Ethernet passive optical network-long-term evolution deployment for a green access network
Bibliographic record
Abstract
Access networks contribute a significant portion of the energy consumption of the telecom network. In this study, the authors propose a distributed bandwidth allocation signalling framework for a green hybrid Fibre-Wireless (Fi-Wi) access network, which is based on the convergence of ethernet passive optical network and third-generation partnership project-long-term evolution-advanced technologies. According to the proposed framework, the Fi-Wi network is deployed by pairing joint optical network unit-base station (ONU-BS) nodes. An ONU-BS that is experiencing light load from the end-users sends a SLEEP signal to the optical line terminal (OLT), and switches to the stand-by mode whereas its BS module forwards the traffic to the peer ONU-BS. The peer ONU-BS keeps buffering the requests destined to the sleeping ONU-BS, and sends REPORTs for the corresponding packets to the OLT. Through simulations, the authors generate various traffic profiles, and show that the proposed framework provides significant amount of energy savings at the ONUs when compared with regular operation mode. Furthermore, the simulation results also show that average packet delay and average packet loss do not increase significantly by utilising the active ONU-BS devices whereas their corresponding peers are sleeping.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".