule are combined for decoding regular and irregular LDPC codes. The performance and complexity of the decoder is studied for Sum-Product and Gallager A algorithms. The result is a flexible decoder for any available LDPC code and any combination of decoding algorithms based on the communication systems need. In this technique, we benefit the high performance of soft-decision algorithms and low complexity of hard-decision algorithms by changing the decoding rule after a few iterations. Hence, a desirable performance can be obtained with less average number of soft iterations. Moreover, all the nodes do not update messages in each iteration. As a result, the total number of computations is reduced considerabl

Reliability Ratio Based Weighted Bit-Flipping Decoding for LDPC Codes

In this contribution, a novel reliability-ratio based weighted bit-flipping(RRWBF) algorithm is proposed for decoding Low Density Parity Check (LDPC) codes. The RRWBF algorithm proposed is benchmarked against the conventional weighted bit-flipping (WBF) algorithm [1] and the improved weighted bit-flipping (IWBF) algorithm [2]. More than 1 and 2 dB coding gain was achieved at an BER of 10-5 while invoking the RRWBF algorithm in comparison to the two benchmarking schemes, when communicating over an AWGN and an uncorrelated Rayleigh channel, respectively. Furthermore, the decoding complexity of the proposed RRWBF algorithm is maintained at the same level as that of the conventional WBF algorithm

Mathematical Programming Decoding of Binary Linear Codes: Theory and Algorithms

Mathematical programming is a branch of applied mathematics and has recently been used to derive new decoding approaches, challenging established but often heuristic algorithms based on iterative message passing. Concepts from mathematical programming used in the context of decoding include linear, integer, and nonlinear programming, network flows, notions of duality as well as matroid and polyhedral theory. This survey article reviews and categorizes decoding methods based on mathematical programming approaches for binary linear codes over binary-input memoryless symmetric channels.Comment: 17 pages, submitted to the IEEE Transactions on Information Theory. Published July 201