Author :
Sharon, Eran ; Litsyn, Simon ; Goldberger, Jacob
Abstract :
An efficient decoding schedule for low-density parity-check (LDPC) codes that outperforms the conventional approach, in terms of both complexity and performance, is presented. Conventionally, in each iteration, all symbol nodes and, subsequently, all the check nodes, send messages to their neighbors ("flooding schedule"). In contrast, in the proposed method, the updating of nodes is performed according to a serial schedule which propagates the information twice as fast. A density evolution (DE) algorithm for asymptotic analysis of the new schedule is derived, showing that, when working near the code\´s capacity, the decoder converges in approximately half the number of iterations. In addition, a concentration theorem is proved, showing that, for a randomly chosen serial schedule, code graph, and decoder input, the decoder\´s performance approaches its expected one as predicted by the DE algorithm, when the code length increases.
Keywords :
computational complexity; graph theory; iterative decoding; message passing; parity check codes; scheduling; LDPC decoding; asymptotic analysis; bipartite graph; check nodes; code graph; concentration theorem; decoding schedule; density evolution algorithm; flooding schedule; iteration; iterative decoding; low-density parity-check codes; message-passing schedule; serial schedule; symbol nodes; Algorithm design and analysis; Channel capacity; Convergence; Floods; Iterative algorithms; Iterative decoding; Jacobian matrices; Message passing; Parity check codes; Scheduling algorithm;