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Record W1554031761 · doi:10.1109/pimrc.2005.1651484

Capacity Region of a Multi-Code DS-UWB System with Rayleigh Monocycles Supporting Variable Bit Rate Multiclass Services

2006· article· en· W1554031761 on OpenAlexaff
T.C. Wong, J.W. Mark, K.C. Chua

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVariable bitrateComputer scienceBit error rateAlgorithmInterference (communication)Electronic engineeringCode rateCode (set theory)Frame (networking)Variable (mathematics)Rayleigh fadingSIGNAL (programming language)Decoding methodsReal-time computingTelecommunicationsChannel (broadcasting)Bit rateMathematicsEngineering

Abstract

fetched live from OpenAlex

An analytical formulation of the outage probability in terms of bit error rate specification for variable bit rate (VBR) multiclass services in a multi-code direct-sequence ultra-wideband (DS-UWB) system with Rayleigh monocycles is derived via an outage probability analysis. The analytical framework is formulated for the general case in which different traffic classes have different varying bit rates. Multiple spreading codes are used by each user to achieve variable bit rate (VBR). Closed-form expressions for the signal at the correlator's output during a frame interval and the power of the interference resulting from one of the active spreading codes from the interfering users on one pulse are explicitly derived for Rayleigh monocycles. The analytical work leads to the determination of the capacity region of a multi-code DS-UWB system supporting VBR services. Numerical results of the capacity region are also presented.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.672

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 head0.013
GPT teacher head0.199
Teacher spread0.186 · 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

Citations2
Published2006
Admission routes1
Has abstractyes

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