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Xinge Yan

dblp:207/4541 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

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

Computer 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 · 85% Internet of things and sensor networks · 8% Wireless networking · 8%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Physical-layer communications › MIMO › interference channel › MIMO interference channel
linear transceiver design
0.312017
Multi-Objective Optimization for Distributed MIMO Networks · IEEE Trans. Commun. 2017
Physical-layer communications › MIMO › interference channel
MIMO interference channel
0.312017
Multi-Objective Optimization for Distributed MIMO Networks · IEEE Trans. Commun. 2017
Internet of things and sensor networks
energy harvesting
0.112017
Multi-Objective Optimization for Distributed MIMO Networks · IEEE Trans. Commun. 2017
Physical-layer communications
physical layer security
0.112017
Multi-Objective Optimization for Distributed MIMO Networks · IEEE Trans. Commun. 2017
Wireless networking
simultaneous wireless information and power transfer
0.112017
Multi-Objective Optimization for Distributed MIMO Networks · IEEE Trans. Commun. 2017

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

weighted tchebycheff method · 0.3penalty function method · 0.3multi-objective optimization · 0.3charnes-cooper transformation · 0.3
YearPublicationVenuePosition
2017 Multi-Objective Optimization for Distributed MIMO Networks
abstract
In this paper, we investigate the linear transceiver optimization for multiple-inputmultiple-output (MIMO) interference networks, where multiple pairs of multi-antenna source and destination nodes communicate simultaneously. Different from most of existing works, we jointly consider three critical issues of the linear transceiver optimization for MIMO interference networks based on multi-objective optimization theory, i.e., signal transmission, energy and security. Specifically, using the modified weighted Tchebycheff method, we investigate three kinds of multi-objective optimization problems (MOOPs): 1) sum mean square error minimization and harvested energy maximization; 2) transmit power minimization and energy harvesting efficiency maximization; 3) transmit power minimization, energy harvesting efficiency maximization, and physical layer security. Based on the Charnes-Cooper transformation and penalty function method, the formulated MOOPs are transformed into convex optimization problems and thus can be effectively solved. The resulting Pareto optimal solutions set reveals the complicated but important relationships among these involved single objective optimization problems, which are usually individually investigated in the literature. Finally, numerical simulation results demonstrate the performance advantages of the proposed algorithm and corroborate the theoretical analysis.
Zan Li 0001, Shiqi Gong, Chengwen Xing, Zesong Fei, Xinge Yan
IEEE Trans. Commun.5