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Record W2140264163 · doi:10.1109/vtcf.2006.266

Branch-and-Bound Approach to OFDMA Radio Resource Allocation

2006· article· en· W2140264163 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

VenueIEEE Vehicular Technology Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsLakehead University
Fundersnot available
KeywordsOrthogonal frequency-division multiple accessComputer scienceComputational complexity theoryOrthogonal frequency-division multiplexingResource allocationFrequency-division multiple accessMathematical optimizationUpper and lower boundsAlgorithmChannel (broadcasting)Channel allocation schemesWirelessMathematicsComputer networkTelecommunications

Abstract

fetched live from OpenAlex

Radio resource allocation (RRA) problems in orthogonal frequency division multiple access (OFDMA) systems are studied in this paper. By assuming perfect channel estimation for all users, fast branch-and-bound (BnB) based optimal and suboptimal algorithms are proposed to solve the RRA problems in OFDMA systems. As demonstrated by simulation results, the proposed optimal algorithm offers the same performance as that achieved by using exhaustive full-search algorithm, but the computational complexity involved is significantly reduced relative to the full-search algorithm. The proposed suboptimal algorithm offers near- optimal performance whereas the associated computational complexity is much lower than that associated with the proposed optimal algorithm.

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 categoriesnone
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 score0.637
Threshold uncertainty score0.889

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 science0.0000.000
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 head0.007
GPT teacher head0.189
Teacher spread0.182 · 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