5G and Radar Coexistence: Demonstrating Pulsed Radar Interference and 5G Performance Metrics
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".