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Record W2114736842 · doi:10.1109/glocom.2010.5683741

Parallel Link Rendezvous in Ad Hoc Cognitive Radio Networks

2010· article· en· W2114736842 on OpenAlexaff
Majid Altamimi, Kshirasagar Naik, Xuemin Shen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Waterloo
FundersBeijing Institute For Brain DisordersKing Saud University
KeywordsRendezvousComputer scienceComputer networkControl channelCognitive radioWireless ad hoc networkMobile ad hoc networkChannel (broadcasting)ThroughputBlock (permutation group theory)Protocol (science)Distributed computingWirelessEngineeringTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

A Cognitive Radio (CR) network seeks to access and utilize the unused license spectrum portions. These portions have dynamic behavior that introduces challenge for the CR user to allocate and rendezvous on the same portion. In CR Ad-hoc Network (CRAHN), there is difficulty of maintaining a Common Control Channel (CCC); for that, CRAHN requires a protocol able to guarantee users rendezvous without CCC. This paper proposes a distributed Medium Access Control (MAC) protocol using the concept of Balanced Incomplete Block Design (BIBD) to achieve a rendezvous channel without a CCC or synchronization. If the channels of a network are assigned to the BIBD elements and the searching sequence to the BIBD block, there is a guarantee of a rendezvous in at least one channel for each searching sequence. Simulation results confirm that the protocols outperform other protocols with respect to Time to Rendezvous (TTR), channel utilization, network throughput, and percentage of collisions between the SU and the PU network. In addition, the protocols fairly distribute the network load on channels, and share the channels fairly among network nodes.

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)0.002
metaresearch head score (Gemma)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.236
Teacher spread0.224 · 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

Citations31
Published2010
Admission routes1
Has abstractyes

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