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Record W2151614248 · doi:10.1002/ett.2571

Optimisation study of power allocation and relay location for amplify‐and‐forward systems over Nakagami‐<i>m</i>fading channels

2012· article· en· W2151614248 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.

Bibliographic record

VenueTransactions on Emerging Telecommunications Technologies · 2012
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsNakagami distributionRelayFadingCoding gainComputer scienceDiversity gainTransmitter power outputOutage probabilityCoding (social sciences)Power (physics)Mathematical optimizationTelecommunicationsMathematicsStatisticsDecoding methodsChannel (broadcasting)TransmitterPhysics

Abstract

fetched live from OpenAlex

ABSTRACT We consider power allocation (PA) and relay positioning in a dual‐hop amplify‐and‐forward relaying system over Nakagami‐ m fading channels. We investigate adaptive PA with fixed relay location, optimal relay location with fixed PA, and joint optimisation of the PA and relay location under transmit power constraint in order to minimise average error probability and outage probability. Analytical results are validated by numerical simulations and comparisons between the different optimisation schemes and their performance are provided. Results show that optimum PA brings only coding gain, whereas optimum relay location yields, in addition to the latter, diversity gains as well. Also, joint optimisation improves both, the diversity gain and coding gain. Furthermore, results illustrate that the analysed adaptive algorithms outperform uniform schemes. Copyright © 2012 John Wiley &amp; Sons, Ltd.

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)0.001
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 score0.835
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.039
GPT teacher head0.300
Teacher spread0.260 · 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