Iterative Decoding of LDPCC

Tom Høholdt, JØrn Justesen

Michael Lentmaier, Arvind Sridharan, Daniel J. Costello +1 more

An iterative decoding threshold analysis for terminated regular LDPC convolutional (LDPCC) codes is presented. Using density evolution techniques, the convergence behavior of an iterative belief propagation decoder is analyzed for the binary erasure channel and the AWGN channel with binary inputs. It is shown that for a terminated LDPCC code ensemble, the thresholds are better than for corresponding regular and irregular LDPC block codes.

Dmitri V. Truhachev, David G. M. Mitchell, Michael Lentmaier +1 more

In this paper, we study ensembles of connected spatially coupled low-density parity-check codes (SC-LDPCCs), i.e., ensembles described by graphs in which regular SC-LDPCC chains of various lengths serve as edges. We show that, by carefully connecting individual SC-LDPCC chains, we obtain LDPC code ensembles with improved iterative decoding thresholds compared to those of a single coupled chain, in addition to reducing the decoding complexity required to achieve a specific bit error probability. Moreover, we show that, like the component SC-LDPCC chains, the proposed constructions have a typical minimum distance that grows linearly with block length.

Ligter, Luca

Dmitri V. Truhachev, David G. M. Mitchell, Michael Lentmaier +1 more

Codes constructed from connected spatially coupled low-density parity-check code (SC-LDPCC) chains are proposed and analyzed. It is demonstrated that connecting coupled chains results in improved iterative decoding performance. The constructed protograph ensembles have better iterative decoding thresholds compared to an individual SC-LDPCC chain and require less computational complexity per bit when operating in the near-threshold region. In addition, it is shown that the proposed constructions are asymptotically good in terms of minimum distance.

David Burshtein, Gadi Miller