4 research outputs found
Efficient implementation of linear programming decoding
While linear programming (LP) decoding provides more flexibility for
finite-length performance analysis than iterative message-passing (IMP)
decoding, it is computationally more complex to implement in its original form,
due to both the large size of the relaxed LP problem, and the inefficiency of
using general-purpose LP solvers. This paper explores ideas for fast LP
decoding of low-density parity-check (LDPC) codes. We first prove, by modifying
the previously reported Adaptive LP decoding scheme to allow removal of
unnecessary constraints, that LP decoding can be performed by solving a number
of LP problems that contain at most one linear constraint derived from each of
the parity-check constraints. By exploiting this property, we study a sparse
interior-point implementation for solving this sequence of linear programs.
Since the most complex part of each iteration of the interior-point algorithm
is the solution of a (usually ill-conditioned) system of linear equations for
finding the step direction, we propose a preconditioning algorithm to
facilitate iterative solution of such systems. The proposed preconditioning
algorithm is similar to the encoding procedure of LDPC codes, and we
demonstrate its effectiveness via both analytical methods and computer
simulation results.Comment: 44 pages, submitted to IEEE Transactions on Information Theory, Dec.
200