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Record W2049622659 · doi:10.1145/1280940.1281073

The bandwidth deficit problem in loosely coupled WLAN-to-cellular vertical handover

2007· article· en· W2049622659 on OpenAlexaff
Seyed Vahid Azhari, Mohammed N. Smadi, T.D. Todd

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHandoverComputer networkComputer scienceBandwidth (computing)Call blockingBandwidth managementDynamic bandwidth allocationChannel (broadcasting)Blocking (statistics)Bandwidth allocation

Abstract

fetched live from OpenAlex

Seamless WLAN-to-cellular handover is often difficult to achieve, since WLAN coverage can often be lost very quickly. Recent results have shown that successful handover may require that the WLAN link be allowed to rate-adjust itself to very low values (e.g., 1-2 Mbps) before the vertical handover is completed. When this occurs the call temporarily occupies a WLAN bandwidth far higher than the value for which it was originally provisioned. This effect is referred to as the vertical handover bandwidth deficit problem. In this paper we consider the effects of the bandwidth deficit problem on system performance, and how the problem can be mitigated. It is shown that this bandwidth demand can result in significant vertical handover dropping due to a lack of bandwidth on the WLAN access point. A static bandwidth reservation scheme motivated by the classical cellular guard channel approach is first considered and is found to result in an unacceptable increase in the WLAN new call blocking rate. A novel transient bandwidth reservation scheme accompanied by a momentary forced handover mechanism is proposed to overcome the shortcomings of the static approach in multiple AP WLANs. Our presented results show that almost two orders of magnitude reduction in the vertical handover dropping rate can be achieved while maintaining an acceptable new call blocking rate at the WLAN AP.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.246
Teacher spread0.235 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2007
Admission routes1
Has abstractyes

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