Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Weixiu Du

dblp:65/10899 · DBLP profile ↗
← Back
4ranked-venue papers
1as first author
0since 2021 · last 1996
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-authorComputer networks · 1

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.

Computer networks
1 paper
Physical-layer communications · 100%
Theoretical computer science
1 paper
Mathematical optimization · 100%

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

TopicWeightPapersLastEvidence papers
Physical-layer communications
code-division multiple access
0.011996
A new multistage detector for synchronous CDMA communications · IEEE Trans. Commun. 1996
Physical-layer communications › signal detection › multiuser detection
multistage detection
0.011996
A new multistage detector for synchronous CDMA communications · IEEE Trans. Commun. 1996
Physical-layer communications › signal detection
multiuser detection
0.011996
A new multistage detector for synchronous CDMA communications · IEEE Trans. Commun. 1996
Mathematical optimization › continuous optimization › nonlinear optimization
quadratic programming
0.011996
A new multistage detector for synchronous CDMA communications · IEEE Trans. Commun. 1996

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

likelihood function · 0.0hessian matrix transformation · 0.0
YearPublicationVenuePosition
1996 A new multistage detector for synchronous CDMA communications
abstract
In this paper, we develop a new multistage detector to approach the optimal solution of the detection problem in a synchronous code division multiple access (CDMA) system. The transformation modifies the diagonal elements of the Hessian matrix of the quadratic likelihood function and brings the continuous minimum of the transformed function as close to the optimal solution as possible. The computational complexity is essentially linear with the number of users, except that a few computations of a quadratic function are needed.
Zhen-Liang Shi, Weixiu Du, Peter F. Driessen
IEEE Trans. Commun.2
1993 Discrimination power enhancement via high resolution velocity estimators
Weixiu Du, R. Lynn Kirlin
ICASSP (4)1
1992 Multistage detection in synchronous code-division multiple-access systems
abstract
Although the optimal detection of signals in a K-user CDMA synchronous system has been proved to be NP-hard, some suboptimal methods do exist to approximate the optimal one. The authors develop a new multistage detector using the transformation proposed by Carter (1984) to approach the optimal solution. The transformation modifies the diagonal elements of the hessian matrix of the quadratic function corresponding to the optimal receiver and brings the continuous minimum of the transformed function as closely to the optimal solution as possible. The convergence of the new multistage algorithms is studied. The computational complexity is essentially linear with the number of users except that a few computations of a quadratic function are needed. The numerical results for the detection of synchronous CDMA signals are shown to be very encouraging.>
Zhen-Liang Shi, Weixiu Du, Peter F. Driessen
PIMRC2
1991 Design of transformation matrices for array processing and spectral analysis
abstract
Two methods are proposed for designing transformation matrices in array processing and spectral analysis: the stochastic method and the p-norm based method. The authors had previously presented the stochastic method for spatial smoothing, but they did not explicitly formulate the problem using the concept of stochastic transformation. The stochastic method can also be used in other applications if the signal statistics are known or can be estimated. The stochastic method enjoys the advantage of being able to utilize a priori signal statistics. The p-norm based method chooses the vector p-norm as the objective function for optimization, and it provides options to satisfy different designing specifications.>
R. Lynn Kirlin, Weixiu Du
ICASSP2