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Record W2125360243 · doi:10.1109/twc.2005.850272

Downlink resource management for packet transmission in OFDM wireless communication systems

2005· article· en· W2125360243 on OpenAlexaff
Jun Cai, Xuemin Shen, J.W. Mark

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingSubcarrierComputer scienceComputer networkTelecommunications linkScheduling (production processes)Resource allocationWirelessNetwork packetReal-time computingChannel (broadcasting)TelecommunicationsEngineering

Abstract

fetched live from OpenAlex

In this paper, an optimal downlink resource management scheme for heterogeneous packet transmission in orthogonal frequency-division multiplexing (OFDM) wireless communication systems is proposed. By making use of the channel impulse response and the properties of the OFDM physical layer, a resource management scheme is developed by integrating power distribution, subcarrier allocation, and the generalized processor sharing (GPS) scheduling. The scheme can: 1) maximize system throughput; 2) guarantee the required signal-to-noise ratio for heterogeneous traffic; 3) provide fairness to all the traffic admitted in the system; and 4) satisfy the total transmission power constraint. For practical implementation, a simplified power and subcarrier allocation algorithm, a robust H/sub /spl infin// channel estimation algorithm, and a truncated GPS (TGPS) scheduling scheme are introduced. Simulation results show that the proposed resource management scheme exhibits good throughput performance.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.251
Teacher spread0.234 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations74
Published2005
Admission routes1
Has abstractyes

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