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Irish Signals and Systems Conference

New algorithm for LDPC decoding over GF(q)

Christian Spagnol

Published Jan 1, 2005• 7 citations
Publisher

Abstract

This paper present a novel decoding scheme for low density parity check codes (LDPC) over GF(q). The scheme is a variation of the sum-product algorithm. The novel algorithm has a low complexity and reduces the number of look up table accesses necessary. Moreover, the algorithm does not compute division or multiplication, making it suitable for hardware implementation. The new algorithm has been compared with existing algorithms showing that both the BER performance and the speed of convergence are identical.

DOI
10.1049/cp:20050348

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This paper present a novel decoding scheme for low density parity check codes (LDPC) over GF(q). The scheme is a variation of the sum-product algorithm. The novel algorithm has a low complexity and reduces the number of look up table accesses necessary. Moreover, the algorithm does not compute division or multiplication, making it suitable for hardware implementation. The new algorithm has been compared with existing algorithms showing that both the BER performance and the speed of convergence are identical.
This paper proposes a new sum-product algorithm to improve BER performance for low-density parity-check codes. The proposed algorithm can replace multiplications and divisions with additions and subtractions by adding the logarithmic and exponential functions. In addition, the proposed algorithm can simplify both the ln[tanh(x)] and tanh-1[exp(x)] functions by using two quantization tables which can reduce tremendous computational complexity. Moreover, the simulation results show that the proposed SP algorithm can improve maximum of 0.8 dB BER performance compared with the existing modified sumproduct algorithm.
This paper proposes new simplified sum-product (SSP) decoding algorithm to improve BER performance for low-density parity-check codes. The proposed SSP algorithm can replace multiplications and divisions with additions and subtractions without extra computations. In addition, the proposed SSP algorithm can simplify both the In[tanh(x)] and tanh-1 [exp(x)] by using two quantization tables which can reduce tremendous computational complexity. Moreover, the simulation results show that the proposed SSP algorithm can improve about of BER performance compared with the existing modified sum-product algorithms.
This paper proposes new simplified sum-product (SSP) decoding algorithm to improve BER performance for low-density parity-check codes. The proposed SSP algorithm can replace multiplications and divisions with additions and subtractions without extra computations. In addition, the proposed SSP algorithm can simplify both the ln[tanh(x)] and exp[tanh-1(x)] by using two quantization tables which can reduce tremendous computational complexity. Moreover, the simulation results show that the proposed SSP algorithm can improve about 0.3~0.8 dB of BER performance compared with the existing modified sum-product algorithm.
Low-density parity-check (LDPC) codes over finite fields GF(q) have a better performance than those of the binary low-density parity-check codes, at short and medium block lengths, but the decoder of GF(q)-LDPC has more complexity. In this paper, we present a new reduced-complexity belief propagation (BP) decoding algorithm for GF(q)-LDPC codes by analyzing the reliability of code symbols. Instead of update all the variable nodes during iteration, we just update variable nodes which are mostly to be in an error. The algorithm also shows a property to reduce the effects of oscillating on decoding. The simulations prove that we could improve performance and reduce complexity at the same time for short and medium GF(q)-LDPC codes.
A simple,yet effective decoding algorithm for LDPC(low-density parity-check) codes over GF(q) is presented.By taking advantages of the first-term Taylor's series expansion to approximate the correction term of the Jacobian logarithm used in LLR-SPA(log-likelihood ratio sum-product algorithm),we propose an algorithm which significantly simplifies the check node update computation of the optimal LLR-SPA.Compared to the offset min-sum algorithm,the proposed algorithm achieves a gain of about 0.2dB at the BER of 10-4.Moreover,unlike the offset min-sum algorithm,parameters of this algorithm are independent of the order of the Galois Filed.
In this paper, we propose low complexity implementations of sum-product algorithm (SPA) for decoding low-density parity-check (LDPC) codes. The finite precision implementations for SPA have an important tradeoff between the decoding performance and hardware complexity caused by two dominant area consuming factors. One is the memory for updated messages storage and the other is the look-up table (LUT) for implementation of nonlinear function Psi(x). The proposed two types of variable quantization schemes result in significant reductions of LUT sizes. Computer simulation results indicate that the performance losses of SPA with reduced LUT by proposed schemes are less than 0.05 dB compared to that of SPA with original LUT. Moreover, the proposed implementation offers a large reduction in the finite wordlength for storage of updated messages using a kind of compression technique without performance losses. As a result, using the proposed implementation based on variable quantization scheme and data compression technique, the novel proposed implementation for SPA decoder architecture offers significant reductions of 75% and 33% in hardware complexities of LUT and memories, respectively, without significant performance degradations over conventional uniform quantization scheme. Furthermore, since each LUT with variable (6:3) quantization scheme II has only 8 entries, its hardware implementation can be replaced with a small combination circuit instead of readonly-memory (ROM) for LUT