Next Generation Multi-Access Edge-Computing Fiber-Wireless-Enhanced HetNets for Low-Latency Immersive Applications
Bibliographic record
Abstract
Next generation optical access networks have to cope with the contradiction between the intense computation and ultra-low latency requirements of the immersive applications and limited resources of smart mobile devices. In this chapter, after presenting a brief overview of the related work on multi-access edge computing (MEC), the authors explore the potential of full and partial decentralization of computation by leveraging mobile end-user equipment in an MEC-enabled FiWi-enhanced LTE-A HetNet, by designing a two-tier hierarchical MEC-enabled FiWi-enhanced HetNet-based architecture for computation offloading, which leverages both local (i.e., on-device) and nonlocal (i.e., MEC/cloud-assisted) computing resources to achieve low response time and energy consumption for mobile users. They also propose a simple yet efficient task offloading mechanism to achieve an improved quality of experience (QoE) for mobile users.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".