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Record W2124977877 · doi:10.1109/wpmc.2002.1088350

Scheduling of multimedia traffic in interference-limited broadband wireless access networks

2003· article· en· W2124977877 on OpenAlexaff
Mohamed H. Ahmed, Halim Yanıkömeroğlu, S. Mahmoud, D.D. Falconer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceComputer networkWireless broadbandBroadband networksQuality of serviceBase stationNetwork packetScheduling (production processes)BroadbandPacket lossWireless networkWirelessTransmission delayReal-time computingTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

The paper proposes a transmission scheduling algorithm for interference management in broadband wireless access networks. The algorithm aims at minimizing the cochannel interference using base station coordination while still maintaining the other quality of service (QoS) requirements, such as packet delay, throughput, and packet loss. The interference reduction is achieved by avoiding (or minimizing) concurrent transmission of potential dominant interferers. Dynamic slot allocation based on traffic information in other cells/sectors is employed. In order to implement the algorithm in a distributed manner, base stations have to exchange traffic information. Both real-time and non-real-time services are considered. Results show that significant enhancement in the signal-to-interference ratio can be achieved which decreases the packet error rate by almost one order of magnitude at the expense of a higher, but acceptable, packet delay at high loading.

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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.239
Teacher spread0.225 · 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

Citations9
Published2003
Admission routes1
Has abstractyes

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