Measurement-Based Analysis of 5G Cellular Network Interference on Radar Altimeters and Joint Power-Angle Control Mitigation Strategy
Bibliographic record
Abstract
The global deployment of 5G technology, particularly in the C-band (3.4–4.2 GHz), has introduced critical interference challenges for safety-critical avionics, such as radar altimeters (RAs), which are essential for safe aircraft navigation. This study investigates the impact of 5G base stations (BS) on RAs through airborne measurements conducted using a helicopter above and around a 5G BS. Signal strength and interference patterns were captured at various altitudes and distances, illustrating how out-of-band (OOB) and spurious emissions from 5G signals can interfere with RA readings. An optimization model was developed to minimize interference through joint power and angle control at the BS, balancing the trade-off between 5G signal quality and aviation safety. The proposed method significantly reduces interference while maintaining a minimum quality of service (QoS) for 5G users. Simulation results demonstrate that, compared to power-only control, joint control improves signal quality by approximately 15 dB while reducing interference, suggesting a viable solution for harmonizing 5G and aviation requirements. This work offers a novel approach to mitigating 5G interference on RAs and provides valuable insights for improving the coexistence of these critical systems.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".