Assessing Interference Impacts of 5G C-Band on Radar Altimeter Across Urban and Rural Macrocell Environments
Bibliographic record
Abstract
The implementation of 5G Base Stations (BSs) around airports has raised concerns regarding potential interference with radar altimeter systems. These systems are crucial for computing the altitude above the ground, particularly during landing and other low-altitude operations. 5G systems operating in C-band emit signals within a frequency range that closely aligns with the operational frequency of radar altimeters. Such proximity can interfere with the operation of the radar altimeter, resulting in erroneous altitude measurements or even system failures. This situation could put flight and passenger safety at risk, as accurate altitude measurements are essential for safe takeoff, navigation, and landing, especially in low-visibility conditions. This paper presents a comprehensive comparative assessment of altimeters' performance in the presence of 5G signals, evaluating the impact of Active Antenna Systems (AAS) and Fixed-Beam Sectoral Antennas (SA) within contrasting Urban Macrocell (UMC) and Rural Macrocell (RMC) settings. The results of this study provide critical insights into altimeter robustness against 5G interference, offering recommendations for the optimal altimeter and antenna configuration for effective mitigation of interference risks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".