Fiber-Wireless (FiWi) Broadband Access Networks in an Age of Convergence: Past, Present, and Future
Bibliographic record
Abstract
After describing the beginnings and state of the art of integrated fiber-wireless (FiWi) broadband access networks in great detail, we briefly review recent progress and point to various ongoing research activities, including the design of energy-efficient “green” FiWi access networks, advanced survivability techniques, and integration of wireless and fiber optic sensors, towards realizing adaptable, dependable, and ecoconscious future-proof broadband access networks based on both wireless and shared passive fiber media. Furthermore, we discuss service, application, business, and operation related aspects, which motivate access technology to move into a substantially different direction in the long run than continued capacity provisioning. Given that most 4G cellular mobile network researches so far have been focusing on the achievable performance gains in the wireless front-end only without looking into the details of backhaul implementations and possible backhaul bottlenecks, we identify open key research challenges for FiWi broadband access networks. We explore ways of how they can be deployed across relevant economic sectors other than telecommunications per se, taking major paradigm shifts such as the Third Industrial Revolution, Energy Internet, smart grid, and explosion of mobile data traffic in today’s cellular networks into account.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".