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5G and Radar Coexistence: Demonstrating Pulsed Radar Interference and 5G Performance Metrics

2025· article· W7117874208 on OpenAlex
Eric Forbes, Gustave Anderson, Ying Wang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsRadarTelecommunications linkInterference (communication)Man-portable radarKey (lock)WirelessFire-control radarPulse repetition frequencyRadar engineering detailsContinuous-wave radar

Abstract

fetched live from OpenAlex

The escalating demand for wireless spectrum necessitates more effective coexistence strategies between commercial 5G networks and incumbent radar systems. Unlike traditional approaches that permit spectrum access to only one system at a time, this demonstration directly investigates the real time performance impacts on 5G communications under simultaneous radar interference. Our approach leverages a low cost, flexible, and modular experimental platform integrating commercial user equipment (UE) and software defined radios (SDRs). The demonstration highlights the effects of pulsed radar interference on key 5G metrics including UE throughput, uplink (UL) SINR, and downlink (DL) SNR, through live spectrum visualization and performance monitoring. Iterative test cycles allow systematic variation of radar parameters such as pulse repetition frequency (PRF) and duty cycles, fostering interactive discussions on practical coexistence scenarios. Preliminary results illustrate that simultaneous radar and 5G operation is feasible under certain conditions, providing valuable insights into optimizing future spectrum sharing deployments.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.014
GPT teacher head0.234
Teacher spread0.219 · 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

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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