MétaCan
Menu
Back to cohort
Record W2020196384 · doi:10.1109/icc.2012.6363842

CM-MAC: A cognitive MAC protocol with mobility support in cognitive radio ad hoc networks

2012· article· en· W2020196384 on OpenAlexaff
Peng Hu, Mohamed Ibnkahla

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer networkComputer scienceCognitive radioThroughputWireless ad hoc networkNode (physics)Multiple Access with Collision Avoidance for WirelessProtocol (science)Access controlCarrier sense multiple access with collision avoidanceControl channelMedia access controlOptimized Link State Routing ProtocolWirelessRouting protocolTelecommunicationsEngineeringBase stationNetwork packetMedicine

Abstract

fetched live from OpenAlex

Cognitive radio ad hoc networks (CRAHNs) have recently been proposed as a way to bring cognitive radio technology to traditional ad hoc networks. An important problem is to design a medium access control (MAC) protocol that addresses the decentralized control and local observation for spectrum management. In this paper, we propose a cognitive MAC protocol with mobility support (CM-MAC) based on Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) technique, where CM-MAC protocol can respond to the CRs vicinity state to primary exclusive regions. Furthermore, this paper analyzes the throughput performance for the proposed MAC protocol with the consideration of multiple primary user activities and CR node mobility. Our analytical results show that the proposed MAC protocol has desired upper bound of spectrum utilization as well as outperforms the throughput performance of CSMA/CA MAC and statistical channel allocation (SAC) MAC protocols given a certain condition.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.019
GPT teacher head0.280
Teacher spread0.261 · 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.

Study designOther design
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

Citations10
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicCognitive Radio Networks and Spectrum SensingFrench-language works237,207