11 research outputs found
Efficient Maximum-Likelihood Decoding of Linear Block Codes on Binary Memoryless Channels
In this work, we consider efficient maximum-likelihood decoding of linear
block codes for small-to-moderate block lengths. The presented approach is a
branch-and-bound algorithm using the cutting-plane approach of Zhang and Siegel
(IEEE Trans. Inf. Theory, 2012) for obtaining lower bounds. We have compared
our proposed algorithm to the state-of-the-art commercial integer program
solver CPLEX, and for all considered codes our approach is faster for both low
and high signal-to-noise ratios. For instance, for the benchmark (155,64)
Tanner code our algorithm is more than 11 times as fast as CPLEX for an SNR of
1.0 dB on the additive white Gaussian noise channel. By a small modification,
our algorithm can be used to calculate the minimum distance, which we have
again verified to be much faster than using the CPLEX solver.Comment: Submitted to 2014 International Symposium on Information Theory. 5
Pages. Accepte
On the Minimum/Stopping Distance of Array Low-Density Parity-Check Codes
In this work, we study the minimum/stopping distance of array low-density
parity-check (LDPC) codes. An array LDPC code is a quasi-cyclic LDPC code
specified by two integers q and m, where q is an odd prime and m <= q. In the
literature, the minimum/stopping distance of these codes (denoted by d(q,m) and
h(q,m), respectively) has been thoroughly studied for m <= 5. Both exact
results, for small values of q and m, and general (i.e., independent of q)
bounds have been established. For m=6, the best known minimum distance upper
bound, derived by Mittelholzer (IEEE Int. Symp. Inf. Theory, Jun./Jul. 2002),
is d(q,6) <= 32. In this work, we derive an improved upper bound of d(q,6) <=
20 and a new upper bound d(q,7) <= 24 by using the concept of a template
support matrix of a codeword/stopping set. The bounds are tight with high
probability in the sense that we have not been able to find codewords of
strictly lower weight for several values of q using a minimum distance
probabilistic algorithm. Finally, we provide new specific minimum/stopping
distance results for m <= 7 and low-to-moderate values of q <= 79.Comment: To appear in IEEE Trans. Inf. Theory. The material in this paper was
presented in part at the 2014 IEEE International Symposium on Information
Theory, Honolulu, HI, June/July 201
On the Minimum Distance of Array-Based Spatially-Coupled Low-Density Parity-Check Codes
An array low-density parity-check (LDPC) code is a quasi-cyclic LDPC code
specified by two integers and , where is an odd prime and . The exact minimum distance, for small and , has been calculated, and
tight upper bounds on it for have been derived. In this work, we
study the minimum distance of the spatially-coupled version of these codes. In
particular, several tight upper bounds on the optimal minimum distance for
coupling length at least two and , that are independent of and
that are valid for all values of where depends on , are
presented. Furthermore, we show by exhaustive search that by carefully
selecting the edge spreading or unwrapping procedure, the minimum distance
(when is not very large) can be significantly increased, especially for
.Comment: 5 pages. To be presented at the 2015 IEEE International Symposium on
Information Theory, June 14-19, 2015, Hong Kon
Characterization and Efficient Search of Non-Elementary Trapping Sets of LDPC Codes with Applications to Stopping Sets
In this paper, we propose a characterization for non-elementary trapping sets
(NETSs) of low-density parity-check (LDPC) codes. The characterization is based
on viewing a NETS as a hierarchy of embedded graphs starting from an ETS. The
characterization corresponds to an efficient search algorithm that under
certain conditions is exhaustive. As an application of the proposed
characterization/search, we obtain lower and upper bounds on the stopping
distance of LDPC codes.
We examine a large number of regular and irregular LDPC codes, and
demonstrate the efficiency and versatility of our technique in finding lower
and upper bounds on, and in many cases the exact value of, . Finding
, or establishing search-based lower or upper bounds, for many of the
examined codes are out of the reach of any existing algorithm