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Record W2740470694 · doi:10.1109/icc.2017.7997274

Sensing, probing, and transmitting strategy for energy harvesting cognitive radio

2017· article· en· W2740470694 on OpenAlexaff
Keyu Wu, Hai Jiang, Chintha Tellambura

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCognitive radioMarkov decision processPartially observable Markov decision processComputer scienceThroughputMarkov processTransmission (telecommunications)Power controlChannel (broadcasting)Energy harvestingEnergy (signal processing)Control channelState spaceOptimal controlMarkov chainWirelessMarkov modelPower (physics)Computer networkTelecommunicationsMathematical optimizationMachine learningMathematics

Abstract

fetched live from OpenAlex

We consider a single channel energy harvesting cognitive radio system, where the joint optimization of spectrum sensing, channel probing and transmission power control is considered with the goal to maximize the throughput. We model this control problem as a two-stage continuous-state Markov decision process with one stage for sensing and probing control, and the other for transmission power control. By utilizing the stochastic structure of the two-stage Markov decision process, we simplify the model via the notion of after-state, which reduces the state space and facilitates decision makings. Finally, the performance of the generated strategy is investigated via simulation.

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 categoriesScholarly communication
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 score0.977
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0010.001
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 head0.031
GPT teacher head0.268
Teacher spread0.236 · 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 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

Citations6
Published2017
Admission routes1
Has abstractyes

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