Iterative threshold decoding without interleaving for convolutional self-doubly orthogonal codes

Cardinal, C.; Haccoun, D.; Gagnon, François

doi:10.1109/tcomm.2003.815083

Cited by 44 publications

(22 citation statements)

References 20 publications

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“…The set A is said to be self-doubly-orthogonal if all of the following conditions are satisfied [6]:…”

Section: Definition 2 (Self-doubly-orthogonal Codes)mentioning

confidence: 99%

“…It has also been shown that the double orthogonality property maximizes the number of independent observations over two successive decoding iterations. Therefore, CSO2Cs can be decoded by the iterative threshold decoding algorithm without interleaving, and the asymptotic error performance may be achieved after a few iterations [6].…”

Section: Definition 2 (Self-doubly-orthogonal Codes)mentioning

confidence: 99%

“…where a is the weight value used by the weighing technique for reducing the correlations between inputs to each iteration and hence improving the error performance [3], [6]. It is clear that the above decoding procedure is divided into M steps, each corresponding to an iteration where the onestep BP decoding is simply performed.…”

Section: B) Variable Messages: Formentioning

confidence: 99%

“…Iterative decoding of CSOCs using either threshold decoding or belief propagation (BP) allows an improvement of the error performance [4], [5]. For lowcomplexity iterative threshold decoding without interleaving, convolutional self-doubly-orthogonal codes (CSO2Cs) were proposed as an extension of CSOCs [6], [7]. It has been shown that the double orthogonality property of CSO2Cs improves the code structure by maximizing the number of independent observations between successive decoding iterations.…”

Section: Introductionmentioning

confidence: 99%

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Procedures for Efficient Iterative Decoding of Orthogonal Convolutional Codes

Haccoun

Cardinal

2006

2006 IEEE International Conference on Communications

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A procedure for the forward-only iterative belief propagation decoding of orthogonal convolutional codes is presented. It can be dramatically simplified to perform the iterative threshold decoding of convolutional self-doubly-orthogonal codes without interleaving. These procedures can help implement iterative decoders efficiently using a serial concatenation of onestep BP decoders or one-step threshold decoders, respectively. Simulations have shown that the error performance of orthogonal convolutional codes can be improved by iterative decoding whether based on belief propagation decoding or threshold decoding. For convolutional self-doubly-orthogonal codes, iterative threshold decoding can achieve the same error performance as iterative belief propagation decoding, but with greatly reduced decoding complexity, allowing an advantageous tradeoff between implementation complexity and latency.

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“…The set A is said to be self-doubly-orthogonal if all of the following conditions are satisfied [6]:…”

Section: Definition 2 (Self-doubly-orthogonal Codes)mentioning

confidence: 99%

Section: Definition 2 (Self-doubly-orthogonal Codes)mentioning

confidence: 99%

Section: B) Variable Messages: Formentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Procedures for Efficient Iterative Decoding of Orthogonal Convolutional Codes

Haccoun

Cardinal

2006

2006 IEEE International Conference on Communications

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“…In a series of papers [9], [10], [11], [12] it has been shown that the less complex iterative threshold decoding (ITD) achieves the same error rate performance as BP for a special class of convolutional codes, namely convolutional self (and self-doubly) orthogonal codes.…”

Section: Introductionmentioning

confidence: 99%

Comparison of belief propagation and iterative threshold decoding based on dynamical systems

Mohamad

Teich

Lindner

2013

2013 IEEE International Symposium on Information Theory

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For a special class of convolutional codes, iterative threshold decoding (ITD) has been shown by simulations to achieve the same error rate performance as belief propagation (BP). In order to get a better understanding of these iterative decoding algorithms, we describe ITD and BP as discrete-time dynamical systems. Based on the theory of dynamical systems, we compare the dynamical behavior of ITD and BP. For the special case of a linear dynamical system, the behavior can be completely characterized. In this case we show that the fixed points of both ITD and BP are globally stable but they do not coincide. The analysis is extended to the case of a continuous-time dynamical system, which represents an important step for modeling analog iterative decoders.

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From Iterative Threshold Decoding to a Low-Power High-Speed Analog VLSI Decoder Implementation

Teich

Oliveri

2019

Advances in Computational Intelligence

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Iterative threshold decoding without interleaving for convolutional self-doubly orthogonal codes

Cited by 44 publications

References 20 publications

Procedures for Efficient Iterative Decoding of Orthogonal Convolutional Codes

Procedures for Efficient Iterative Decoding of Orthogonal Convolutional Codes

Comparison of belief propagation and iterative threshold decoding based on dynamical systems

From Iterative Threshold Decoding to a Low-Power High-Speed Analog VLSI Decoder Implementation

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