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List decoding

About: List decoding is a research topic. Over the lifetime, 7251 publications have been published within this topic receiving 151182 citations.


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Proceedings ArticleDOI
01 Oct 2012
TL;DR: In this paper, it was shown that simultaneous non-unique decoding is rate-optimal for the general K-sender, L-receiver discrete memoryless interference channel when encoding is restricted to randomly generated codebooks, superposition coding, and time sharing.
Abstract: It is shown that simultaneous nonunique decoding is rate-optimal for the general K-sender, L-receiver discrete memoryless interference channel when encoding is restricted to randomly generated codebooks, superposition coding, and time sharing. This result implies that the Han-Kobayashi inner bound for the two-user-pair interference channel cannot be improved simply by using a better decoder such as the maximum likelihood decoder. It also generalizes and extends previous results by Baccelli, El Gamal, and Tse on Gaussian interference channels with point-to-point Gaussian random codebooks and shows that the Cover-van der Meulen inner bound with no common auxiliary random variable on the capacity region of the broadcast channel can be improved to include the superposition coding inner bound simply by using simultaneous nonunique decoding. The key to proving the main result is to show that after a maximal set of messages has been recovered, the remaining signal at each receiver is distributed essentially independently and identically.

46 citations

Proceedings ArticleDOI
01 Jan 2004
TL;DR: This paper presents the application to H.264 standard of a soft-input VLC decoding algorithm based on MAP sequence estimation techniques and using residual source redundancy information to provide channel error protection and correction.
Abstract: This paper presents the application to H.264 standard of a soft-input VLC decoding algorithm based on MAP sequence estimation techniques and using residual source redundancy information to provide channel error protection and correction. This algorithm relies on the presence of soft values and of contextual information available at the input of the source decoder, and is fully compatible with the existing H.264 standard. Numerical results obtained with this considered soft-input decoding algorithm to the decoding of a H.264 encoded video sequence under the assumption of an unequal error protection scheme are presented. Performance obtained for the 'Foreman' ITU reference sequence show that the proposed algorithm provides gains up to 12 to 15 dB in terms of PSNR when compared to classical hard input decoding methods.

46 citations

Journal ArticleDOI
TL;DR: A technique is proposed to break trapping sets while decoding to lower the error-floor, which has moderate complexity overhead and is applicable to any code without requiring a prior knowledge of the structure of its trapping sets.
Abstract: Error-floors are the main reason for excluding LDPC codes from applications requiring very low bit-error rate. They are attributed to a particular structure in the codes' Tanner graphs, known as trapping sets, which traps the message-passing algorithms commonly used to decode LDPC codes, and prevents decoding from converging to the correct codeword. A technique is proposed to break trapping sets while decoding. Based on decoding results leading to a decoding failure, some bits are identified in a previous iteration and flipped and decoding is restarted. This backtracking may enable the decoder to get out of the trapped state. A semi-analytical method is also proposed to predict the error-floor after backtracking. Simulation results indicate the effectiveness of the proposed technique in lowering the error-floor. The technique, which has moderate complexity overhead, is applicable to any code without requiring a prior knowledge of the structure of its trapping sets.

46 citations

Proceedings ArticleDOI
06 Apr 2014
TL;DR: The union bounds as well as the simulation results illustrate that, under ML decoding, SPCs are superior to NSPCs in BER performance while N SPCs and SPC’s have the same FER performance.
Abstract: The distance spectrum is used to estimate the maximum likelihood (ML) performance of block codes. A practical method which can run on a memory-constrained computer is proposed to calculate the distance spectrum of polar codes. Utilizing the distance spectrum, the frame error rate (FER) and bit error rate (BER) performances of non-systematic polar codes (NSPCs) and systematic polar codes (SPCs) are analyzed. The union bounds as well as the simulation results illustrate that, under ML decoding, SPCs are superior to NSPCs in BER performance while NSPCs and SPCs have the same FER performance.

46 citations

Journal ArticleDOI
L. van De Meeberg1
TL;DR: The measured bit error rate P_b for a constraint length 3 decoder has been plotted versus the channel transition probability p and shows close agreement with the improved bound on the bit error probability.
Abstract: Tighter upper bounds on the error event and the bit error probabilities, respectively, for maximum-likelihood decoding of binary convolutional codes on the binary symmetric channel are derived from upper bounds previously published by Viterbi [1]. The measured bit error rate P_b for a constraint length 3 decoder has been plotted versus the channel transition probability p and shows close agreement with the improved bound on the bit error probability.

46 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202384
2022153
202179
202078
201982
201894