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IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)

Simplified forced convergence decoding algorithm for low power LDPC decoders

Byung Jun Choi, Myung Hoon Sunwoo

Published Nov 1, 2014• 6 citations
Publisher

Abstract

This paper proposes a simplified forced-convergence (SFC) algorithm to reduce the computational complexity for low-density parity-check (LDPC) decoding. To reduce the computational complexity, the proposed algorithm uses the modified check node (CN) operation that does not use a condition for deactivating CNs. Therefore, the proposed SFC algorithm uses only one threshold value while the existing forced-convergence (FC) algorithm uses two threshold values. The simulation results show that SFC achieves a bit error rate (BER) performance close to min-sum (MS) algorithm. SFC can reduce the computational complexity of check node by approximately 22.21% compared to FC.

DOI
10.1109/apccas.2014.7032868

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This paper proposes an efficient Forced Convergence (FC) algorithm to reduce the computational complexity of LDPC decoders. To reduce the computational complexity, the proposed algorithm uses only one threshold value and two conditions of variable nodes (VNs) while the existing FC algorithm uses two threshold values and two conditions. The simulation results show that the proposed algorithm achieves the bit error rate (BER) performance close to the typical Min-Sum algorithm. however, it can significantly reduce the computational complexity compared to the existing FC algorithm.
An improvement of the forced-convergence decoding algorithm is discussed. Iterative decoding schemes such as the sum-product (SP) decoding bring out excellent performance of low-density parity check (LDPC) codes. The forced-convergence (FC) decoding is also an iterative decoding algorithm obtained by modifying the SP decoding. The FC decoding succeeds to reduce the decoding complexity of the SP decoding, but the error correcting performance is severely degraded by the modification. The performance degradation of the FC decoding is expected to be mitigated if the convergence criterion used in the algorithm is appropriately modified. This paper discusses several types of modifications of the convergence criteria. Computer simulation shows that the proposed modifications improve the performance of the FC decoding algorithm.
A modified message propagation algorithm is proposed for a low-complexity decoder of low-density parity-check (LDPC) codes, which controls the information propagated from variable and check nodes.The proposed threshold-based node deactivation for variable nodes and zero-forcing scheme for check nodes remarkably reduce decoding complexity required for similar error performance. In the proposed scheme, different thresholds, which are determined from the base matrix of the LDPC codes, are applied for each type of variable node. In addition, thresholds for deactivating variable nodes are increased while the decoding process is operated for a reduction in decoding complexity without early error floor, which is a drawback of the conventional threshold-based deactivation scheme. Simulation results show that the proposed scheme enables normalized min-sum decoders to decode successfully with less complexity than the conventional threshold-based deactivating scheme.
This paper proposes a low-complexity LDPC decoding algorithm with simplified check nodes updating. The proposed algorithm simplifies the result of the second-minimum in check nodes based on the first-minimum computation instead of computing it directly. In order to obtain the approximate result, effective corrected coefficients are utilized, which can reduce the complexity by eliminating the complex computations. Complexity analysis is provided and the results indicate that the complexity of the proposed algorithm is much lower than the general NMS algorithm and MS-based simplified algorithms. Simulation results show that the performance of the proposed algorithm can closely match the NMS algorithm with the same number of iterations.
This paper proposes the simplified sum-product (SSP) decoding algorithm to improve BER performance for low-density parity-check codes (LDPC). The sum-product algorithm achieves the highest BER performance among the LDPC decoding algorithms. However, based on the hyperbolic tangent and inverse hyperbolic tangent functions for the check node updates, the sum-product (SP) algorithm requires high computational complexity. To reduce computational complexity, the proposed SSP algorithm adopts a piecewise linear approximation, the logarithmic and exponential functions, which can replace multiplications and divisions with additions and subtractions. In addition, the proposed SSP algorithm can simplify both the ln[tanh(x)] and tanh-1[exp(x)] by using two quantization tables which can reduce tremendous computational complexity. Simulation results show that the proposed SSP algorithm can improve about 0.8 dB of bit error ratio (BER) performance compared with the modified sum-product algorithm.
A simplified decoding algorithm for low-density parity-check (LDPC) codes over high order Galois field is proposed to reduce the complexity of traditional sum-product algorithm (SPA). There are only addition operation and comparison operation in this algorithm, without complex multiplication operation. The computation complexity of proposed algorithm is much smaller than the SPA. The signal-to-noise power ratio (SNR) isn't needed in this algorithm, so the process of SNR estimation can also be removed. The simulation results show that the bit error rate (BER) performance of proposed algorithm degrades only about 0.5 dB than the SPA, so the proposed algorithm is an efficient simplified algorithm in engineering.