Joint Trajectory Design, Task Data, and Computing Resource Allocations for NOMA-Based and UAV-Assisted Mobile Edge Computing
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
Mobile edge computing (MEC) has been considered as a promising technique to address the explosively growing computation-intensive applications. Thanks to the flexibility of the unmanned aerial vehicles (UAVs), the UAV-assisted MEC can serve mobile terminals (MTs) effectively, i.e., the computing server installed on the UAV can flexibly change its location to serve MTs. Moreover, since non-orthogonal multiple access (NOMA) is able to accommodate massive connectivity, the NOMA-based and UAV-assisted MEC can provide flexible computing services for MTs in large-scale access networks (e.g., sensor networks and Internet of Things). However, due to the diversity of the UAV's trajectory and the interference among MTs introduced by NOMA, the performance (e.g., energy consumption and delay) of the NOMA-based and UAV-assisted MEC system is adversely affected. Therefore, in this paper, we formulate an optimization problem to minimize the largest energy consumption among MTs by jointly optimizing the trajectory, task data and computing resource allocations, and then propose an iterative algorithm to solve the optimization problem. Furthermore, to minimize the largest energy consumption among MTs with lower complexity, we propose a fixed point service scheme and optimize the location of the fixed point. The simulation results show that the proposed optimization algorithms can effectively reduce the largest energy consumption among MTs and ensure the fairness among MTs.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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