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Record W2002528944 · doi:10.1109/icc.2010.5502231

Distributed Relay Selection and Power Control in Cognitive Radio Networks with Cooperative Transmission

2010· article· en· W2002528944 on OpenAlex
Changqing Luo, F. Richard Yu, Hong Ji, Victor C. M. Leung

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 institutionsCarleton UniversityUniversity of British Columbia
Fundersnot available
KeywordsRelayComputer scienceCognitive radioTransmission (telecommunications)Relay channelComputer networkPower controlMarkov processSelection (genetic algorithm)Channel state informationMarkov chainChannel (broadcasting)Markov decision processMathematical optimizationPower (physics)WirelessTelecommunicationsMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we present a distributed relay selection and power allocation scheme concurrently considering the channel states of all related links and residual energy state of the relay nodes for cooperative transmission in cognitive radio (CR) networks. Specifically, we formulate the CR network with cooperative transmission as a restless bandit system, which has been widely applied in operations research and stochastic control. The channel state and residual energy state are presented by finite state Markov chains. With this stochastic optimization formulation, the optimal policy for relay selection and power allocation is indexable, meaning that the relay with the highest index should be selected. The proposed scheme can achieve the tradeoff between achievable rate and network lifetime. Simulation results are presented to illustrate the performance of the proposed scheme.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.864
Threshold uncertainty score0.500

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 integrity0.0000.000
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 head0.005
GPT teacher head0.211
Teacher spread0.207 · 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