Robust Beamforming and Outage Performance of Uplink Multiuser Satellite-Aerial-Terrestrial Networks With Mixed RF-FSO Channels
Bibliographic record
Abstract
In this paper, we investigate the uplink transmission in a satellite-aerial-terrestrial network (SATN), where an aerial platform acts as an amplify-and-forward relay assisting the communication between multiple users and satellite. The users send messages to the aerial relay via radio frequency (RF) links, which are then forwarded to the satellite through a free-space optical (FSO) link. By assuming that the angle-of-arrival based imperfect channel state information of each user is known at the aerial platform, we propose a beamforming scheme to maximize the minimum average signal-to-interference-plus-noise ratio of the users. Due to the mathematical intractability, we design an iterative algorithm to obtain the optimal beamforming vector for the RF link. Furthermore, by considering that the FSO link experiences the Málaga fading with non-zero boresight pointing error and the RF links follow Nakagami-$m$fading, we derive an analytical expression for the outage probability of the considered SATN. Finally, computer simulation is conducted to validate our theoretical analysis. It is shown that the proposed algorithm can improve the system performance and robustness compared to existing works.
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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 | 0.006 |
| 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) | 0.001 | 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".