An Improved Low Complex Offset Min-Sum Based Decoding Algorithm for LDPC Codes

Roberts, Michaelraj Kingston; Mohanram, S. Sudha; Shanmugasundaram, N.

doi:10.1007/s11036-019-01392-7

Cited by 15 publications

(7 citation statements)

References 29 publications

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“…It can be observed that N s is the nearest integer rounded to the product of the mini-batch size N b and the set of the SNR ratios (lines 2). As the summation of N s should equal N b , we may need to randomly adjust (add or delete) the value of N s (lines [5][6][7][8][9][10][11][12][13][14][15].…”

Section: Trainingmentioning

confidence: 99%

“…Numerical results showed that with one properly chosen parameter for each of these two algorithms, performances can be close to that of the BP algorithm. In [8], one simple method with less complex arithmetic operations to find the offset correction factor had been demonstrated. Chang et al [9] proposed a conditional variable node (VN) selecting metric to realize informed dynamic scheduling (IDS) LDPC decoding schedules.…”

Section: Introductionmentioning

confidence: 99%

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Two Novel Semi-/Auto-Adaptive SNR Algorithms to Efficiently Train Deep Neural SPA Decoders

Huang

2022

IEEE Access

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Section: Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two Novel Semi-/Auto-Adaptive SNR Algorithms to Efficiently Train Deep Neural SPA Decoders

Huang

2022

IEEE Access

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“…In the literature, there exist several variants of OMSA that effectively exploits the maximization operation using DE for the formulation of optimal offset error correction factor 67,69 . Most recently, Roberts et al introduced a new offset decoding algorithm based on the concept of probability theory 75 . In this approach, a low complex offset error correction factor was obtained through simple theoretical derivation.…”

Section: Min‐sum Approximation‐based Decoding Algorithmsmentioning

confidence: 99%

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

Roberts¹,

Kumari

Anguraj³

2021

Int J Communication

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Summary Information theory coding is an impressive and most celebrated field of research that has spawned numerous extremely important solutions to the intractable problems of secure data communications. Recent advancements in error control coding methods have seen a huge surge in using low‐density parity‐check (LDPC) code‐based decoding algorithms to solve imperative issues related to reliable data transmission and reception. Till date, extensive research efforts have been consistently being made on LDPC codes which focus on algorithm‐driven and hardware‐realization‐based approaches. The main intension of this research work is to provide an extensive systematic elucidation on the recent advancements in LDPC decoding algorithms. In addition, a thorough performance evaluation and analysis of several outstanding LDPC decoding techniques is presented. Finally, conclusions are drawn by summarizing the important research findings, interesting open problems, current challenges and broader perspectives for future directions of research.

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“…We can decode the LDPC codes by using any of the algorithms available in the literature, viz., BF, the BP algorithm [27], or the low-complexity derivative of the BP algorithm such as the min-sum algorithm [28]. We analyze the LDPC code's performance with the BP algorithm as it has been reported [11] to be the most optimal.…”

Section: F K Jhmentioning

confidence: 99%

Analytical Determination of Thresholds of LDPC Codes in Free Space Optical Channel

Bhardwaj

Dixit

Jain

2021

IEEE Open J. Commun. Soc.

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Free-space optical (FSO) communication is crucial for the next-generation 5G+ wireless networks. The FSO links suffer from atmospheric-turbulence-induced bit errors. For the increasing link's performance, low-density parity-check (LDPC) codes, complemented by the belief-propagation (BP) algorithm, are an excellent option. The bit error rate (BER) of the LDPC code is characterized by a parameter called the threshold. The threshold is the signal-to-noise ratio (SNR), after which the BER falls arbitrarily and becomes close to zero. We derive the threshold for the LDPC codes under the BP algorithm for an uncorrelated flat FSO channel. The determination of the FSO channel threshold is a tedious task as the density of the loglikelihood ratio from the FSO channel cannot be assumed as Gaussian and is available only in a numerical form. It, thus, requires testing different values of SNR as a possible threshold systematically. Therefore, we propose the divide and conquer algorithm. The threshold depends on the degree distributions, channel state information (CSI), and the turbulence level. When CSI is known, we obtain the threshold at an SNR of 8.10 dB in high turbulence for a regular (3,6) LDPC code. This threshold steps up to 12.48 dB when the CSI is unknown at the receiver. We evaluate the threshold values for various degree distributions (regular and irregular LDPC codes) under high, moderate, and low turbulence levels for both channel models (CSI known and unknown at the receiver). We also confirm the derived threshold values with MATLAB simulations.

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An Improved Low Complex Offset Min-Sum Based Decoding Algorithm for LDPC Codes

Cited by 15 publications

References 29 publications

Two Novel Semi-/Auto-Adaptive SNR Algorithms to Efficiently Train Deep Neural SPA Decoders

Two Novel Semi-/Auto-Adaptive SNR Algorithms to Efficiently Train Deep Neural SPA Decoders

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

Analytical Determination of Thresholds of LDPC Codes in Free Space Optical Channel

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