An optimization framework for energy-efficient elastic optical transmission systems
Bibliographic record
Abstract
The use of Orthogonal Frequency Division Multiplexing (OFDM) technology helps an optical transmission system to break the limitation of wavelength grids by Wavelength Division Multiplexing (WDM), in which a flexible and elastic transmission paradigm is created, so as to achieve better spectrum efficiency and flexibility of the fiber resources. This paper investigates a novel adaptive transmission strategy in OFDM based optical transmission systems, aiming to minimize the transmission cost for a bulk data transfer request in terms of energy consumption and bandwidth usage without violating the constraints on the transmission delay and bit error rate (BER) at the receiver. By considering nonlinear effects of Mach-Zehnder modulator (MZM) and amplified spontaneous emission (ASE) noise from optical amplifier, a novel analytical model for the BER expression is provided and the corresponding optimization problem is formulated. In particular, the proposed formulation is the first in the literature that considers the effect of Peak-to-Average Power Ratio (PAPR) on both electrical and optical domain signals. By adaptively choosing the number of subcarriers, modulation level, and coupled laser power, the transmission cost for a given request can be minimized.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".