Tracking Forecast Memories for Stochastic Decoding

Tehrani, Saeed Sharifi; Naderi, Ali; Kamendje, Guy-Armand; Mannor, Shie; Gross, Warren J.

doi:10.1007/s11265-009-0441-5

Cited by 11 publications

(5 citation statements)

References 20 publications

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“…Stochastic decoders also require channel SNR estimation, so they share this fixed complexity cost with NGDBF. The most efficient stochastic decoding strategy is the Tracking Forecast Memory (TFM) described by Tehrani et al [18]. The TFM-based decoder requires 2d c − 1 XOR operations at each parity-check node, nearly twice the number of XNOR operations needed by GDBF algorithms.…”

Section: ) Comparison With Previous Gdbf Algorithmsmentioning

confidence: 99%

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Noisy Gradient Descent Bit-Flip Decoding for LDPC Codes

Sundararajan

Winstead

Boutillon

2014

IEEE Trans. Commun.

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A modified Gradient Descent Bit Flipping (GDBF) algorithm is proposed for decoding Low Density Parity Check (LDPC) codes on the binary-input additive white Gaussian noise channel. The new algorithm, called Noisy GDBF (NGDBF), introduces a random perturbation into each symbol metric at each iteration. The noise perturbation allows the algorithm to escape from undesirable local maxima, resulting in improved performance. A combination of heuristic improvements to the algorithm are proposed and evaluated. When the proposed heuristics are applied, NGDBF performs better than any previously reported GDBF variant, and comes within 0.5 dB of the belief propagation algorithm for several tested codes. Unlike other previous GDBF algorithms that provide an escape from local maxima, the proposed algorithm uses only local, fully parallelizable operations and does not require computing a global objective function or a sort over symbol metrics, making it highly efficient in comparison. The proposed NGDBF algorithm requires channel state information which must be obtained from a signal to noise ratio (SNR) estimator. Architectural details are presented for implementing the NGDBF algorithm. Complexity analysis and optimizations are also discussed.Comment: 16 pages, 22 figures, 2 table

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Section: ) Comparison With Previous Gdbf Algorithmsmentioning

confidence: 99%

“…The results shown in Figs. 18 and 19 represent single cases, and only partially demonstrate the superior convergence of NGDBF algorithms. To gain more insight into the convergence properties, we performed statistical analysis on the objective functions of several GDBF and NGDBF algorithms over many frames.…”

Section: Convergence Analysismentioning

confidence: 99%

Noisy Gradient Descent Bit-Flip Decoding for LDPC Codes

Sundararajan

Winstead

Boutillon

2014

IEEE Trans. Commun.

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“…In [14], tracking forecast memories (TFMs) are introduced to extract the mean of regenerative bits, as a replacement for edge-memories. They have the advantage of a lower hardware complexity, but do not improve the performance of the decoder, which remains approximately 0.5 dB away from 32 iterations floating-point SPA.…”

Section: B Stochastic Decodingmentioning

confidence: 99%

“…As was noted in [6] and [14], it is likely that the gap between the performance of floating-point SPA and of stochastic decoding is due to the variable node implementation. The RHS algorithm improves the accuracy of the variable node operation by extracting the information in the stochastic stream and performing exact operations in the LLR domain.…”

Section: A Algorithmmentioning

confidence: 99%

“…First, the probability value that is embedded in the stochastic stream must be estimated, and second, that probability must be converted to the LLR domain. The same function used in [14] for TFMs at the output of the stochastic variable node can be used here at the input. This function is also known as an exponential moving average and is a wellknown first-order tracking filter.…”

Section: ) Converting Stochastic Streams To Llr Valuesmentioning

confidence: 99%

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A Relaxed Half-Stochastic Iterative Decoder for LDPC Codes

Leduc-Primeau

Hemati

Gross

et al. 2009

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

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Abstract-This paper presents a Relaxed Half-Stochastic (RHS) low-density parity-check (LDPC) decoding algorithm that uses some elements of the sum-product algorithm (SPA) in its variable nodes, but maintains the low-complexity interleaver and check node structures characteristic of stochastic decoders. The algorithm relies on the principle of successive relaxation to convert binary stochastic streams to a log-likelihood ratio (LLR) representation. Simulations of a (2048, 1723) RS-LDPC code show that the RHS algorithm can outperform 100-iterations floating-point SPA decoding. We describe approaches for lowcomplexity implementation of the RHS algorithm. Furthermore, we show how the stochastic nature of the belief representation can be exploited to lower the error floor.

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A low-latency algorithm for stochastic decoding of LDPC codes

Huang

Gaudet

Salehi

2015

2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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We introduce a low-latency stochastic decoding algorithm, called conditional stochastic decoding, to implement iterative Low-Density Parity-Check (LDPC) decoders. The conditional stochastic decoder, which utilizes reliable messages in the decoding process and the channel receiving probability to generate stochastic streams, improves error rate performance and decreases decoding latency. Compared to conventional stochastic decoders, the proposed algorithm achieves more than 20-percent reduction in the decoding latency for the (1056,528) LDPC code from the WiMAX standard. We also show that the proposed algorithm, due to its lower frame error rate, is a suitable choice to be used in the Hybrid Automatic Repeat reQuest (HARQ) systems.

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Tracking Forecast Memories for Stochastic Decoding

Cited by 11 publications

References 20 publications

Noisy Gradient Descent Bit-Flip Decoding for LDPC Codes

Noisy Gradient Descent Bit-Flip Decoding for LDPC Codes

A Relaxed Half-Stochastic Iterative Decoder for LDPC Codes

A low-latency algorithm for stochastic decoding of LDPC codes

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