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Record W1899102036 · doi:10.1109/icupc.1998.733018

Highly structured rosette antenna arrays for wireless multimedia systems

2002· article· en· W1899102036 on OpenAlexaff
J. Sydor, James Duggan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsMicrocellWirelessComputer scienceChannel (broadcasting)Antenna (radio)Computer networkTelecommunications

Abstract

fetched live from OpenAlex

Wireless multimedia systems that use 3-7 GHz spectrum will require significant frequency reuse and packing of coverage zones to achieve the data delivery densities necessary to support large scale multimedia services. Based on service up-take rates of 1-6% by the households located within an urban core, data delivery densities of 100-600 MB/S/km/sup 2/ need to be sustained to support wireless services such as video-on-demand and high speed data dissemination. We examine whether it is possible to achieve such densities by using narrow sector antenna arrays, which when arranged around a hub as oblong microcells, form rosette-like macrocells (rosettes) providing a full 360 degree coverage. Such architectures are interesting because oblong microcells allow interlacing and offer the possibility of C/I control by geometrical orientation and alignment of microcell and subscriber antennas. The rosettes studied here have assignments of co-frequency channels in every N/sup th/ microcell, where N is the number of distinctly different sets of co-frequency channel groups. Microcells are spaced uniformly around a hub with each microcell being placed in that region of low sidelobe radiation generated by the adjacent co-frequency microcells.

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: Methods · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.000
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 head0.039
GPT teacher head0.269
Teacher spread0.230 · 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
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

Citations1
Published2002
Admission routes1
Has abstractyes

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