Resource Allocation for Low-Latency Mobile Edge Computation Offloading in NOMA Networks
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Bibliographic record
Abstract
In this paper, we investigate the resource allocation for mobile edge computation offloading in non-orthogonal multiple access (NOMA) cellular networks. Leveraging NOMA, the massive connectivity can be supported to enable multiple cellular users to simultaneously upload their computation-intensive tasks on the same orthogonal resources, which improves spectral efficiency and reduces transmission delay. However, the co-channel interference in non- orthogonal spectrum sharing may potentially degrade the achievable rate of offloading computation tasks. Moreover, the overall delay of all cellular users in finishing computation offloading will increase if the computation resources at the edge server are not properly allocated. To minimize the maximum overall delay of all users, we formulate an optimization problem that jointly allocates communication resources and computation resources. Due to the non-convexity of the primal problem, we divide it into three subproblems. By exploiting their specific structures, an efficient algorithm is designed to obtain the suboptimal solution with low computational complexity. Simulation results are presented to demonstrate that our proposed algorithm can effectively reduce the overall delay of cellular users and fully exploit the benefit of NOMA on spectral efficiency, especially when the number of users is large.
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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 | 0.000 |
| 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