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Development of Recommended Practices for Synthetic Aperture Channel Sounding

2024· article· en· W4401719064 on OpenAlexaff
David G. Michelson, Xin Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDepth soundingComputer scienceChannel (broadcasting)Channel soundingSynthetic aperture radarRemote sensingGeologyTelecommunicationsOceanographyMIMO

Abstract

fetched live from OpenAlex

Wireless channel sounding using synthetic aperture or virtual array techniques is an emerging area that is not yet technically mature. Most early work was motivated by COST 259 and similar work that focused on directional channels and was conducted in personal communications bands between 850 MHz and 6 GHz. More recently, efforts have focused to systems used to characterize millimetre-wave channels and have been motivated by efforts to extend 3GPP-based systems into frequency bands above 24 GHz. While synthetic or virtual array channel sounders are free from the challenges associated with mutual effects between adjacent elements and much less expensive to implement than fully populated arrays, they can only be used to characterize environments that are effectively static for the duration of the scan. Significant improvements in performance can be realized by optimizing the array lattice and improving the signal processing techniques used to extract direction of arrival information. However, past efforts to use virtual array techniques by the wireless channel sounding research community have rarely reflected the considerable knowledge and insights developed by the virtual array research community.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.672
Threshold uncertainty score0.183

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 head0.038
GPT teacher head0.290
Teacher spread0.251 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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