Capacity Analysis of UAV Communications Under the Non-Ideal Transceiver Effects
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) have emerged as promising solutions to overcome the challenges that face traditional terrestrial communication. Not only are they reliable and cost-effective, but they can also be considered green strategies with inherent benefits such as diversity, flexibility, and altitude adaptability. This work analyses the effects of hardware impairments (HWIs) on the UAVs and the ground station (GS) communication system where the UAV moves in a random three-dimensional trajectory. In this regard, the average ergodic capacity of the system is derived by considering the Rician fading channel conditions between the UAV and the GS. We consider the average over a random three-dimensional trajectory movement including the angle of arrival and the distance between the UAV and the GS. We also provide an asymptotic analysis when the transmit power of the UAV and the number of GS antennas become exceptionally large. Extensive MATLAB simulations are provided to validate the gained results.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".