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Record W2133040873 · doi:10.1109/icc.2005.1494968

Dynamic resource allocation for delay-tolerant services in downlink OFDM wireless cellular systems

2005· article· en· W2133040873 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsMcGill University
Fundersnot available
KeywordsTelecommunications linkComputer scienceOrthogonal frequency-division multiplexingScheduling (production processes)Spectral efficiencyComputer networkResource allocationWirelessDegradation (telecommunications)Distributed computingEngineeringTelecommunications

Abstract

fetched live from OpenAlex

The paper develops a framework for relating system performance to a user scheduling mechanism in downlink OFDM mobile cellular systems for delay-tolerant traffic. The performance of dynamic resource allocation techniques using best user (BU) and round robin (RR) strategies for user scheduling, and best sub-carrier assignment with power constraint and interference learning (BSA-PC-IL), is evaluated in terms of the fraction of satisfied users and system spectral efficiency (in kbps/MHz/cell). Simulation results indicate that in a low-mobility, single-cell environment, RR performs better than BU. However, in a high-mobility environment, BU significantly outperforms RR for both single-cell and multi-cell mobile systems. In a slow-mobility, multi-cell environment, BU has a slightly better performance than RR. In general, the BU scheme has a much slower degradation rate in the fraction of satisfied users at increased system loads than the RR scheme.

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 score0.783
Threshold uncertainty score0.750

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 science0.0000.000
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 head0.004
GPT teacher head0.196
Teacher spread0.193 · 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

Quick stats

Citations8
Published2005
Admission routes1
Has abstractyes

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