Multi-UAV Enabled Integrated Sensing and Wireless Powered Communication: A Robust Multi-Objective Approach
Bibliographic record
Abstract
The integration of sensing and communication functions is a fundamental paradigm for enabling robust and resource-efficient unmanned aerial vehicle (UAV)-assisted wireless networks. This paper addresses the optimization of integrated sensing and communication (ISAC) systems in UAV-aided wireless networks featuring wireless power transfer (WPT). We propose a novel architecture wherein multiple UAV-based radars concurrently serve multiple clusters of energy-limited communication users while performing sensing tasks. Initially, radars sense the environment, enabling users to harvest and store energy from radar transmissions. Subsequently, this stored energy facilitates uplink communication from nodes to UAVs. Our multi-objective design problem optimizes UAV trajectories, radar transmit waveforms, radar receive filters, time scheduling, and uplink powers to enhance both radar and communication system performance. Incorporating user location uncertainty, we formulate a robust non-convex optimization problem. To address this, we employ an alternating optimization approach, complemented by fractional programming, S-procedure, and majorization-minimization (MM) techniques. Numerical examples illustrate the efficacy of our method across diverse scenarios.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".