Linzhi Shen 0001

dblp:132/9388-1 · also Lin-Zhi Shen 0001 · DBLP profile ↗
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3ranked-venue papers
3as first author
0since 2021 · last 2019
0000-0001-8556-9877ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 2 · 2 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Coding theory · 80% Information theory · 20%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Coding theory › error-correcting codes › error probability analysis
decoding error probability
0.412019
The Decoding Error Probability of Linear Codes Over the Erasure Channel · IEEE Trans. Inf. Theory 2019
Information theory › communication channels › channel models › discrete memoryless channel
erasure channel
0.412019
The Decoding Error Probability of Linear Codes Over the Erasure Channel · IEEE Trans. Inf. Theory 2019
Coding theory › error-correcting codes › block codes
linear code
0.412019
The Decoding Error Probability of Linear Codes Over the Erasure Channel · IEEE Trans. Inf. Theory 2019
Coding theory › error-correcting codes › decoding
list decoding
0.412019
The Decoding Error Probability of Linear Codes Over the Erasure Channel · IEEE Trans. Inf. Theory 2019
Coding theory › error-correcting codes › decoding › decoding algorithms › optimal decoding
maximum-likelihood decoding
0.412019
The Decoding Error Probability of Linear Codes Over the Erasure Channel · IEEE Trans. Inf. Theory 2019

Methods — techniques the papers use, named apart from their topics

support weight distribution · 0.4error exponent analysis · 0.4
YearPublicationVenuePosition
2019 The Decoding Error Probability of Linear Codes Over the Erasure Channel
abstract
In this paper, we study the decoding error probability of linear codes over the erasure channel under the list decoding. The notion of the qℓ-incorrigible sets of linear codes is introduced to characterize its decoding error probability under the list decoding or the maximum likelihood decoding. By calculating the qℓ-incorrigible set distributions, the decoding error probability of a linear code over the erasure channel under the list decoding or the maximum likelihood decoding is expressed by its support weight distributions. For the ensemble of all [n,k] linear codes, the average decoding error probability under the maximum likelihood decoding and the average unsuccessful decoding probability under unambiguous decoding are determined. Furthermore, the error exponent of the average unsuccessful decoding probability under the unambiguous decoding is determined for the ensemble of all [n,nR] linear codes.
Linzhi Shen 0001, Fang-Wei Fu 0001
IEEE Trans. Inf. Theory1
2018 A new class of zero-difference balanced functions
Linzhi Shen 0001, Jiejing Wen, Fang-Wei Fu 0001
Inf. Process. Lett.1
2015 The list decoding error probability of linear codes over the erasure channel
abstract
In this paper, we study the list decoding error probability of a linear code over the erasure channel. The notion of L-incorrigible sets of a linear code is introduced to characterize its performance under list decoding. The L-incorrigible set distribution of a linear code can also be used to completely determine its decoding error probability under maximum likelihood decoding over the erasure channel. Furthermore, we show that the L-incorrigible set distribution of a linear code can be determined by its support weight distribution. Finally, the error exponent of the unsuccessful decoding probability under optimal decoding for the ensemble of all [n, nR] linear codes is determined.
Linzhi Shen 0001, Fang-Wei Fu 0001
ISIT1