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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 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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations31
Published2010
Admission routes1
Has abstractyes

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