The Manifestation of Stopping Sets and Absorbing Sets as Deviations on the Computation Trees of LDPC Codes

The error mechanisms of iterative message-passing decoders for low-density parity-check codes are studied. A tutorial review is given of the various graphical structures, including trapping sets, stopping sets, and absorbing sets that are frequently used to characterize the errors observed in simulations of iterative decoding of low-density parity-check codes. The connections between trapping sets and deviations on computation trees are explored in depth using the notion of problematic trapping sets in order to bridge the experimental and analytic approaches to these error mechanisms. A new iterative algorithm for finding low-weight problematic trapping sets is presented and shown to be capable of identifying many trapping sets that are frequently observed during iterative decoding of low-density parity-check codes on the additive white Gaussian noise channel. Finally, a new method is given for characterizing the weight of deviations that result from problematic trapping sets

New Classes of Partial Geometries and Their Associated LDPC Codes

The use of partial geometries to construct parity-check matrices for LDPC codes has resulted in the design of successful codes with a probability of error close to the Shannon capacity at bit error rates down to $10^{-15}$. Such considerations have motivated this further investigation. A new and simple construction of a type of partial geometries with quasi-cyclic structure is given and their properties are investigated. The trapping sets of the partial geometry codes were considered previously using the geometric aspects of the underlying structure to derive information on the size of allowable trapping sets. This topic is further considered here. Finally, there is a natural relationship between partial geometries and strongly regular graphs. The eigenvalues of the adjacency matrices of such graphs are well known and it is of interest to determine if any of the Tanner graphs derived from the partial geometries are good expanders for certain parameter sets, since it can be argued that codes with good geometric and expansion properties might perform well under message-passing decoding.Comment: 34 pages with single column, 6 figure

Generalized Belief Propagation to break trapping sets in LDPC codes

6 pagesInternational audienceIn this paper, we focus on the Generalized Belief Propagation (GBP) algorithm to solve trapping sets in Low-Density Parity-Check (LDPC) codes. Trapping sets are topological structures in Tanner graphs of LDPC codes that are not correctly decoded by Belief Propagation (BP), leading to exhibit an error-floor in the bit-error rate. Stemming from statistical physics of spin glasses, GBP consists in passing messages between groups of Tanner graph nodes. Provided a well-suited grouping, this algorithm proves to be a powerful decoder as it may lower harmful topological effects of the Tanner graph. We then propose to use GBP to break trapping sets and create a new decoder to outperform BP and to defeat error-floor