Spherical Array-Based Joint Beamforming and UAV Positioning in Massive MIMO Systems
Bibliographic record
Abstract
This work considers a spherical array (SA)-based dual-hop massive multiple-input multiple-output (mMIMO) system using an unmanned aerial vehicle (UAV) as an amplify-and-forward (AF) relay between the base station (BS) and Internet of Things (IoT) gateway. We propose a particle swarm optimization (PSO)-based UAV deployment technique to maximize the total achievable rate by considering joint optimization of UAV location, hybrid beamforming (HBF) at two terminal nodes, and analog beamforming/combining at the UAV relay. Additionally, we employ singular value decomposition (SVD) of the channel matrices to form the transmit and receive radio frequency (RF) stages of the UAV relay, and an orthogonal matching pursuit (OMP)-based algorithmic approach to the HBF for the BS and the gateway. The illustrative results show that our proposed joint beamforming scheme for two distinct SA configurations significantly improves spectral and energy efficiencies, and outperforms uniform rectangular arrays (URAs).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".