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Record W2011843276 · doi:10.1109/vtcfall.2012.6399153

Spectrum Sharing in Cognitive Radio Systems: Ergodic and Outage Capacities

2012· article· en· W2011843276 on OpenAlexaff
Vahid Asghari, Sonia Aı̈ssa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsCognitive radioTransmitterErgodic theoryComputer scienceFadingChannel state informationChannel (broadcasting)Interference (communication)Computer networkTransmission (telecommunications)Transmitter power outputResource allocationTelecommunicationsTopology (electrical circuits)WirelessMathematicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

In this paper, we obtain the resource allocation policies pertaining to the ergodic and outage capacity notions in spectrum-sharing cognitive radio systems where the transmission parameters of the secondary users are adaptively changed based on the availability of channel state information (CSI) of the secondary user link, and soft-sensing information (SSI) about the activity of the licensed-band (primary) user as obtained from the sensing detector at the secondary user's transmitter. Assuming availability of SSI and CSI statistics at the secondary transmitter and considering operation under constraints on average received-interference and peak transmit-power, we investigate two different capacity notions of spectrum-sharing fading channels, namely, ergodic and Outage, and obtain their corresponding optimal power allocation policies. We also sustain our theoretical results by numerical and simulation analysis.

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)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.242
Teacher spread0.217 · 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 designTheoretical or conceptual
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

Citations1
Published2012
Admission routes1
Has abstractyes

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