Subcarrier and Resource Allocation in Aerial Intelligent Reflecting Surface-Assisted Wireless Networks
Bibliographic record
Abstract
This paper addresses the problem of resource allocation in aerial intelligent reflecting surface (AIRS)-assisted wireless networks, where intelligent reflecting surfaces (IRS) are deployed on flying platforms. Specifically, we investigate the joint optimization of AIRS placement and phase shifts, along with base station subcarrier and power allocation to maximize the system sum rate. To address the problem's non-convexity, an alternating optimization framework is proposed. In this framework, we employ K-means clustering to determine optimal AIRS locations and decompose the problem into two interdependent subproblems: AIRS phase optimization on the one hand and base station subcarrier and power allocation on the other hand, each solved iteratively until convergence is reached. In addition, the successive convex approximation method is used to tackle the non-convexity of AIRS phase shift optimization. Numerical results demonstrate that the proposed approach outperforms benchmark schemes, highlighting the potential of AIRS to extend cellular coverage in challenging scenarios such as emergencies or regions with limited infrastructure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".