NOMA Empowered Multi‐Access Edge Computing and Edge Intelligence
Bibliographic record
Abstract
Mobile edge computing (MEC) has been considered as a promising solution for enabling computation-intensive yet latency-sensitive applications at resource-constrained wireless devices. Due to the advanced non-orthogonal multiple access (NOMA) for next-generation wireless access networks, NOMA-empowered MEC enables a flexible and spectrum efficient multi-access task offloading approach in future heterogeneous small-cell networks. In this chapter, we first review the recent advances in NOMA-empowered MEC and edge intelligence. Then, as a concrete design example, we leverage the small-cell dual connectivity in heterogeneous small-cell networks and study a paradigm of dual computation offloading in which an edge-computing user can simultaneously offload partial workloads to a cloudlet server (CS) co-located at the macro base station and an edge server (ES) co-located at a small-cell based-station. To facilitate the multi-user dual computation offloading, we exploit a hybrid NOMA and frequency division multiple access (FDMA) transmission in which the edge users from the NOMA groups for offloading their respective workloads to different ESs at different small-cell base stations. Meanwhile, all users use FDMA for offloading their workloads to the CS at the macro base station. A joint optimization of the users' partial offloading decisions, the hybrid NOMA-FDMA transmission, as well as the processing rate allocations at the ESs and the CS, is formulated to minimize the overall task completion latency. An efficient algorithm is proposed to solve the joint optimization problem. Numerical results are provided to validate the effectiveness and efficiency of our proposed algorithms.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".