The near-capacity performance and effective decoding algorithms of Low-Density Parity-Check (LDPC) codes have made them a mainstay of contemporary error correction. With an emphasis on iterative techniques like the Belief Propagation (BP) algorithm, the Min-Sum algorithm, and their variations, this paper offers a thorough analysis of decoding algorithms for LDPC codes. We investigate the trade-offs between various algorithmic techniques’ computational complexity, decoding latency, and error correction performance. Particular focus is placed on new methods and hardware-friendly algorithms that are tailored for next-generation communication systems, such 5 G and beyond.
The near-capacity performance and effective decoding algorithms of Low-Density Parity-Check (LDPC) codes have made them a mainstay of contemporary error correction. With an emphasis on iterative techniques like the Belief Propagation (BP) algorithm, the Min-Sum algorithm, and their variations, this paper offers a thorough analysis of decoding algorithms for LDPC codes. We investigate the trade-offs between various algorithmic techniques’ computational complexity, decoding latency, and error correction performance. Particular focus is placed on new methods and hardware-friendly algorithms that are tailored for next-generation communication systems, such 5 G and beyond.
Reduced-Complexity Decoding of LDPC Codes Various log-likelihood-ratio-based belief-propagation (LLR- BP) decoding algorithms and their reduced-complexity derivatives for low-density parity-check (LDPC) codes are presented. Numerically accurate representations of the check-node update computation used in LLR-BP decoding are described. Furthermore, approximate representation of the decoding computations are shown to achieve a reduction in complexity, by simplifying the check-node update or symbol-node update, or both. In particular, two main approaches for simplified check-node updates are presented that are based on the so-called min-sum approximation coupled with either a normalization term or an additive offset term. Density evolution is used to analyze the performance of these decoding algorithms, to determine the optimum values of the key parameters, and to evaluate finite quantization effects. Simulation results show that these reduced-complexity decoding algorithms for LDPC codes achieve a performance very close to that of the BP algorithm. The unified treatment of decoding techniques for LDPC codes presented here provides flexibility in selecting the appropriate scheme from a performance, latency, computational complexity, and memory-requirement perspective.
Various log-likelihood-ratio-based belief-propagation (LLR-BP) decoding algorithms and their reduced-complexity derivatives for low-density parity-check (LDPC) codes are presented. Numerically accurate representations of the check-node update computation used in LLR-BP decoding are described. Furthermore, approximate representations of the decoding computations are shown to achieve a reduction in complexity by simplifying the check-node update, or symbol-node update, or both. In particular, two main approaches for simplified check-node updates are presented that are based on the so-called min-sum approximation coupled with either a normalization term or an additive offset term. Density evolution is used to analyze the performance of these decoding algorithms, to determine the optimum values of the key parameters, and to evaluate finite quantization effects. Simulation results show that these reduced-complexity decoding algorithms for LDPC codes achieve a performance very close to that of the BP algorithm. The unified treatment of decoding techniques for LDPC codes presented here provides flexibility in selecting the appropriate scheme from performance, latency, computational-complexity, and memory-requirement perspectives.
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.
Channel coding technique is a fundamental building block in any modern communication system to realize reliable, fast, and secure data transmission. At the same time, it is a challenging and crucial task, as the data transmission happens in a channel where noise, fading, and other impairments are present. The Low-Density Parity-Check (LDPC) codes give substantial results close to the Shannon limit when the complexity and processing delay time are unlimited. In this paper, the performance of the LDPC decoding with four algorithms was investigated. The investigated four algorithms were Belief Propagation (BP), Layered Belief Propagation (LBP), Normalized min-sum (NMS), and Offset min-sum (OMS). These algorithms were examined for code rates ranging from 1/3 to 9/10 and message block lengths (64, 512, 1024, and 5120) bits. The simulation results revealed the flexibility of these decoders in supporting these code rates and block lengths, which enables their usage in a wide range of applications and scenarios for fifth-generation (5G) wireless communication. In addition, the effect of the maximum number of decoding iterations on the error correction performance was investigated, and a gain of 5.6 dB can be obtained by using 32 decoding iterations at BER=2*10-3 instead of one decoding iteration. The results showed that the decoders performed better for longer message blocks than for short message blocks, and less power was required for transmitting longer messages. Finally, the comparison results of their performance in terms of bit error rate (BER) under the same conditions showed a gain of 0.8 dB using LBP at BER= 10-5 compared with the NMS decoding algorithm.
Error Correction Codes (ECCs) stand as a linchpin in ensuring data accuracy in wireless communication.As the landscape of modern communication standards continues to expand, there is a mounting inclination towards efficient ECC technologies, such as Low-Density Parity-Check codes (LDPCs).Distinguished by their near-capacity performance and low computational complexity, LDPCs are increasingly utilized in the successful encoding and decoding of data.This study undertakes an exploration of recent advancements in LDPC research, encompassing the analysis of decoding algorithms, architectures, applications, simulations, real-world implementations, and complexities across various hardware platforms.The central research problem addressed within this work is the identification of the most efficacious LDPC decoder implementation, with an emphasis placed on Field-Programmable Gate Array (FPGA) technology.From the outcomes of this study, the Min-Sum algorithm emerged as the favored choice for LDPC decoding, particularly within FPGA implementations.The selection of this algorithm is attributable to its simplicity and implementation feasibility, thus directly addressing the posed research problem.The inherent simplicity of the Min-Sum algorithm's structure renders it a practical choice for real-world applications.Further, its proficiency in error correction and compatibility with FPGA hardware underscore its potential for augmenting the reliability of data transmission in communication systems.The findings of this study advocate for the Min-Sum algorithm as a valuable asset in LDPC decoding, notably within FPGA implementations.This positions it as a promising candidate for optimizing data communication systems.The selection of FPGA as the implementation platform reaffirms its practicality and relevance in contemporary communication technology, thus offering a comprehensive solution to the identified research problem.
Many classes of high-performance Low-density parity check codes are based on parity check matrices composed of permutation sub matrices. The emulation-simulation framework further allows the algorithm and implementation to be iteratively redefined to improve the error floor performance of message passing decoder. Log-Like hood-Ratio (LLR) based Belief-Propagation (BP) algorithm is presented for Low Density Parity Check codes. Numerically accurate representation of check node update computation used in LLR-BP decoding is described. The implementation of Sum-Product algorithm (SPA) within Low Density Parity Check Code (LDPC) decoder is described in this paper and the correction term is used to improve the decoding performance of min-sum algorithm (MSA). Quantization and log-tanh function approximation in sum-product decoder strongly affect which absorbing set dominates in error floor region. For LDPC decoder, bit error rate (BER) decreases with increase in the signal to noise ratio.