Measurement-Based Analysis of 5G Cellular Network Interference on Radar Altimeters and Joint Power-Angle Control Mitigation Strategy
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it