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Record W2115254362 · doi:10.1109/vetecs.2005.1543538

Analysis of Average Signal-to-Interference-Noise Ratio for Indoor UWB Rake Receiving System

2005· article· en· W2115254362 on OpenAlexaff
Tao Jia, Dong In Kim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsSimon Fraser University
FundersMedical Research CouncilInha University
KeywordsRakeInterference (communication)Computer scienceAsynchronous communicationRake receiverSignal-to-interference-plus-noise ratioElectronic engineeringNoise (video)Channel (broadcasting)Signal-to-noise ratio (imaging)Transmission (telecommunications)WirelessUltra-widebandTelecommunicationsComputer networkFadingEngineeringPhysicsPower (physics)

Abstract

fetched live from OpenAlex

In this paper, we derive an analytical expression for average signal-to-interference-plus-noise ratio (SINR) for ultra wideband (UWB) Rake receiver in an indoor multiuser communication scenario, given that the interference level is fluctuating due to asynchronous transmission among different users. The indoor wireless channel model adopted here is a standard channel model recently released by IEEE 802.15 study group 3a. We propose a theoretical framework to analyze the average SINR and show that our analysis is well coincident with the simulation results. Finally, we show that our result can be used to theoretically determine the optimum integration interval for a single-user UWB transmitted reference (TR) system.

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)0.001
metaresearch head score (Gemma)0.004
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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
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

Citations17
Published2005
Admission routes1
Has abstractyes

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