Intelligent Offloading, Phase-Shift and Trajectory Design in Priority-Aware IRS- and UAV-Assisted MEC Systems
Bibliographic record
Abstract
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has become increasingly popular due to its flexibility. However, the task priority of ground users (GUs) is often neglected and the wireless communications between UAVs and GUs are usually unreliable, leading to poor quality of service (QoS). To address this issue, we propose a priority-aware intelligent reflecting surface (IRS) and UAV-aided MEC network. Subsequently, we formulate a system utility maximization problem by jointly optimizing task offloading, IRS phase shift, UAV trajectory and resource allocation. Then, we decouple the original problem into three subproblems, and design a Dynamic Dual-Weighting (DDW)-based optimization framework (DOF) to effectively solve them. Numerical results show that DOF is superior to other baselines in terms of system utility.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".