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Record W2009767393 · doi:10.1109/ccece.2014.6900961

A spatial reuse media access protocol for cooperative spectrum sensing

2014· article· en· W2009767393 on OpenAlexaff
Xiao Shao, H. Leib

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsMcGill University
Fundersnot available
KeywordsCognitive radioComputer scienceComputer networkReuseBandwidth (computing)Protocol (science)Multipath propagationWireless intrusion prevention systemFadingWirelessWireless sensor networkWireless networkKey distribution in wireless sensor networksTelecommunicationsChannel (broadcasting)Engineering

Abstract

fetched live from OpenAlex

Cognitive Radio (CR) is a paradigm for a more flexible and efficient usage of radio spectrum, where spectrum sensing is a vital function. When spectrum sensing is performed by several sensors in cooperation to overcome multipath fading and shadowing, a proper media access control (MAC) protocol is needed to coordinate the information exchange between sensors, and conserve resources. This work considers a dedicated wireless sensor network (WSN) for cooperative spectrum sensing, focusing on a spatial reuse MAC protocol. The MAC protocol is designed for a bandwidth limited system, and it is associated with the application of cooperative spectrum sensing. The design and implementation of such a protocol are discussed in detail, and performance numerical results for a specific scenario are presented.

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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.034
GPT teacher head0.309
Teacher spread0.276 · 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
GenreMethods

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

Citations0
Published2014
Admission routes1
Has abstractyes

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