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Record W2064615650 · doi:10.1002/wcm.373

Air interface switching and performance analysis for fast vertical handoff in cellular network and WLAN interworking

2006· article· en· W2064615650 on OpenAlexaff
Minghui Shi, Liang Xu, Xuemin Shen, J.W. Mark, Aladdin Saleh

Bibliographic record

VenueWireless Communications and Mobile Computing · 2006
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsBell (Canada)University of Waterloo
Fundersnot available
KeywordsHandoverComputer networkComputer scienceNetwork packetAir interfaceWirelessWireless networkNetwork interfaceTelecommunicationsBase station

Abstract

fetched live from OpenAlex

Abstract The integration of wireless local area network (WLAN) hotspot and the 3G cellular networks is imminently the future mode of public access networks. One of the key elements for the successful integration is vertical handoff between the two heterogeneous networks. Service disruption may occur during the vertical handoff because of the IP layer handoff activities, such as registration, binding update, routing table update, etc. In this paper, the network interface switching and registration process are proposed for the integrated WLAN/cellular network. Two types of fast vertical handoff protocols based on bicasting and non‐bicasting supporting real‐time traffic, such as voice over IP, are modeled. The performance of a bicasting based handoff scheme is analyzed and compared with that of fast handoff without bicasting. Numerical results and the simulation are given to show that packet loss rate can be reduced by the bicasting during handoff scheme without increasing bandwidth on both wireless interfaces. Copyright © 2006 John Wiley & Sons, Ltd.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.227
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2006
Admission routes1
Has abstractyes

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