Efficient Dual-Hop Massive MIMO IoT Networks with UAV DF Relaying and Hybrid Beamforming
Bibliographic record
Abstract
This study considers a dual-hop massive multiple-input multiple-output (mMIMO) system, where a decode-and-forward (DF) relay in the form of an unmanned aerial vehicle (UAV) facilitates the transmission of multiple data streams from a base station (BS) to a gateway serving multiple Internet-of-Things (IoT) devices. To maximize the end-to-end throughput in a three-node wireless sensor network (WSN), we investigate a novel joint optimization problem of hybrid beamforming (HBF) and UAV relay positioning in a given deployment span. The study adopts a geometry-based millimeter-wave (mmWave) channel model for both links and utilizes particle swarm optimization (PSO) to optimize the UAV location. The radio frequency (RF) stage is designed to minimize the number of RF chains through the utilization of slow time-varying angular information, while the baseband (BB) stage is designed through singular value decomposition (SVD) of the reduced-dimension effective channel matrix. The illustrative results show that the proposed joint HBF approach enhances energy efficiency compared to full-digital beamforming, and the UAV DF relay, placed via the PSO-based deployment scheme, attains a higher capacity compared to fixed UAV deployment locations.
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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".