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John Vicente

dblp:76/9842 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2011
—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
Network optimization and economics · 60% Wireless networking · 20% Physical-layer communications · 20%

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

TopicWeightPapersLastEvidence papers
Wireless networking › heterogeneous wireless networks
heterogeneous network integration
0.112011
Integration Gain of Heterogeneous WiFi/WiMAX Networks · IEEE Trans. Mob. Comput. 2011
Network optimization and economics › fairness
max-min fairness
0.112011
Integration Gain of Heterogeneous WiFi/WiMAX Networks · IEEE Trans. Mob. Comput. 2011
Physical-layer communications › spread spectrum
processing gain
0.112011
Integration Gain of Heterogeneous WiFi/WiMAX Networks · IEEE Trans. Mob. Comput. 2011
Network optimization and economics
resource allocation
0.112011
Integration Gain of Heterogeneous WiFi/WiMAX Networks · IEEE Trans. Mob. Comput. 2011
Network optimization and economics › fairness
throughput fairness
0.112011
Integration Gain of Heterogeneous WiFi/WiMAX Networks · IEEE Trans. Mob. Comput. 2011

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

simulation · 0.1heuristic algorithm · 0.1approximation algorithm · 0.1
YearPublicationVenuePosition
2011 Integration Gain of Heterogeneous WiFi/WiMAX Networks
abstract
We study the integrated WiFi/WiMAX networks, where users are equipped with dual-radio interfaces that can connect to either a WiFi or a WiMAX network. Previous research on integrated heterogeneous networks (e.g., WiFi/cellular) usually considers one network as the main and the other as the auxiliary. The performance of the integrated network is compared with the "main” network. The gain is apparently due to the additional resources from the auxiliary network. In this study, we are interested in integration gain that comes from the better utilization of the resource rather than the increase of the resource. The heterogeneity of the two networks is the fundamental reason for the integration gain. To quantify it, we design a generic framework that supports different performance objectives. We focus on the max-min throughput fairness in this work and also briefly cover the proportional fairness metric. We first prove that it is NP-hard to achieve integral max-min throughput fairness, then propose a heuristic algorithm, which provides two-approximation to the optimal fractional solution. Simulation results demonstrate significant integration gain from three sources, namely, spatial multiplexing, multinetwork diversity, and multiuser diversity. For the proportional fairness metric, we derive the formulation and propose a heuristic algorithm, which shows satisfactory performance when compared with the optimal solution.
Wei Wang 0074, Xin Liu 0002, John Vicente, Prasant Mohapatra
IEEE Trans. Mob. Comput.3