Multiple access partial computational offloading: Two-user case
Bibliographic record
Abstract
The opportunity to offload computational tasks to computing resources embedded in the network infrastructure enables mobile users to expand the scope of the applications that can be processed and to reduce the energy required to complete them. In this paper we seek insight into the impact of the choice of the multiple access scheme that is employed when each of two users has a divisible task that they wish to complete with partial assistance from a computing resource at the access point. In particular, we obtain quasi-closed-form solutions for the partitioning of the problem, the transmission powers and the transmission rates that minimize the sum of the transmission energy and the local computation energy for each user, subject to latency, transmission power and achievable rate constraints. We consider both the case in which the multiple access scheme is unrestricted, and the case of time division multiple access. Our numerical results demonstrate that exploiting the full capabilities of the multiple access channel enables a substantial reduction in the energy expended by the users, especially when the users have significantly different channel gains.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".