NB-LDPC codes, a class of codes well-known for their exceptional error correcting performance, are not yet used widely in practice due to the high complexity of decoding algorithms. In this paper, we propose a low complexity decoder for these codes by means of a novel graph expansion. We view the finite field over which the code is constructed as the quadratic extension of one of its subfields, and then expand the Tanner graph of the code into a graph over that particular field. Decoding algorithm, which is tailored for this larger graph, presents significant complexity gains while the performance loss is minimal.
The finite-length absorbing set enumerators for non-binary protograph based low-density parity-check (LDPC) ensembles are derived. An efficient method for the evaluation of the asymptotic absorbing set distributions is presented and evaluated.
This paper applies error-exponent and dispersion-style analyses to derive finite-blocklength achievability bounds for low-density parity-check (LDPC) codes over the point-to-point channel (PPC) and multiple access channel (MAC). The error-exponent analysis applies Gallager's error exponent to bound achievable symmetrical and asymmetrical rates in the MAC. The dispersion-style analysis begins with a generalization of the random coding union (RCU) bound from random code ensembles with i.i.d. codewords to random code ensembles in which codewords may be statistically dependent; this generalization is useful since the codewords of random linear codes such as LDPC codes are dependent. Application of the RCU bound yields finite-blocklength error bounds and asymptotic achievability results for both i.i.d. random codes and LDPC codes. For discrete, memoryless channels, these results show that LDPC codes achieve first- and second-order performance that is optimal for the PPC and identical to the best prior results for the MAC.
Absorbing sets are combinatorial structures in a Tanner graph that have been shown to characterize iterative decoder failure, and particularly error floor behavior, of LDPC codes. In this paper, we examine the connection between absorbing sets and the syndromes of their support vectors. Using this framework, we provide a new characterization of fully absorbing sets, which have been considered the most harmful for iterative decoders. We also show how the sets of absorbing set support vectors appear as translates of codewords in subspaces of the code. These techniques are used to derive new search methods for absorbing sets.