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Record W2079281532 · doi:10.1002/ett.2736

Joint subcarrier and power allocation in downlink OFDMA systems: an multi‐objective approach

2013· article· en· W2079281532 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 · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSubcarrierSortingComputer scienceOrthogonal frequency-division multiple accessTelecommunications linkMathematical optimizationGenetic algorithmResource allocationJoint (building)Power (physics)Orthogonal frequency-division multiplexingAlgorithmEngineeringMathematicsComputer network

Abstract

fetched live from OpenAlex

ABSTRACT In this paper, we present a new technique for resource allocation in multi‐user orthogonal frequency division multiple access systems. The goal is to maximise the minimum data rate available to any user while minimising the total transmitted power. In order to achieve an optimal solution and capacity bounds, the subcarrier and power should be allocated simultaneously. Multi‐objective genetic algorithm can be used for joint allocation of subcarriers and power in such a case, and in this paper, it is achieved using non‐dominated sorting genetic algorithm‐II. The simulation results indicate that the proposed algorithm achieves high data rates as compared with previous algorithms. The algorithm allocates both subcarriers and bits jointly without being computationally expensive. The faster convergence of the algorithm to near optimal value, as compared with previous algorithms, is indicative of its less complexity. Copyright © 2013 John Wiley & 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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.815
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.016
GPT teacher head0.234
Teacher spread0.218 · 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