MétaCan
Menu
Back to cohort
Record W2103141688 · doi:10.1109/twc.2008.071354

On the impact of soft vertical handoff on optimal voice admission control in PCF-based WLANs loosely coupled to 3G networks

2009· article· en· W2103141688 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2009
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsHandoverComputer scienceBlocking (statistics)Computer networkAdmission controlSoft handoverQuality of serviceCall blockingCall Admission ControlMobility managementVoice over IPNetwork packetWireless networkWirelessTelecommunications

Abstract

fetched live from OpenAlex

Soft vertical handoff (VHO) and admission control are usually considered as two independent mechanisms ensuring respectively packet-level QoS and call-level QoS for voice calls in loosely coupled 3G/WLAN networks. In this paper, we evaluate the impact of the soft VHO on the blocking performance of the optimal voice admission control in different mobility environments where the WLAN operates the Point Coordination Function (PCF). For this purpose, we propose an accurate analytical mobility model for the soft VHO region. Then, based on the proposed model, we derive and analyze the blocking and dropping probability expressions of the optimal voice admission control algorithm in the 3G network loosely coupled to the PCF-based WLAN. Results show us that a resource-efficient soft handoff (RESHO) performs significantly better than a static-threshold soft handoff (STSHO) particularly in WLAN mobility environments. In fact, the 3G new call blocking probability reduction gained by using RESHO compared to STSHO is largely increased when mobile station (MS) velocities have low mean and high variability which typically characterizes theWLAN mobility environment. Besides, results show us that RESHO reduces all blocking and dropping probabilities.We believe that the provided model and the presented results could help design efficient MS controlled soft VHO algorithms for emergent loosely coupled 3G/WLAN networks.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0050.000
Research integrity0.0000.002
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 head0.031
GPT teacher head0.318
Teacher spread0.287 · 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