MétaCan
Menu
Back to cohort
Record W1581559247 · doi:10.1109/icc.2002.997456

Performance analysis of rate adaptation scheme for data traffic in DS-CDMA systems

2003· article· en· W1581559247 on OpenAlexaff
Liang Xu, Xuemin Shen, J.W. Mark

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceCode division multiple accessPower controlFadingThroughputBit error rateSpread spectrumInterference (communication)Markov chainReal-time computingChannel (broadcasting)Transmission (telecommunications)Computer networkPower (physics)TelecommunicationsWireless

Abstract

fetched live from OpenAlex

A rate adaptation scheme for data traffic in direct-sequence code-division multiple-access (DS-CDMA) system is studied. With the scheme, the received power of each active data user is maintained constant by using transmission power control to compensate for channel fading and path loss, while the transmission rate is dynamically adjusted to guarantee a target bit energy-to-equivalent noise spectral density (E/sub b//N/sub e/) when interference varies. A continuous-time Markov chain (CTMC) with state-dependent parameter is used to model the user activities in the system. Analysis and simulation results show that the rate adaptation scheme outperforms the conventional SIR based power control scheme, in terms of a power gain and lower average delay for the data users to achieve the same throughput. Moreover, by applying back-off access control, the performance of the rate adaptation scheme can be further improved.

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)0.003
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.138
GPT teacher head0.336
Teacher spread0.198 · 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

Citations5
Published2003
Admission routes1
Has abstractyes

Explore more

Same topicWireless Communication Networks ResearchFrench-language works237,207