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Record W2143082264 · doi:10.1109/glocom.2006.224

MMC05-4: On the Optimality of Threshold Scheduling Policies for Video Transmission in Markovian Fading Wireless Channels with Channel-Aware ARQ

2006· article· en· W2143082264 on OpenAlexaff
Minh Hanh Ngo, Vikram Krishnamurthy

Bibliographic record

VenueGlobecom · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceFadingRayleigh fadingScheduling (production processes)Network packetMarkov decision processComputer networkMarkov processHybrid automatic repeat requestWirelessChannel (broadcasting)Mathematical optimizationAutomatic repeat requestTransmission (telecommunications)Real-time computingMathematicsTelecommunications

Abstract

fetched live from OpenAlex

We consider the problem of optimal transmission scheduling for real time multimedia (video) data transmission over wireless communication links. It is assumed that the wireless channel is Rayleigh fading and can be represented by a finite state Markov chain (FSMC) model, and that retransmissions are allowed via the use of an ARQ protocol. Due to a delay constraint, there is a limit on the number of time slots that may be used to transmit some (pre-designed) number of packets. The problem of optimal transmission scheduling is formulated as a finite horizon Markov decision process (MDP) with a cost function that takes into account the transmission cost and a penalty cost on the packet loss rate. Using the concept of supermodularity and convexity on the optimal cost and immediate cost functions, we prove that the optimal transmission scheduling policy is a threshold function of time and buffer size. These threshold policies are applicable for any delay-sensitive real time packet transmission system. Finally, the theoretical results are illustrated via numerical examples.

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 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 score0.585
Threshold uncertainty score0.757

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 head0.012
GPT teacher head0.221
Teacher spread0.209 · 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 teacher head, 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

Citations0
Published2006
Admission routes1
Has abstractyes

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