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International Conference on Wireless Communications, Networking and Mobile Computing

Dynamic Schedules Based on Variable Nodes Residual for LDPC Codes

Guojun Han, Xingcheng Liu

Published Sep 1, 2009• 4 citations
Publisher

Abstract

Layered belief-propagation (LBP) with dynamic schedule for low-density parity-check (LDPC) codes using variable nodes residual is presented. The proposed schedule outperforms the conventional LBP with predetermined schedule both in terms of convergence speed and error performance. As compared with node-wised residual belief-propagation (NRBP), it has much lower extra complexity. For LDPC code constructed by PEG, simulation results show that the new schedule offers better error performance and requires much lower extra complexity.

DOI
10.1109/wicom.2009.5303121

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Layered belief-propagation (LBP) with dynamic schedule for low-density parity-check (LDPC) codes using variable nodes residual is presented. The proposed schedule outperforms the conventional LBP with predetermined schedule both in terms of convergence speed and error performance. As compared with node-wised residual belief-propagation (NRBP), it has much lower extra complexity. For LDPC code constructed by PEG, simulation results show that the new schedule offers better error performance and requires much lower extra complexity.
An efficient dynamic schedule for layered belief-propagation (LBP) decoding of low-density parity-check (LDPC) codes is presented, in which the check nodes connecting to a variable node with maximum relative message residual are chosen in each dynamic decoding iteration, and then LBP is performed for these check nodes. In this schedule we combined the features of lazy schedule and node-wise residual belief propagation (NWRBP) together while keeping the extra computational complexity very low. Simulation results show that the new schedule speeds up the convergence rate and greatly improves error performance at medium to high signal-to-noise ratio (SNR) when compared to the standard LBP decoding algorithm.
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We propose Variable-to-Check Residual Belief Propagation (VCRBP) for LDPC decoding for faster convergence, better error correcting performance and lower complexity. It is similar to Residual Belief Propagation (RBP) that was recently applied to LDPC decoding by Vila Casado et al. because it is also a dynamic scheduling belief propagation using residuals, but it is different because its residuals are computed from variable-to-check message. Simulation shows that it out-performs with only maximum 8 iterations by about 0.3 dB compared with RBP at an FER of 10-4. Also, we propose node-wise VCRBP (N-VCRBP) for reducing complexity of VCRBP. N-VCRBP also exhibits the performance very close to VCRBP but with significantly lower decoding complexity.
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The informed dynamic scheduling (IDS) strategies for the low-density parity check (LDPC) decoding have shown superior performance in error correction and convergence speed, particularly those based on reliability measures and residual belief propagation (RBP). However, the search for the most unreliable variable nodes and the residual precomputation required for each iteration of the IDS-LDPC increases the complexity of the decoding process which becomes more sequential, making it hard to exploit the parallelism of signal processing algorithms available in multicore platforms. To overcome this problem, a new, low-complexity scheduling system, called layered vicinal variable nodes scheduling (LWNS) is presented in this paper. With this LWNS, each variable node is updated by exchanging intrinsic information with all its associated control and variable nodes before moving to the next variable node updating. The proposed scheduling strategy is fixed by a preprocessing step of the parity control matrix instead of calculation of the residuals values and by computation of the most influential variable node instead the most unreliable metric. It also allows the parallel processing of independent Tanner graph subbranches identified and grouped in layers. Our simulation results show that the LWNS BP have an attractive convergence rate and better error correction performance with low complexity when compared to previous IDS decoders under the white Gaussian noise channel (AWGN).