New Min-sum LDPC Decoding Algorithm Using SNR-Considered Adaptive Scaling Factors

Abstract

This paper proposes a new min-sum algorithm for lowdensity parity-check decoding. In this paper, we first define the negative and positive effects of the received signal-to-noise ratio (SNR) in the min-sum decoding algorithm. To improve the performance of error correction by considering the negative and positive effects of the received SNR, the proposed algorithm applies adaptive scaling factors not only to extrinsic information but also to a received log-likelihood ratio. We also propose a combined variable and check node architecture to realize the proposed algorithm with low complexity. The simulation results show that the proposed algorithm achieves up to 0.4 dB coding gain with low complexity compared to existing min-sum-based algorithms.

Decoding of Decode and Forward (DF) Relay Protocol using Min-Sum Based Low Density Parity Check (LDPC) System

“…Wang et al [51] saved around 60% to 70% of computations by variable node (VN) set and symbol combination set performed separately which reducing the search space in check node (CN) processing, C.C. Cheng et al [52] requires 51% fewer comparators with loss of 0.05dB error performance used tree structure based minimum value finder (MVF) by removing (14) (15) (16) (17) the connection units and a suitable normalization factor to enhance the error performance, Ioannis Tsatsaragkos et al [53] utilizes up to 25% less comparators also need less than 14 iterations for maximum 30 iteration by partitioning and minimum identification of approximation process, Meng Zhu et al [48] performed a uniform quantization with channel likelihood and information transmission between check nodes and variables nodes, Nguyen Thi Dieu Linh et al [54] used early stopping node which reduce the number of iteration and decreased computation processing 5 times using BPSK and 10 times using QPSK compared with conventional method, Ahmed Emran et al [55] only requires 0.08 to 0.24dB more than sum product algorithm (SPA) with much lower complexity by approximate the scaling factor graph to a stair graph with constant horizontal step S, and the scaling factor takes values which is exponential and at the same time easy to implement, Yongmin Jung et al [56] performed low complexity architecture by combined variable and check node operation just one multiplier which is used in initial process reused in the iterative process. During initial process, received LLR multiplies by the scaling factor before iterative decoding while during iterative process the extrinsic information multiplies by the scaling factor at every iteration.…”

“…From Table 2, seven of the studies used regular LDPC code [45], [47], [48], [51], [52], [55], [57]; while the others [47], [56], [57] utilized the irregular LDPC code. Ahmed.…”

“…Optimization approach is the act or process of obtaining the best result of system design under the given circumstances. From the Table2, the existing optimization approach have been used in studies includes selection searching method for scaling factors[45][47][51][52][53][55][56], numbering system[51]…”

Certain investigations on recent advances in the design of decoding algorithms using low‐density parity‐check codes and its applications

“…Existing research works on LDPC decoding were oriented towards reducing the overall computation complexity without affecting the decoding stability 11 . Then, gradually some improved versions of MSA were proposed using a variation of error correction factors which led to more effective outcomes in terms of computational costs and coding gain precision 12,13 . The existing studies have demonstrated the practical importance of the LDPC‐based decoding algorithms and its application which is oriented towards the design and development of LDPC decoder prototype modules 14 .…”