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Record W2109875426 · doi:10.1109/wd.2011.6098215

An on-demand routing protocol for multi-hop multi-radio multi-channel cognitive radio networks

2011· article· en· W2109875426 on OpenAlex
Ahmed Chehata, Wessam Ajib, Halima Elbiaze

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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsCognitive radioComputer networkComputer scienceRouting protocolChannel (broadcasting)Distributed computingRouting (electronic design automation)WirelessTelecommunications

Abstract

fetched live from OpenAlex

Cognitive radio networks are composed of spectrum-agile devices capable of changing their configurations and transmission parameters on the fly based on their spectral environment. This capability opens up the possibility of designing flexible and dynamic spectrum access strategies with the purpose of opportunistically reusing portions of the spectrum temporarily vacated by licensed primary users. However, this flexibility in the spectrum access brings a new complexity in the design of communication protocols at different layers. In this paper, we consider the problem of routing in multi-hop cognitive radio networks. We propose a multi-radio multi-channel on-demand solution that is able to effectively manage the transmission activities of cognitive and primary users. The routing metric should be carefully developed in order to provide a tradeoff between the channel diversity of the routing path and the end-to-end delay. Through simulations, we highlight the performance of our proposed solution and compare it to multi-radio multichannel on-demand distance vector protocol.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.088
GPT teacher head0.322
Teacher spread0.234 · 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