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Tsinghua Science & Technology

Low complexity LDPC decoder with modified Sum-Product algorithm

Chen Qian, Weilong Lei, Zhaocheng Wang

Published Feb 1, 2013• 11 citations
Publisher

Abstract

This paper describes an efficient implementation of the Sum-Product Algorithm (SPA) within a Low Density Parity Check (LDPC) code decoder, where a horizontal process correction term is used to improve the decoding performance of the Min-Sum algorithms. The correction term is implemented as a look-up table. The algorithm uses the correction term redundancy by means of a coordinate transformation to reduce the hardware complexity. Simulations and hardware tests indicate that the decoding performance is very good with the appropriate look-up table.

DOI
10.1109/tst.2013.6449408

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This paper describes an efficient implementation of the Sum-Product Algorithm (SPA) within a Low Density Parity Check (LDPC) code decoder, where a horizontal process correction term is used to improve the decoding performance of the Min-Sum algorithms. The correction term is implemented as a look-up table. The algorithm uses the correction term redundancy by means of a coordinate transformation to reduce the hardware complexity. Simulations and hardware tests indicate that the decoding performance is very good with the appropriate look-up table.
An improved decoding algorithm for low-density parity-check (LDPC) codes is presented. By taking advantages of the first-term Taylor’s series multiple 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. Besides, the parameter δ is introduced to determine suitable expansion points. The simulation result shows that the proposed method with ten expansion points when δ is set to 0.01 has almost identical performance compared with ideal SPA algorithm and outperforms both the min-sum algorithm (MS), the offset min-sum algorithm (OMS) and the normalised min-sum algorithm (NMS). The architecture of proposed method is also presented for implementation, which has reduced computational complexity and is feasible for hardware implementation.
In this paper, we propose a low-complexity, high-efficiency two-dimensional look-up table (2D LUT) for carrying out the sum-product algorithm in the decoding of low-density parity-check (LDPC) codes. Instead of employing adders for the core operation when updating check node messages, in the proposed scheme, the main term and correction factor of the core operation are successfully merged into a compact 2D LUT. Simulation results indicate that the proposed 2D LUT not only attains close-to-optimal bit error rate performance but also enjoys a low complexity advantage that is suitable for hardware implementation.
This letter proposes a new algorithm for the check node update in the decoding of low-density parity-check (LDPC) codes. The proposed algorithm is based on a new approximation formula of standard sum-product algorithm (SPA) which can reduce the approximation error of min-sum algorithm (MSA) and has almost the same performance as sum-product algorithm (SPA) under both floating precision operation and fixed-point operation. Besides, the new approximation formula can be implemented in simple structures competitive with MSA.
Low-Density Parity check (LDPC) codes offer high-performance error correction near the Shannon limit which employs large code lengths and some iterations in the decoding process. The conventional decoding algorithm of LDPC is the Log Likelihood Ratio based Belief Propagation (LLR BP) which is also known as the `Sum-Product algorithm' which gives the best decoding performance and requires the most computational complexity and implementations with increased hardware complexity. Another simpler variant of this algorithm is used which is known as `min-sum algorithm' which reduces computational complexity as well as hardware complexity but with reduced accuracy. This paper analyzes the reason min-sum algorithm is more prone to errors when compared to the sum-product algorithm, and puts forward two improved algorithms which improve the performance of the min-sum algorithm with comparable algorithmic complexity.
In this paper, an improved low-complexity sum-product decoding algorithm is presented for low-density parity-check (LDPC) codes. In the proposed algorithm, reduction in computational complexity is achieved by utilizing fast Fourier transform (FFT) with time shift in the check node process. The improvement in the decoding performance is achieved by utilizing an optimized integer constant in the variable node process. Simulation results show that the proposed algorithm achieves an overall coding gain improvement ranging from 0.04 to 0.46 dB. Moreover, when compared with the sum-product algorithm (SPA), the proposed decoding algorithm can achieve a reduction of 42%–67% of the total number of arithmetic operations required for the decoding process.
A novel efficient decoding algorithm reduced the sum-product algorithm (SPA) Complexity with LPDC code is proposed. Base on the hyperbolic tangent rule, modified the Check node update with two horizontal process, which have similar calculation, Motivated by the finding that sun- min (MS) algorithm reduce the complexity reducing the approximation error in the horizontal process, simplify the information weight small part. Compared with the exiting approximations, the proposed method is less computational complexity than SPA algorithm. Simulation results show that the author algorithm can achieve performance very close SPA.