Power saving clusters for energy-efficient design of fiber-wireless access networks
Bibliographic record
Abstract
Wireless and optical broadband access technologies are foreseen to converge by combining the high transmission capacity of the optical communications with the flexibility and the ubiquitous nature of the wireless communications in order to satisfy the growing end-user demand for bandwidth. This hybrid technology eliminates the cost of running fiber to the destination by allowing the fiber deployment until a certain point from where wireless base stations take over to provide service to the end-user. Despite handling the growing end-user demand, new telecommunication technologies are required to be energy efficient. Recently, there is an increasing interest to reduce the energy consumption of the Information and Communications Technology (ICT) sector and the ICT related CO2emissions. In this paper, we propose an energy efficient design scheme for a fiber-wireless network consisting of a WDM-PON in the optical back-end and a 4G broadband access technology-enabled wireless front-end, e.g. Long-Term Evolution (LTE) or WIMAX. The proposed design scheme uses the average load profiles on the WDM-PON segments and attempts to form power saving clusters (PSCs) which are fiber rings interconnecting several hybrid (fiber-wireless) access networks. Each PSC enables one or more OLTs to sleep and distributes the backlogged traffic among the active segments in the ring. The proposed scheme aims to maximize the number of sleeping segments, and consequently maximize the power saving. Through simulations, we show that the proposed scheme leads to a power saving between 20% and 45% with a maximum of 3.5% increase in the fiber deployment cost by running the interconnection fibers to form PSCs.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".