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Record W2276680976 · doi:10.1109/vtcfall.2015.7391070

Performance Analysis of Low-Complexity Uniform Power Loading with Reduced-Overhead OFDM Systems over Rayleigh Fading Channels

2015· article· en· W2276680976 on OpenAlex
Ebrahim Bedeer, Md. Jahangir Hossain

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingRayleigh fadingFadingTransmitterOverhead (engineering)Channel (broadcasting)Computer scienceChannel capacityMultiplexingPower (physics)Electronic engineeringControl theory (sociology)TelecommunicationsEngineering

Abstract

fetched live from OpenAlex

In this paper, we analyze the performance of two low-complexity uniform power loading with reduced- overhead orthogonal frequency division multiplexing (OFDM) schemes over Rayleigh fading channels. In the first feedback scheme, the receiver feeds back to the transmitter the channel gains and the indices of the best $M$ subchannels; while for the second feedback scheme, the receiver feeds back only the indices of the best $M$ subchannels. In both schemes, the available power budget is equally distributed over the best $M$ subchannels. We derive closed-form expressions for the capacity and the outage capacity of the first and second schemes, respectively. Numerical results show that there is an optimal number of the best subchannels, $M$, that maximizes the achievable capacity and it depends on the system parameters.

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: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.227
Teacher spread0.205 · 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

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Citations1
Published2015
Admission routes1
Has abstractyes

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