Hybrid NOMA-OMA Transmission Scheduling for Production Efficiency Maximization in Industrial Edge Computing Networks
Bibliographic record
Abstract
We consider a mobile edge computing (MEC) assisted Industrial Internet of Things (IIoT) network, where multiple assembly processing lines in a smart factory are equipped with sensing devices. They sense raw products, generate and offload computing tasks, and finally process the raw products based on the computing results. In this scenario, different positions of the processing machines lead to different priorities and diverse Quality-of-Service (QoS) requirements of tasks. Therefore, how to schedule tasks and allocate the network resources becomes a critical and challenging issue. In this study, we introduce a novel batch-based hybrid nonorthogonal multiple access (NOMA)/orthogonal multiple access (OMA) transmission scheme. The selection between NOMA and OMA schemes is optimized based on the QoS requirements of tasks. Then, we formulate a production efficiency maximization problem with the objective of maximizing the speed of the assembly lines subject to the deadline constraints of offloading and computing procedures. To this end, a two-layer decomposition method is used to decompose the formulated problem into two subproblems. Furthermore, we utilize a bisection searching method to approximate the optimal solution, and propose an efficient method to determine the feasibility of the top-layer subproblem. Simulation results demonstrate the significant performance improvement of our proposed method. In specific, the production efficiency is enhanced by 525% in comparison with pure NOMA scheme.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".