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Record W2111031080 · doi:10.1109/glocom.2004.1379129

Queuing analysis for radio link level scheduling in a multi-rate TDMA wireless network

2005· article· en· W2111031080 on OpenAlexaff
Long Bao Le, Ekram Hossain, Attahiru Sule Alfa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceTime division multiple accessComputer networkQueueing theoryTelecommunications linkLink adaptationScheduling (production processes)Wireless networkNetwork packetFadingQueueMarkov processRadio Link ProtocolChannel (broadcasting)WirelessTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

We analyse the queuing performance of a radio link level round-robin scheduler for downlink data transmission in a multi-rate TDMA (time division multiple access) wireless network. One broadcast channel in the downlink is shared by multiple mobile users in a time multiplexing fashion and a round-robin scheduler serves each user in exactly one time slot. The finite state Markov channel (FSMC) is used to capture different states of a slow Rayleigh fading channel. Depending on the channel condition, the modulation level at the transmitter is adapted and, therefore, one or multiple packets can be transmitted in one time slot. Using the matrix geometric method (MGM), the system is modeled as a quasi-birth and death (QBD) process and then the queue length and delay distributions are derived. We present typical numerical results and discuss their useful implications on system design.

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)0.004
metaresearch head score (Gemma)0.009
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.259
Teacher spread0.227 · 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

Citations28
Published2005
Admission routes1
Has abstractyes

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