Optimizing Downlink Communication in a Multi-STAR-RIS-Assisted Multi-Antenna AAV Network
Bibliographic record
Abstract
Autonomous aerial vehicles (AAVs) are crucial for enhancing global connectivity, but the AAV transmissions are vulnerable to limited onboard energy and signal blockages in dense urban areas. Reconfigurable intelligent surfaces (RISs) can mitigate blockages, while allowing for fewer on-board antennas and reduced energy consumption for AAVs. In this letter, our objective is to study whether the distributed RIS network can achieve the gains comparable to a multi-antenna AAV, thus enabling reduced on-board energy consumption. To this end, we develop a framework to optimize multi-user scheduling, amplitude and phase shifts of simultaneous transmission and reflection (STAR)-RISs, and AAV beamforming in a distributed STAR-RIS-assisted multi-antenna AAV network. In this context, the sum-rate maximization problem is non-convex due to the interdependence among beamforming, phase shifts, and scheduling variables. To solve the problem, we decompose it into three sub-problems. We use semi-definite programming and integer constraint relaxation to solve the phase shifts and scheduling optimization, and apply standard successive convex approximation for beamforming optimization. We then employ alternating optimization to iterate until convergence is achieved. Our findings offer insights into scenarios where a distributed STAR-RIS network can achieve performance gains comparable to a multi-antenna AAV network.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".