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Record W2128744749 · doi:10.1109/glocom.2004.1379007

Delay constrained rate and power adaptation over correlated fading channels

2005· article· en· W2128744749 on OpenAlex

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 British Columbia
Fundersnot available
KeywordsFadingComputer scienceMarkov decision processAutomatic repeat requestPower controlChannel (broadcasting)Markov processChannel state informationWirelessControl theory (sociology)Mathematical optimizationPower (physics)Computer networkHybrid automatic repeat requestControl (management)TelecommunicationsTelecommunications linkMathematicsStatistics

Abstract

fetched live from OpenAlex

We present a general Markov decision process based framework for the identification of optimal delay-constrained rate adaptation for MQAM systems and power adaptation for type-I hybrid ARQ systems. This framework can be applied to adaptive resource allocation over correlated fading channels. It is shown that the optimal rate and power control can be obtained by solving the appropriately formulated Markov decision processes using linear programming techniques. In the case of adaptive MQAM systems, the optimal rate allocation policy is dependent on the current buffer occupancy and the channel state. For hybrid ARQ systems the power control law can be formulated as being dependent on the current buffer occupancy and the history record of channel and action observations. The results of this paper are of importance for the development of modern wireless standards that support delay sensitive multimedia traffic.

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 score0.818
Threshold uncertainty score0.443

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 head0.007
GPT teacher head0.200
Teacher spread0.193 · 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

Citations31
Published2005
Admission routes1
Has abstractyes

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