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Record W2104362704 · doi:10.1109/pimrc.2006.254245

Enhanced Wireless Hotspot Downlink Supporting IEEE802.11 and WCDMA

2006· article· en· W2104362704 on OpenAlexaff
Roland Yuen, Xavier Fernando

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSubcarrierComputer scienceTelecommunications linkComputer networkAir interfaceWirelessRadio over fiberRadio resource managementLink adaptationSignal-to-interference-plus-noise ratioElectronic engineeringWireless networkSubcarrier multiplexingOptical linkRadio frequencyOrthogonal frequency-division multiplexingTelecommunicationsFadingEngineeringOptical fiberChannel (broadcasting)Power (physics)Physics

Abstract

fetched live from OpenAlex

Dual mode handsets that support both cellular and wireless local area network (WLAN) interfaces have been recently introduced by manufacturers like Nokia. Wireless hotspots also better to be enhanced to support both these services. Simultaneous subcarrier multiplexed transmission of both WLAN and cellular radio signals over a single radio-over-fiber (ROF) link is possible for hotspot enhancement. However, link design in this multi-system scenario is a complex task. There are number of quality measures such as signal to noise, distortion and interference ratios involved both in the optical link and in the air interface. These are functions of several parameters such as the fiber length, system bandwidth, modulation depth, radio cell size and relative RF power. The scenario is quite challenging when two RF systems are involved. In this paper we analyze such a dual system downlink and show how to decide the cumulative optical modulation index (mu) and the RF power ratio (T) that will yield the best performance for both systems

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.377

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 head0.006
GPT teacher head0.218
Teacher spread0.212 · 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 designBench or experimental
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

Citations9
Published2006
Admission routes1
Has abstractyes

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