PSO-Based Joint UAV Positioning and Hybrid Precoding in UAV-Assisted Massive MIMO Systems
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Bibliographic record
Abstract
This work studies the joint design of hybrid pre-coding (HP) and optimal positioning of unmanned aerial vehicle (UAV) relay in a millimeter-wave (mmWave) multi-user massive multiple-input multiple-output (MU-mMIMO) systems to maximize the spectral and energy efficiencies. The UAV operates as a flying wireless relay, expanding a base station’s coverage and delivering capacity boost to a group of users/devices that are obscured by obstructions. We explore the geometry-based mmWave channel model for the UAV-User link and propose joint HP and UAV positioning scheme (JHPP). In particular, the RF beamformer is designed using singular value decomposition (SVD) of channel matrix by incorporating users’ angle-of-departure (AoD) information to reduce the number of radio frequency (RF) chains, and the baseband (BB) precoder is designed using regularized zero-forcing (RZF) technique to mitigate MU interference. Then, using a particle swarm optimization-based location algorithm (PSO-L), a constrained optimization problem with the goal of maximizing the achievable sum-rate (ASR) is constructed for the optimal UAV placement in the given search space. Illustrative results show that the integration of a UAV relay considerably enhances the performance of mmWave MU-mMIMO systems when the BS is remote. Moreover, compared to UAV random placement in the given flying span, PSO-L based UAV positioning has higher spectral/energy efficiency. Finally, the use of a hemispherical array (HSA) configuration at UAV relay can further increase the performance when compared to uniform rectangular array (URA).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it