A Novel Energy-Efficient Transmission Scheme in CO-OFDM Elastic Optical Networks
Bibliographic record
Abstract
The use of orthogonal frequency division multiplexing (OFDM) technology enables an optical transmission system to break the limitation of wavelength grids due to legacy of wavelength division multiplexing. This constructs a flexible and elastic transmission paradigm so as to achieve high spectrum efficiency and flexibility of fiber resource usage. This paper introduces a novel adaptive transmission strategy in elastic coherent optical OFDM transmission systems, aiming to optimize the system operation in terms of energy and spectrum consumptions for a transmission demand with a required data rate. By jointly considering the nonlinear effects of Mach-Zehnder modulator and amplified spontaneous emission noise from optical amplifiers, as well as the performance impairment due to high peak-to-average-power ratio (PAPR) in the electronic domain, we first provide an analytical model on the bit error rate performance for a single-elastic optical transmission line. To achieve an efficient PAPR reduction, we introduce a new method called simplified null switching, which is considered very suitable in the elastic optical transmission systems due to lower computation complexity and little dependence on the channel side information. Based on the analytical model, an optimization problem is formulated based on the proposed analytical model and solved via mathematical programming. Case studies via extensive numerical experiments are conducted to verify the proposed analytical model and gain better understanding on the solutions of formulated optimization problem.
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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 | 0.001 |
| 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 | 0.000 | 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".