Optimal ARIS Deployment for Network Throughput Maximization in Multi ARIS-assisted Networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we jointly optimize power allocation and Aerial Reconfigurable Intelligent Surface (ARIS) phase shift along with the optimal ARIS deployment in multi-ARIS-enabled downlink communication networks with an objective to maximize the total network throughput. We solve the ARIS deployment problem using the low complexity Deep Neural Network (DNN)based solution followed by Alternating Optimization (AO)-based solution for joint power allocation and ARIS phase shift optimization. Extensive numerical investigations are performed to evaluate the effectiveness of the proposed solution in various scenarios, including the varying number of ARIS elements, the varying ARIS clusters, the varying number of users per cluster, and the field radius. The simulation results show that the proposed solution with joint power and ARIS phase shift optimization outperforms the equal power allocation technique along with both random phase shift optimization and optimal phase shift optimization in all the scenarios mentioned above.
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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 | 0.000 | 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 | 0.000 |
| 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 it