LDPC Decoders

Savin, Valentin

doi:10.1016/b978-0-12-396499-1.00004-2

Cited by 12 publications

(4 citation statements)

References 81 publications

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“…The success grafting of gold nanoparticles at the surface of PSb-PVBN3 can clearly be observed from both carbon and nitrogen spectra (Figure 1f, 1j figure 1j), in agreement with triazole binding energy as reported in literature. [38][39][40][41] The triazole environment (0.4 at. %) is only a fraction of the total amount of nitrogen detected by XPS (1.9 at.…”

Section: Resultsmentioning

confidence: 99%

A nanopatterned dual reactive surface driven by block copolymer self-assembly

et al. 2020

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Section: Resultsmentioning

confidence: 99%

A nanopatterned dual reactive surface driven by block copolymer self-assembly

et al. 2020

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“…BP decoding consists of an iterative exchange of messages along the edges of the Tanner graph, where each message provides an estimation of the incident variable-node [24]. BP-RNN and BP-FF decoding algorithms are weighted variants of the BP decoding.…”

Section: Preliminaries a Neural Bp Decodingmentioning

confidence: 99%

Decoding Short LDPC Codes via BP-RNN Diversity and Reliability-Based Post-Processing

Rosseel¹,

Mannoni²,

Fijalkow³

et al. 2022

Preprint

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This paper investigates decoder diversity architectures for short low-density parity-check (LDPC) codes, based on recurrent neural network (RNN) models of the belief-propagation (BP) algorithm. We propose a new approach to achieve decoder diversity, by specializing BP-RNN decoders to specific classes of errors, with absorbing set support. We further combine our approach with an ordered statistics decoding (OSD) postprocessing step. We show that the OSD post-processing step effectively takes advantage of the bit-error rate optimization, deriving from the use of binary cross-entropy loss function, and the diversity brought by the use of multiple BP-RNN decoders, thus providing an efficient way to bridge the gap to maximum likelihood decoding.

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“…These strategies indicate the order in which the check node and variable node computations are performed during the decoding iterations [13]. Two types of strategies may be employed: flooded and layered.…”

Section: Theoretical Background Of Ldpc Decodingmentioning

confidence: 99%

“…The most important class of soft-decision LDPC decoding is represented by the min-sum (MS) algorithm [13] and its variants: offset MS (OMS) [10], normalized MS (NMS) [10], self-correcting MS (SCMS) [11], and finite alphabet iterative decoding (FAID) [12]. In these algorithms, the following messages are used [13]:…”

Section: Theoretical Background Of Ldpc Decodingmentioning

confidence: 99%

Design Trade‐Offs for FPGA Implementation of LDPC Decoders

Amăricăi¹,

Boncalo²

2017

Field - Programmable Gate Array

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Low density parity check (LDPC) decoders represent important throughput bottlenecks, as well as major cost and power-consuming components in today's digital circuits for wireless communication and storage. They present a wide range of architectural choices, with different throughput, cost, and error correction capability trade-offs. In this book chapter, we will present an overview of the main design options in the architecture and implementation of these circuits on field programmable gate array (FPGA) devices. We will present the mapping of the main units within the LDPC decoders on the specific embedded components of FPGA device. We will review architectural trade-offs for both flooded and layered scheduling strategies in their FPGA implementation.

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LDPC Decoders

Cited by 12 publications

References 81 publications

A nanopatterned dual reactive surface driven by block copolymer self-assembly

A nanopatterned dual reactive surface driven by block copolymer self-assembly

Decoding Short LDPC Codes via BP-RNN Diversity and Reliability-Based Post-Processing

Design Trade‐Offs for FPGA Implementation of LDPC Decoders

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