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Record W2117043187 · doi:10.1109/icc.2004.1313205

On the design of optical fiber based wireless access systems

2004· article· en· W2117043187 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
TopicPower Line Communications and Noise
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceRadio over fiberOptical wirelessWirelessElectronic engineeringOptical amplifierWidebandPath lossOptical linkOptical fiberComputer networkTelecommunicationsOpticsPhysicsEngineering

Abstract

fetched live from OpenAlex

Optical fiber based wireless access schemes receive renewed attention with the popularity of hot-spots. They increase capacity, QoS and support wideband multimedia services and have the possibility of utilizing existing fiber infrastructure. However, link design in a fiber-wireless system needs careful consideration of many factors. There are two signal to noise ratios involved, the optical SNR (OSNR) and the electrical SNR. These two form the cumulative SNR in the concatenated fiber-wireless channel. The OSNR is a function of the modulation index m, E/O, O/E conversion losses and, the fiber length. There is a 39 dB loss due to E/O and O/E conversion only in resistively matched wideband links and the OSNR rapidly decreases with fiber length. The cumulative SNR at the mobile unit decides the QoS and cell size. This SNR depends on OSNR, wireless channel path loss and the optical receiver amplifier gain. In this paper, we study the relationships between critical design parameters, such as maximum radio and optical link losses, cumulative and optical SNR and, optical amplifier gain in a fiber-based wireless 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.

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 score0.844
Threshold uncertainty score0.137

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 head0.042
GPT teacher head0.258
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

Quick stats

Citations42
Published2004
Admission routes1
Has abstractyes

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