Delay-Minimization Nonorthogonal Multiple Access Enabled Multi-User Mobile Edge Computation Offloading
Why this work is in the frame
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Bibliographic record
Abstract
The significant advances of cellular systems and mobile Internet services have yielded a variety of computation intensive applications, resulting in great challenge to mobile terminals (MTs) with limited computation resources. Mobile edge computing, which enables MTs to offload their computation tasks to edge servers located at cellular base stations (BSs), has provided a promising approach to address this challenging issue. Considering the advantage of improving transmission efficiency provided by nonorthogonal multiple access (NOMA), we propose an NOMA-enabled computation offloading scheme, in which a group of MTs offload partial of their computation workloads to an edge server based on the NOMA transmission. After finishing all MTs’ offloaded computation workloads, the edge server sends the computation results back to the MTs based on NOMA. We aim at minimizing the overall delay for completing all MTs’ computation requirements, which is achieved by jointly optimizing the MTs’ offloaded computation workloads, and the uploading duration for the MTs to send their computation workloads to the BS, and the downloading-duration for the BS to send the computation results back to the MTs. Despite the nonconvexity of the joint optimization problem, we exploit its layered structure and propose an efficient algorithm to compute the optimal offloading solution. Numerical results are provided to validate the accuracy and efficiency of our proposed algorithm and show the performance advantage of our NOMA-enabled computation-offloading scheme.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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