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Record W2105377890 · doi:10.1109/tvt.2007.905329

Fast Client-Based Connection Recovery for Soft WLAN-to-Cellular Vertical Handoff

2008· article· en· W2105377890 on OpenAlexaff
Vahid Azhari, Mohammad AL-Smadi, T.D. Todd

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2008
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer networkHandoverComputer scienceCellular networkBase stationWirelessNetwork packetWireless networkDefault gatewaySoft handoverRoamingLocal area networkWi-FiTelecommunications

Abstract

fetched live from OpenAlex

Wireless local area network (WLAN)-to-cellular vertical handoff (VHO) involves time-consuming procedures that may significantly disrupt real-time communication. When a soft handoff is anchored by an enterprise private branch exchange (PBX)/gateway, handoff execution can take several seconds due to the time required to establish the cellular-to-PBX call leg. This situation is compounded by the fact that WLAN coverage may sometimes be lost far sooner than a VHO can be triggered and executed. In this paper, we propose and investigate the use of a VHO support node (VHSN) which is attached locally to the wired LAN hotspot infrastructure. When WLAN coverage is lost, the VHSN improves soft-handoff performance by quickly intercepting and redirecting the media flow through the local cellular base station using its cellular end-station attachment. This action can quickly recover the connection before the new cellular call leg is established. Unlike proxy forwarding, wireless multihop, and other infrastructure-modification schemes, the VHSN does not extend wireless coverage or perform infrastructure-based packet forwarding. Instead, the VHSN functions as both an (Ethernet) LAN and a cellular end station. Results will be presented which show the performance improvements that are possible using the proposed mechanism.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.629
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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 teacher head, not a consensus.

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

Citations10
Published2008
Admission routes1
Has abstractyes

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