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AIRBORNE SAFETY IN THE AGE OF 5G: ASSESSING THE POTENTIAL INTERFERENCE BETWEEN C-BAND AND AERONAUTICAL RADAR ALTIMETER

2023· article· en· W4389739273 on OpenAlexaff
Aisha Elsayem, Haidy Elghamrawy, Ali Massoud, Aboelmagd Noureldin

Bibliographic record

Venue˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · 2023
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsRoyal Military College of CanadaQueen's University
Fundersnot available
KeywordsAltimeterRadarRemote sensingRadar altimeterSoftware deploymentComputer scienceInterference (communication)Range (aeronautics)C bandTelecommunicationsEngineeringAerospace engineeringGeographyChannel (broadcasting)

Abstract

fetched live from OpenAlex

Abstract. The recent deployment of 5G technology in the C-band frequency range has raised concerns regarding potential interference with aeronautical radar altimeters. 5G technology utilizes the C-band in the range of 3.7–3.98 GHz, which partially overlaps with the frequency range used by radar altimeters operating in the range of 4.2–4.4 GHz, resulting in an increased possibility of interference between the two systems. In this study, a comprehensive methodology was employed to conduct an interference analysis, investigating the compatibility between 5G wireless systems and radar altimeters. This involved implementing a realistic scenario that replicated the operational interaction between radar altimeters and 5G systems, with the goal of identifying the impact of 5G networks on radar altimeter performance. This paper outlines the scenarios leading to interference, and suggests feasible methods to overcome this issue.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.023
GPT teacher head0.274
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.

Study designOther design
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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

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