Joint Optimization of UAV Trajectory and User Scheduling in Intelligent Reflecting Surface- Aided Systems with Interfering Nodes
Bibliographic record
Abstract
This paper focuses on maximizing the aggregate throughput in a downlink wireless communication system. The system involves collaboration between an unmanned aerial vehi-cle (UAV) and an intelligent reflecting surface (IRS) acting as a mobile relay between the base stations (BSs) and a set of users. The goal is to optimize the reflecting matrix of the IRS, user scheduling, and UAV trajectory to maintain strong links between the users and the serving BS while suppressing interference from other nodes. Depending on whether the amplitudes of reflecting elements are fixed, we consider both ideal and non-ideal IRS scenarios. To address the mixed-integer non-convex optimization problem with coupled variables, the original problem is decom-posed into three sub-problems, and an alternating optimization algorithm is developed. For the ideal IRS case, the successive convex approximation (SCA) technique is employed to transform the problem into a convex one. In the non-ideal case, a novel algorithm utilizing the direction matrix is proposed to achieve a rank-one solution. Simulation results demonstrate the rapid convergence and improved sum rate performance of the proposed algorithm compared to traditional schemes.
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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 | 0.001 |
| 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".