MétaCan
Menu
Back to cohort
Record W2167177211 · doi:10.1109/tvt.2003.814224

A probing process for dynamic resource allocation in fixed broadband wireless access networks

2003· article· en· W2167177211 on OpenAlexafffund
Qiang Wang, Anjali Agarwal

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2003
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsConcordia University
FundersConcordia University
KeywordsPollingComputer networkComputer scienceNetwork packetBandwidth (computing)BroadbandWireless broadbandProcess (computing)WirelessBase stationBandwidth allocationResource allocationProtocol (science)Broadband networksWireless networkTelecommunications

Abstract

fetched live from OpenAlex

The paper deals with the support of both real-time and non-real-time communication services in a broadband fixed wireless access network. It investigates the feasibility of dynamically allocating the bandwidth not utilized by other sectors in the staggered resource allocation method. A new medium-access control (MAC) protocol based on the proposed probing process that does not need information exchange and coordination among base stations is presented to improve data packet transmission. The probing process detects available slots unused by other sectors to provide a higher capacity in sectorized cells. A simplified C-PRMA, referred to as prioritized access with centralized polling command, is adopted to implement the probing process effectively. Simulation results show that the proposed MAC protocol with probing process provides an improvement over C-PRMA. In terms of data traffic, the proposed protocol increases the link capacity to near 100% as opposed to 78% for C-PRMA when the workload of other sectors is 69% of the link capacity. In terms of voice traffic, the probing process provides 23% more user capacity than C-PRMA when there are 20 voice users in other sectors.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.303
Teacher spread0.280 · 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 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

Citations4
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Vehicular TechnologySame topicWireless Communication Networks ResearchFrench-language works237,207