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Sequential decoding

About: Sequential decoding is a research topic. Over the lifetime, 8667 publications have been published within this topic receiving 204271 citations.


Papers
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Proceedings ArticleDOI
11 May 2003
TL;DR: This novel scheme overcomes an existing difficulty in the IS practice that requires codebook information and shows large IS gains for single parity-check codes and short-length block codes.
Abstract: We introduce an importance sampling scheme for linear block codes with message-passing decoding. This novel scheme overcomes an existing difficulty in the IS practice that requires codebook information. Experiments show large IS gains for single parity-check codes and short-length block codes. For medium-length block codes, IS gains in the order of 10/sup 3/ and higher are observed at high signal-to-noise ratio.

49 citations

Journal ArticleDOI
TL;DR: It is shown that as the length of the code becomes large, the systolic Viterbi decoder maintains a regular and general interconnection structure as well as moderate throughput rate gain over the sequential Viterba decoder.
Abstract: Results on efficient forms of decoding convolutional codes based on the Viterbi algorithm by using systolic arrays are presented. Various properties of convolutional codes are discussed. A technique called strongly connected trellis decoding is introduced to increase the efficient utilization of all the systolic array processors. Issues dealing with the composite branch metric generation, survivor updating, overall system architecture, throughput rate, and computational overhead ratio are also investigated. The scheme is applicable to both hard and soft decoding of any rate b/n convolutional code. It is shown that as the length of the code becomes large, the systolic Viterbi decoder maintains a regular and general interconnection structure as well as moderate throughput rate gain over the sequential Viterbi decoder. >

48 citations

Proceedings ArticleDOI
17 Jun 2018
TL;DR: A novel score function is proposed for sequential decoding of polar codes by biasing the path metrics in the min-sum version of the stack successive cancellation decoding algorithm with its expected value.
Abstract: A novel score function is proposed for sequential decoding of polar codes. Significant reduction of the average decoding complexity is achieved by biasing the path metrics in the min-sum version of the stack successive cancellation decoding algorithm with its expected value. The proposed approach can be also used for near-ML decoding of short extended BCH codes.

48 citations

Journal ArticleDOI
TL;DR: A new separation algorithm to improve the error-correcting performance of LP decoding for binary linear block codes using an IP formulation with indicator variables that help in detecting the violated parity checks and an efficient method of finding cuts induced by redundant parity checks.
Abstract: Maximum likelihood (ML) decoding is the optimal decoding algorithm for arbitrary linear block codes and can be written as an integer programming (IP) problem. Feldman relaxed this IP problem and presented linear programming (LP) based decoding. In this paper, we propose a new separation algorithm to improve the error-correcting performance of LP decoding for binary linear block codes. We use an IP formulation with indicator variables that help in detecting the violated parity checks. We derive Gomory cuts from the IP and use them in our separation algorithm. An efficient method of finding cuts induced by redundant parity checks (RPC) is also proposed. Under certain circumstances we can guarantee that these RPC cuts are valid and cut off the fractional optimal solutions of LP decoding. It is demonstrated on three LDPC codes and two BCH codes that our separation algorithm performs significantly better than LP decoding and belief propagation (BP) decoding.

48 citations

Proceedings ArticleDOI
24 Jun 2007
TL;DR: Results taken together lead to improved BER performance in the low-to-moderate SNR region, where the decoding behavior is influenced by the complete pseudo-codeword spectra and by the Tanner graph cycle histogram, with the time-varying convolutional codes outperforming both the underlying QC block codes and their time-invariant Convolutional counterparts.
Abstract: In this paper we study the iterative decoding behavior of time-invariant and time-varying LDPC convolutional codes derived by unwrapping QC LDPC block codes. In particular, for a time-varying LDPC convolutional code, we show that the minimum pseudo-weight of the convolutional code is at least as large as the minimum pseudo-weight of the underlying QC code. We also prove that the unwrapped convolutional codes have fewer short cycles than the QC codes. These results taken together lead to improved BER performance in the low-to-moderate SNR region, where the decoding behavior is influenced by the complete pseudo-codeword spectra and by the Tanner graph cycle histogram, with the time-varying convolutional codes outperforming both the underlying QC block codes and their time-invariant convolutional counterparts.

48 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202351
2022112
202124
202026
201922
201832