82 research outputs found
An Efficient Algorithm for Finding Dominant Trapping Sets of LDPC Codes
This paper presents an efficient algorithm for finding the dominant trapping
sets of a low-density parity-check (LDPC) code. The algorithm can be used to
estimate the error floor of LDPC codes or to be part of the apparatus to design
LDPC codes with low error floors. For regular codes, the algorithm is initiated
with a set of short cycles as the input. For irregular codes, in addition to
short cycles, variable nodes with low degree and cycles with low approximate
cycle extrinsic message degree (ACE) are also used as the initial inputs. The
initial inputs are then expanded recursively to dominant trapping sets of
increasing size. At the core of the algorithm lies the analysis of the
graphical structure of dominant trapping sets and the relationship of such
structures to short cycles, low-degree variable nodes and cycles with low ACE.
The algorithm is universal in the sense that it can be used for an arbitrary
graph and that it can be tailored to find other graphical objects, such as
absorbing sets and Zyablov-Pinsker (ZP) trapping sets, known to dominate the
performance of LDPC codes in the error floor region over different channels and
for different iterative decoding algorithms. Simulation results on several LDPC
codes demonstrate the accuracy and efficiency of the proposed algorithm. In
particular, the algorithm is significantly faster than the existing search
algorithms for dominant trapping sets
Lowering the Error Floor of LDPC Codes Using Cyclic Liftings
Cyclic liftings are proposed to lower the error floor of low-density
parity-check (LDPC) codes. The liftings are designed to eliminate dominant
trapping sets of the base code by removing the short cycles which form the
trapping sets. We derive a necessary and sufficient condition for the cyclic
permutations assigned to the edges of a cycle of length in the
base graph such that the inverse image of in the lifted graph consists of
only cycles of length strictly larger than . The proposed method is
universal in the sense that it can be applied to any LDPC code over any channel
and for any iterative decoding algorithm. It also preserves important
properties of the base code such as degree distributions, encoder and decoder
structure, and in some cases, the code rate. The proposed method is applied to
both structured and random codes over the binary symmetric channel (BSC). The
error floor improves consistently by increasing the lifting degree, and the
results show significant improvements in the error floor compared to the base
code, a random code of the same degree distribution and block length, and a
random lifting of the same degree. Similar improvements are also observed when
the codes designed for the BSC are applied to the additive white Gaussian noise
(AWGN) channel
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