Integrated User Association, Computation Offloading, Resource Allocation, and UAV Trajectory Control Against Jamming for UAV-Based Wireless Networks
Bibliographic record
Abstract
In this paper, we address optimum design of uncrewed aerial vehicle (UAV)–based wireless networks with a focus on computation offloading in the presence of an active aerial attacker. Our design aims to minimize the maximum computation time among the tasks of ground users while satisfying the energy consumption requirements. To this end, we propose a joint optimization problem of partial computation offloading, ground user association, multiple UAVs trajectory control, computation resource, and sub-channel assignment. To tackle the underlying non-convex mixed-integer nonlinear optimization problem, we use the alternating optimization approach to iteratively solve the five sub-problems, namely, user-UAV association, user scheduling, partial offloading control and bit allocation over time slots, computation resource and sub-channel assignment, and UAV trajectory control until convergence. Moreover, the successive convex approximation method is employed to solve the non-convex sub-problems and improve the resilience of the system against jammer attacks. Additionally, we propose low-complexity algorithms to solve the involved sub-problems. Via extensive numerical studies, we illustrate the effectiveness of our proposed design compared to baselines under the impact of an aerial attacker.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".