Computation Offloading and Resource Allocation Optimization for Mobile Edge Computing-Aided UAV-RIS Communications
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
The concept of Mobile Edge Computing (MEC) has been recently highlighted as a key enabling technology for the deployment of sixth-generation (6G) wireless network services. On the other hand, the possibility of combining Unmanned Aerial Vehicles (UAV) with Reconfigurable Intelligent Surfaces (RIS) has also been recognized as a powerful communication paradigm able to provide improved propagation characteristics of wireless communication channels, as well as increased capacity and extended coverage. Then, the possibility of merging the characteristics of such a communication paradigm with the one provided through MEC represents a valid solution to fulfill the main requirements of 6G networks. In this paper, we consider the combination of computation offloading and resource allocation in an MEC-based system where the MEC server is hosted by a massive MIMO base station, which serves multiple macro-cells assisted by a UAV-equipped RIS. In this context, we focus on minimising the latency for executing tasks of all user equipment (UE) within the considered scenario. To tackle this problem, we formulate an optimisation problem that jointly optimises computation offloading from user equipment (UE) towards the MEC server, and communication resources in the underlying UAV-assisted and RIS-aided network. The extensive simulation results demonstrate how the proposed method outperforms in terms of providing reduced latency for the considered system when compared with other conventional schemes.
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 | 0.001 |
| Open science | 0.001 | 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