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SPECTRUM AGGREGATION SCHEME IN A WIRELESS BROADBAND DATA TRANSCEIVER SYSTEM

2018· article· en· W2890126382 on OpenAlexvenueno aff
Da Guo, Yong Zhang, XU Guang-nian, Park Hyeongchun

Bibliographic record

VenueInternational Journal of Robotics and Automation · 2018
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsnot available
Fundersnot available
KeywordsWireless broadbandBroadbandTransceiverComputer scienceSpectrum (functional analysis)WirelessScheme (mathematics)Computer networkTelecommunicationsWireless networkPhysicsMathematics

Abstract

fetched live from OpenAlex

This paper investigates the design of a wireless broadband data transceiver (WBDT) system. The WBDT system works on hundreds of narrowband discrete channels. Cognitive radio (CR) technology is adopted to detect idle channels. Discrete channels are aggregated into large frequency bands to provide higher transmission capability than traditional wireless data transceivers (WDTs). Guard gaps are used to avoid interference among WBDTs/WDTs working on adjacent channels according to existing WDT standards. To make full use of the idle channels, a new spectrum aggregation (SA) method, maximum space first assignment (MSFA), is proposed after analysing the performance of the WBDT system. MSFA provides a way to reduce the negative effect of the guard gap. The simulation and analysis results show that the novel system architecture and MSFA improve spectral efficiency.

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: none
Teacher disagreement score0.673
Threshold uncertainty score0.315

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 communication0.0000.001
Open science0.0010.000
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 head0.017
GPT teacher head0.256
Teacher spread0.240 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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