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Record W2734577729 · doi:10.1109/twc.2017.2723577

Further Results on Extended Delivery Time for Secondary Packet Transmission

2017· article· en· W2734577729 on OpenAlexaff
Muneer Usman, Hong‐Chuan Yang, Mohamed‐Slim Alouini

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2017
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCognitive radioComputer scienceNetwork packetTransmission (telecommunications)TransceiverComputer networkChannel (broadcasting)WirelessTelecommunications

Abstract

fetched live from OpenAlex

Cognitive radio transceiver can opportunistically access the underutilized spectrum resource of primary systems for new wireless services. With interweave cognitive implementation, secondary transmission may be interrupted by primary transmission. To facilitate the packet delay analysis of such secondary transmission, we study the extended delivery time of secondary packet transmission. In particular, we derive the exact distribution function of extended delivery time of a fixed-size secondary packet with non-work-preserving strategy, where interrupted packets must be repeated. We also analyze the effect of imperfect periodic sensing, i.e., the secondary user periodically senses the spectrum for availability, with a chance of missing an available channel on a certain sensing attempt. These results complement previous work on work-preserving strategy with perfect sensing. Selected numerical and simulation results are presented for verifying the mathematical formulation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0020.005
Open science0.0030.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0110.002

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 head0.030
GPT teacher head0.278
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2017
Admission routes1
Has abstractyes

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