In this paper, we present a new forced convergence decoding scheme for LDPC codes. We remove the early detected variable nodes from the Tanner graph and thus the parity matrix shrinks iteration by iteration and the decoding complexity can be reduced. When the parity check matrix shrinks to zero before the maximum iteration, we get a chance to detect the wrong deletion and, for the decodable codewords, most of such failure can be recovered by restoring the original Tanner graph and resuming the decoding in the remaining iterations. Simulations results indicate that the proposed method can reduce the decoding complexity significantly while keep the error rate performance unchanged or even better.
Low-density parity-check (LDPC)-coded modulation has attracted much attention for its outstanding performance in a bandwidth-limited channel. However, it involves relatively high decoding complexity. Owing to different convergence behaviors of variable nodes, the nodes detected early can be deleted from the Tanner graph, which reduces the size of the parity-check matrix iteration by iteration and thus the decoding complexity. In this paper, we propose a new decoding algorithm for LDPC-coded modulation with reduced complexity. In addition, by deleting the converged bits from the modulation constellation and updating the bit metrics of the demapper with the reduced constellation, more accurate estimation of the bits can be achieved. Simulation results show that, compared with conventional decoding schemes, the proposed method not only greatly reduces the overall decoding complexity for LDPC-coded modulation but also significantly improves decoding performance.
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