Variable LLR Scaling in Min-Sum Decoding for Irregular LDPC Codes

Xu, Yin; Szczeciński, Leszek; Rong, Bo; Labeau, Fabrice; He, Dazhi; Wu, Yiyan; Zhang, Wenjun

doi:10.1109/tbc.2014.2364532

Cited by 22 publications

(21 citation statements)

References 19 publications

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“…Therefore, various researches have followed in order to figure out α for the best error correcting performance or for the most efficient hardware implementation [15][16][17][18]20].…”

Section: Min-sum Algorithmsmentioning

confidence: 99%

“…Most of them have tried to multiply the check to variable node (CTV) messages by a scaling factor to compensate for overestimated belief messages in comparison to the SP algorithm, and thus, these approaches are commonly called normalized MS (NMS) algorithms [13,14]. In [15], the CTV messages are adjusted by an offset based on the number of VNs connected to the CNs, and the CTV messages are adaptively scaled based on the iteration count [16,17]. In [18], the first two smallest CTV messages are scaled by different scaling factors using density evolution to improve the decoding performance.…”

Section: Introductionmentioning

confidence: 99%

“…In [18], the first two smallest CTV messages are scaled by different scaling factors using density evolution to improve the decoding performance. Even though the existing algorithms enhance the performance of LDPC decoders, [15,16], and [18] did not take the hardware implementation cost into account, and [17] suffered from the increased average iteration count until the decoding process is completed.…”

Section: Introductionmentioning

confidence: 99%

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Simplified 2-Dimensional Scaled Min-Sum Algorithm for LDPC Decoder

Cho¹,

Lee²,

Chung³

2017

Journal of Electrical Engineering and Technology

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-Among various decoding algorithms of low-density parity-check (LDPC) codes, the minsum (MS) algorithm and its modified algorithms are widely adopted because of their computational simplicity compared to the sum-product (SP) algorithm with slight loss of decoding performance. In the MS algorithm, the magnitude of the output message from a check node (CN) processing unit is decided by either the smallest or the next smallest input message which are denoted as min1 and min2, respectively. It has been shown that multiplying a scaling factor to the output of CN message will improve the decoding performance. Further, Zhong et al. have shown that multiplying different scaling factors (called a 2-dimensional scaling) to min1 and min2 much increases the performance of the LDPC decoder. In this paper, the simplified 2-dimensional scaled (S2DS) MS algorithm is proposed. In the proposed algorithm, we figure out a pair of the most efficient scaling factors which multiplications can be replaced with combinations of addition and shift operations. Furthermore, one scaling operation is approximated by the difference between min1 and min2. The simulation results show that S2DS achieves the error correcting performance which is close to or outperforms the SP algorithm regardless of coding rates, and its computational complexity is the lowest comparing to modified versions of MS algorithms.

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“…Therefore, various researches have followed in order to figure out α for the best error correcting performance or for the most efficient hardware implementation [15][16][17][18]20].…”

Section: Min-sum Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simplified 2-Dimensional Scaled Min-Sum Algorithm for LDPC Decoder

Cho¹,

Lee²,

Chung³

2017

Journal of Electrical Engineering and Technology

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“…LLR scaling is thus equivalent to propagating messages which are elements of G 1 (X). From theoretical point of view [5], [7], [6], the scaling factor should depend on the check node degree. The rationale behind that is the relationship between the check node degree and the reliability.…”

Section: Iterative Decodingmentioning

confidence: 99%

“…The practical implementation of the method is not solved in [6], a sub-optimal approach is derived in which the scaling factor is not anymore a function of two variables but is computed as a function of the sole iteration number. Recently, a variable scaling scheme based on the generalized mutual information was proposed in [7]. The method select the scaling factors as per iteration and as per check node degree independently overcoming the multi-dimensional issue faced by previous methods.…”

Section: Introductionmentioning

confidence: 99%

Min-Sum decoding of irregular LDPC codes with adaptive scaling based on mutual information

Alberge

2016

2016 9th International Symposium on Turbo Codes and Iterative Information Processing (ISTC)

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Min-Sum decoding (MS) is an alternative to belief propagation decoding with substantially lower complexity. MS often results in an overestimation of the log likelihood ratio (LLR) in particular in the early stage of the iterative process. A linear post-processing is usually performed as a compensation. With regular low density parity check codes (LDPC), a fixed scaling of the LLR yields sufficiently good results. In contrast, adaptive strategies are mandatory with irregular codes. It is well known that the scaling factor is an increasing function of the reliability of the LLR. In most of the publications, the scaling factor is envisioned as a function of both the iteration number and the signal-to-noise ratio. It is proposed here to use the mutual information between extrinsics as a measure of the reliability of the LLR. A practical implementation is derived with reasonable complexity. Compared to the literature, the proposed method yields slightly better results in terms of BER and a significant reduction in the number of iterations.

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