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Record W2169686790 · doi:10.1109/glocom.2003.1258268

Dynamic Rate and Power Adaptation for Forward Link Transmission Using High-Order Modulation and Multicode Formats in Cellular WCDMA Networks

2004· article· en· W2169686790 on OpenAlexaff
Dong In Kim, Ekram Hossain, V.K. Bhargava

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceLink adaptationTelecommunications linkTransmission (telecommunications)Code division multiple accessInterference (communication)Modulation (music)Frame (networking)Electronic engineeringPower controlPower (physics)Real-time computingComputer networkTelecommunicationsEngineeringFading

Abstract

fetched live from OpenAlex

This paper addresses the problem of dynamic rate and power adaptation for forward link data transmission using high-order modulation and multicode formats in cellular wideband code division multiple access (WCDMA) networks. A novel framework for dynamic joint adaptation of modulation order, number of code channels (hence transmission rate) and transmission power is proposed for downlink data transmission in a cellular WCDMA system where the different users have similar frame error rate (FER) requirements. Based on a general downlink signal-to-interference ratio (SIR) model, the problem of optimal dynamic rate and power adaptation is formulated, for which the rate and power allocation can be found by an exhaustive search. Two heuristic-based dynamic rate and power allocation schemes are proposed. Performance of dynamic joint rate and power adaptation under the proposed frame-work is evaluated under random micro-mobility model using computer simulations.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.502
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.277
Teacher spread0.255 · 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
GenreMethods

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

Citations2
Published2004
Admission routes1
Has abstractyes

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