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Record W2004142088 · doi:10.1109/wcnc.2012.6214069

Secondary user VoIP capacity in opportunistic spectrum access networks with friendly scheduling

2012· article· en· W2004142088 on OpenAlexaff
Hanan S. Hassanein, Ghada Badawy, T.D. Todd

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer scienceScheduling (production processes)Cognitive radioNetwork packetComputer networkVoice over IPDistributed computingThe InternetEngineeringWirelessTelecommunications

Abstract

fetched live from OpenAlex

In conventional cognitive radio networks it is usually assumed that the primary network remains unchanged. The onus is then placed on the secondary network users to make the best use of any residual radio capacity. In some situations however, the primary network operator may wish to accommodate secondary user access. This objective has motivated recent work which considers simple modifications at the primary user stations that would lead to better secondary spectrum availability. In this paper primary base station scheduling mechanisms are proposed which are designed to be friendly from a secondary network user perspective. We focus on packet scheduling algorithms which maximize friendliness when the secondary users are transmitting real-time VoIP traffic. An optimization problem is first formulated which can maximize friendliness over finite time intervals. An on-line scheduling algorithm is then proposed which attempts to achieve this goal. This is done by having the primary network temporally shape its residual capacity subject to satisfying its own packet deadline constraints. Simulation results are presented which show that our proposed scheme results in better secondary user real-time traffic support compared with conventional scheduling.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score0.847

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 communication0.0000.002
Open science0.0010.000
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 head0.038
GPT teacher head0.249
Teacher spread0.211 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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