Multiagent UAV-Aided URLLC Mobile Edge Computing Systems: A Joint Communication and Computation Optimization Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article, we consider a multiagent unmanned aerial vehicle (UAV)-aided system employing mobile edge computing (MEC) servers to satisfy the requirement of ultrareliable low latency communications (URLLCs) in intelligent autonomous transport applications. Our MEC architecture aims to guarantee quality-of-service (QoS) by investigating task offloading and caching implemented in the nearby UAVs. To enhance system performance, we propose to minimize the network energy consumption by jointly optimizing communication and computation parameters. This includes decisions on task offloading, edge caching policies, uplink transmission power, and the processing rates of users. Given the nonconvex nature and high computational complexity of this optimization problem, an alternating optimization algorithm is proposed, where the three subproblems of caching, offloading, and power allocation are solved in an alternating manner. Our simulation results demonstrate the efficacy of the proposed method, showcasing significant reductions in user energy consumption and optimal resource allocation. This work serves as an initial exploration of the transformative potential of cutting-edge technologies, such as UAVs, URLLC, and MEC, in shaping the future landscape of intelligent autonomous transport systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| 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 it