On Multi-User Binary Computation Offloading in the Finite-Block-Length Regime
Bibliographic record
Abstract
The Mobile Edge Computing framework provides small scale devices with the opportunity to offload their computational tasks to computing infrastructure at a network access point. Effective access to this infrastructure is contingent on the appropriate allocation of the available communication resources among the devices wishing to offload their tasks. In previous work, that allocation has been performed using guidance from classical characterizations of the fundamental limits on the rates at which reliable communication can be achieved, which are contingent on asymptotically long communication block lengths. However, the (latency) constraint on the time by which each device expects the results of its offloaded computational task imposes a natural limit on the block length. In this paper we show how a recent characterization of the rate limits in the finite-block-length regime can be incorporated into the problem of communication resource allocation for a K-device binary computational offloading system that employs the time-division multiple access (TDMA) scheme. We develop an efficient algorithm for that problem that is based on a tailored tree-search algorithm for the binary offloading decisions, a successive convex approximation algorithm for the transmission rates of the users, and closed-form solutions for the transmission powers and durations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".