Multiantenna UAV-Assisted Hybrid FSO/RF Data Collection for IoT: Optimal Design for Fairness
Bibliographic record
Abstract
Wireless sensors are often deployed in hard-to-reach locations for data collection. Unmanned aerial vehicles(UAVs) can easily fly over such locations and so it is a promising solution to collect data from remote sensors. This paper considers a UAV-assisted hybrid free space optical (FSO)/radio frequency (RF) data collection network for Internet of Things (IoT) applications. In this network, multiple remote sensors transfer information to a UAV with multiple antennas through RF links using time division multiple access (TDMA). The UAV uses decode and forward (DF) protocol to send information to a base station (BS) via FSO link. Successive interference cancellation (SIC) is employed to decode the information at the BS. Multi-agent deep reinforcement learning (DRL) is modified and applied to obtain a near-optimal scheme, for transmit power allocation, to minimize the maximum outage probability of decoding the information from the sensors under dynamic weather condition/UAV state. Numerical results are presented to illustrate the system design tradeoffs. Furthermore, the validity and superiority of our proposed approach is verified by comparing it with exhaustive search and differential evolution algorithms.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".