RIS-Aided Communication in CF-mMIMO System With Co-Existing Aerial and Ground Users: Performance Analysis and Optimization
Bibliographic record
Abstract
Future wireless networks will seamlessly integrate unmanned aerial vehicles (UAVs) and ground user equipment (GUEs). However, UAVs in terrestrial networks often experience poor signal reception due to access point (AP) antenna down-tilt, optimized for GUEs. To address this, we propose a downlink (DL) transmission framework leveraging reconfigurable intelligent surfaces (RISs) in a cell-free massive multiple-input multipleoutput (CF-mMIMO) system with co-existing UAVs and GUEs. The framework enhances UAV communication while maintaining GUE quality-of-service (QoS). Using signal-to-interference-plusnoise ratios (SINR) as the performance metric, we formulate a max-min SINR optimization problem to jointly optimize power allocation and RIS phase-shifts. Two novel algorithms are developed to solve this problem. Simulation results demonstrate significant UAV DL performance gains compared to benchmark schemes and reveal the impact of RIS parameters, such as tilt and element arrangement, as well as UAV density, on system performance. These findings offer valuable insights into the deployment and optimization of RIS in UAV-integrated CF-mMIMO systems.
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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 | 0.001 | 0.002 |
| 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".