On the Optimization of Laser-Powered UAV-Assisted Backscatter Communications
Bibliographic record
Abstract
This paper studies the joint trajectory and resource allocation problem for a laser-powered unmanned aerial vehicle (UAV) assisted data aggregation framework. The considered system incorporates semi-passive nodes that establish their wireless communication links with the UAV via bistatic backscatter communications enabled by a battery-powered power beacon source. We aim to optimize the UAV trajectory while minimizing the laser energy consumption throughout the whole flight by tuning the laser power and the power beacon radiated temporal power profiles. Towards this aim, we first adopt path discretization to approximate the optimal control problem of interest into a non-linear programming one whilst accounting for the UAV dynamics constraints and available power budget restrictions. Then, we solve the problem by successive convex approximation (SCA) over the joint set of variables and determine the problem feasibility by a data collection maximization problem. Moreover, we propose a low-complexity solution for the feasibility problem. Finally, the conducted simulations show that the proposed algorithm provides 50% collected data increase and almost 50% laser energy reduction under different operation conditions such as laser station position, maximum laser output power, start and end points of the trajectory, power beacon battery capacity, and the permissible flight length.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".