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Record W2947605383 · doi:10.1109/tcomm.2019.2919620

Throughput Maximization in Energy Limited Full-Duplex Cognitive Radio Networks

2019· article· en· W2947605383 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Communications · 2019
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCognitive radioThroughputComputer scienceUnderlayTransceiverInterference (communication)MIMOElectronic engineeringComputer networkWirelessSignal-to-noise ratio (imaging)TelecommunicationsEngineering

Abstract

fetched live from OpenAlex

In this paper, we consider a cognitive radio system with some of secondary users (SUs) equipped with multiple antenna full-duplex transceivers. We design a hybrid system to sense and access the spectrum opportunistically such that the SU operates on both underlay and interweave hybrid methods while the primary user (PU) may be active or inactive, respectively, to perform the sensing as agile as possible. Moreover, we maximize the throughput by allocating the spectral resources assuming limited available energy during each time frame with constraints on the interference to the primary user. We show that this problem is convex and propose a simple solution algorithm for it. Our simulations confirm the accuracy of our analytical and theoretical results and illustrate that the total achieved throughput of full-duplex cognitive radio even by considering self-interference is maximized under the assumed conditions.

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 categoriesMeta-epidemiology (narrow)
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 score0.890
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.229
Teacher spread0.211 · 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