URLLC-Enabled by Laser Powered UAV Relay: A Quasi-Optimal Design of Resource Allocation, Trajectory Planning and Energy Harvesting
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Bibliographic record
Abstract
Ultra-reliable and low-latency (URLLC) traffic in the upcoming sixth-generation (6G) systems utilize short packets, signalling a distancing from traditional communication systems devised only to support long data packets based on Shannon's capacity formula. This poses a formidable challenge for system designers and network operators. Many URLLC scenarios involve infrastructure-less unmanned aerial vehicle (UAV)-assisted communications. One of the biggest challenges with UAVs is their limited battery capacity, which can cause abrupt disruption of UAV-assisted communications. To overcome these limitations, we consider URLLC-enabled <i>over-the-air</i> charging of UAV relay system using a laser transmitter. Furthermore, we formulate a non-convex optimization problem to minimize the total decoding error rate subject to optimal resource allocation, including blocklength allocation, power control, trajectory planning, and energy harvesting to facilitate URLLC in such systems. In this regard, given its lower complexity, a novel perturbation-based iterative method is proposed to solve the optimization problem. The proposed method yields optimal blocklength allocation and power control for the two transmission phases, i.e., from the source node to the UAV and from the UAV to the robot acting as a ground station. It also maps the UAV trajectory from the initial position to the final position, and the UAV completes the flight using the laser's harvested energy. It is shown that the proposed algorithm and fixed baseline scheme, named fixed blocklength (FB), yield a similar performance as the exhaustive search in terms of UAV energy consumption. In contrast, fixed trajectory (FT) delivers the worst performance. Simultaneously, the proposed method yields the best performance in terms of the lowest average overall decoding error compared to fixed baseline schemes, including FB and FT, showing the efficacy of the proposed technique.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it