Fiber‐Wireless (FiWi)‐Enhanced Mobile Networks in the 6G ERA
Bibliographic record
Abstract
Abstract Although the original premise of 5G networks was to enable the Internet of Everything (IoE) services and applications, the current deployments of such networks prove otherwise. Shortcomings of 5G have recently attracted a great deal of attention from both the research community and the industry to define next‐generation 6G systems, as an enabler of a variety of disruptive applications ranging from extended reality (XR) to haptics. In this article, we review the 6G vision, paying particular attention to its underlying human‐centric premise. We then elaborate on the recently emerging concepts of Tactile Internet and Internet of No Things, which are envisioned to be enabled over FiWi‐enhanced low‐latency LTE‐Advanced (LTE‐A) heterogeneous networks (HetNets) using a TDM/WDM 1/10 Gb/s Ethernet passive optical network (PON) backhaul and a Wi‐Fi offloading front end with artificial intelligence (AI)‐enhanced multiaccess edge computing (MEC) servers placed at the optical‐wireless interfaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".