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Record W2014103891 · doi:10.1145/1280940.1280957

Efficient resource allocation in clustered wireless mesh networks

2007· article· en· W2014103891 on OpenAlex
Ho Ting Cheng, Weihua Zhuang

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 Waterloo
Fundersnot available
KeywordsComputer scienceSubcarrierComputer networkResource allocationQuality of serviceNetwork packetScheduling (production processes)WirelessFadingMaximum throughput schedulingDistributed computingChannel (broadcasting)Dynamic priority schedulingMathematical optimizationRound-robin schedulingOrthogonal frequency-division multiplexingTelecommunications

Abstract

fetched live from OpenAlex

Due to the requisite of multi-channel communications for high-speed data transmissions, power allocation for opportunistically exploiting fading wireless channels, and packet scheduling for quality-of-service provisioning, joint power-frequency-time resource allocation is indispensable. In this paper, we propose a low-complexity intra-cluster resource allocation algorithm, taking power allocation, subcarrier allocation, and packet scheduling into consideration. Numerical results demonstrate that our algorithm is close to optimal, and that our optimality-driven resource allocation algorithm outperforms a greedy algorithm, achieving higher resource utilization and better performance compromise among throughput, packet dropping rate, and packet delay.

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 score0.866
Threshold uncertainty score0.557

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 head0.006
GPT teacher head0.211
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

Quick stats

Citations1
Published2007
Admission routes1
Has abstractyes

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