Multiple UAV-Assisted Cooperative DF Relaying in Multi-User Massive MIMO IoT Systems
Bibliographic record
Abstract
This work considers a multi-user massive multiple-input multiple-output (MU-mMIMO) Internet-of-Things (IoT) system, where multiple unmanned aerial vehicles (UAVs) operating as decode-and-forward (DF) relays connect the base station (BS) to a large number of IoT devices. To maximize the total achievable rate, we propose a novel joint optimization problem of hybrid beamforming (HBF), multiple UAV relay positioning, and power allocation (PA) to multiple IoT users. The study adopts a geometry-based millimeter-wave (mmWave) channel model for both links and utilizes sequential optimization based on K-means UAV-user association. The radio frequency (RF) stages are designed based on the slow time-varying angular information, while the baseband (BB) stages are designed utilizing the reduced-dimension effective channel matrices. The illustrative results show that multiple UAV-assisted cooperative relaying systems outperform a single UAV system in practical user distributions. Moreover, compared to fixed positions and equal PA of UAVs and BS, the joint optimization of UAV location and PA substantially enhances the total achievable rate.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".