UAV-Aided Aerial Reconfigurable Intelligent Surface Communications With Massive MIMO System
Bibliographic record
Abstract
To capture the advantages of unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) technologies, we propose the use of multiple passive aerial RISs in a massive multiple-input multiple-output (MIMO) network. Each aerial RIS is comprised of a RIS panel attached to a UAV, the intention being to support in extending network coverage from the massive MIMO base station. Compared with stationary RISs, our proposed aerial RISs (termed as UAV-RISs) have the ability to reach more users thanks to the line-of-sight links. Our aim is to maximise the total network throughput by finding the optimal power control coefficients at the base station and the phase shifts of the multiple RISs used in the system. This is jointly solved subject to the power consumption constraints, UAV-RIS deployment, and quality-of-service required at the users. We apply zero-forcing precoding for the beamforming design at the base station, and develop an iterative algorithm based on first-order approximation, block coordinate descent, and alternating optimisation technique. Numerical results demonstrate that our proposed method exhibits low computational-complexity and outperforms benchmark schemes in terms of the total network throughput achieved and improvement for the users with worst-case throughput.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".