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Weiwei Sun 0002

dblp:63/6566-2 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2000
0000-0002-2079-4480ORCID · verified

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

Theory of computation · 2 · 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
Combinatorics and discrete mathematics · 44% Graph algorithms and graph theory · 44% Information theory · 13%
Computer networks
1 paper
Physical-layer communications · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Graph algorithms and graph theory › spectral graph theory
eigenvalue bounds		 0.012000
Lower bounds of the minimal eigenvalue of a Hermitian positive-definite matrix · IEEE Trans. Inf. Theory 2000
Combinatorics and discrete mathematics
matrix theory		 0.012000
Lower bounds of the minimal eigenvalue of a Hermitian positive-definite matrix · IEEE Trans. Inf. Theory 2000
Information theory
signal processing		 0.012000
Lower bounds of the minimal eigenvalue of a Hermitian positive-definite matrix · IEEE Trans. Inf. Theory 2000

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

lower bound derivation · 0.0eigenvalue analysis · 0.0
YearPublicationVenuePosition
2000 Lower bounds of the minimal eigenvalue of a Hermitian positive-definite matrix
abstract
In this correspondence, we present several lower bounds of the minimal eigenvalue of a class of Hermitian positive-definite matrices, which improve the previous bounds given by Dembo (1988) and Ma and Zarowski (1995).
Weiwei Sun 0002
 IEEE Trans. Inf. Theory1
1997 The minimal eigenvalues of a class of block-tridiagonal matrices
abstract
In this correspondence, we study the minimal eigenvalues of a class of block-tridiagonal matrices from telecommunication system analysis. We present an eigenvalue analysis for two-user systems and efficient estimates for m-user systems.
Linzhang Lu, Weiwei Sun 0002
 IEEE Trans. Inf. Theory2