The Escape of Sisyphus or What “Post NG-PON2” Should Do Apart from Neverending Capacity Upgrades
Bibliographic record
Abstract
The primary design goal of (r)evolutionary NG-PON1&2 was the provisioning of an ever increasing capacity to cope with video-dominated traffic and handle the explosion of mobile data traffic by means of offloading. Recently, however, questions on the future of “post NG-PON2” have surfaced whether to shift its research focus to business and operation related aspects and move access technology into a substantially different direction than continued capacity upgrades. In fact, recent studies indicate that ultimately the major factor limiting the performance of 4G mobile networks is latency rather than capacity of the backhaul. In this paper, we review recently proposed low-latency techniques for NG-PONs that require architectural modifications at the remote node or distribution fiber level and highlight advanced network coding and real-time polling based low-latency techniques that can be implemented in software, enable NG-PONs to carry higher traffic loads and thereby extend their lifetime, and maintain the passive nature of existent optical distribution networks. Furthermore, we elaborate on emerging trends and open challenges for future post NG-PON2 research. To better understand their true potential, we put them into a wider non-technical and historical perspective leading up to a sustainable Third Industrial Revolution (TIR) economy and its underlying Energy Internet.
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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 | 0.002 | 0.002 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".