Secure Offloading in NOMA-Enabled Multi-Access Edge Computing Networks
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
Multi-access edge computing (MEC) has been recognized as a promising technology for enhancing the computation capability for next generation wireless networks. This paper studies physical layer security for an MEC network, where multiple users desire to securely offload part of their computation tasks to a base station (BS) simultaneously using non-orthogonal multiple access (NOMA) subject to the potential overhearing of a malicious eavesdropper. The secrecy outage probability (SOP) is adopted as a secrecy performance metric of the computation offloading against eavesdropping attacks. We aim to minimize the total energy consumption of the MEC system subject to an individual SOP constraint for each user. To this end, we jointly design each user’s local computing bits, the transmit power, the secrecy code rates, as well as the successive interference cancellation decoding order at the BS side. As the formulated problem is highly non-convex and challenging to solve, we propose an efficient algorithm based on penalty dual decomposition (PDD) and sequential convex approximation methods to obtain an efficient suboptimal solution. To reduce the computational complexity, we further propose a reverse recursion (RR) algorithm and derive semi-closed-form solutions to the design problem. Numerical results are presented to validate the convergence and the effectiveness of our proposed algorithms. We show that the minimal total energy consumption obtained via either the PDD or RR method approaches the optimal performance of exhaustive search as the task duration increases. It is also demonstrated that the RR algorithm can achieve a comparable performance to that of the PDD algorithm while enjoying a much lower computational complexity.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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