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Record W2053406016 · doi:10.1049/iet-com.2011.0006

Ergodic and outage capacities of relaying channels in spectrum‐sharing constrained systems

2013· article· en· W2053406016 on OpenAlexafffund
Kaïs Ben Fredj, Sonia Aı̈ssa, Leila Musavian

Bibliographic record

VenueIET Communications · 2013
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransmitterRelayFadingRayleigh fadingComputer scienceNakagami distributionTransmitter power outputErgodic theoryNode (physics)Topology (electrical circuits)Transmission (telecommunications)Interference (communication)Channel (broadcasting)Computer networkRelay channelTelecommunicationsPower (physics)MathematicsPhysics

Abstract

fetched live from OpenAlex

This study investigates the capacity of multiple relay channels in different fading and shadowing environments under spectrum‐sharing constraints. The authors consider that a secondary user (SU) is allowed to share the spectrum band with a primary user (PU) provided that the SU's transmit power remains below an interference power threshold set by the PU. Considering a scenario where the SU's transmitter and receiver cannot communicate directly, a relay node, chosen among a set of K terminals, helps transmitting data from the SU's transmitter to the destination. The SU's transmitter and chosen relay node adapt their corresponding transmission parameters so as to satisfy the interference‐power constraint at the PU's receiver. The authors derive closed‐form expressions for the ergodic capacity of the SU's channel in Rayleigh fading, Nakagami‐ m fading and lognormal shadowing environments. They further obtain the outage capacity assuming the aforementioned environments and the above‐mentioned spectrum‐sharing limitations. Numerical results are provided to reinforce our theoretical derivations.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.971
Threshold uncertainty score0.433

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.058
GPT teacher head0.274
Teacher spread0.216 · 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 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

Citations7
Published2013
Admission routes2
Has abstractyes

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