Energy-efficient radio-over-fiber system for next-generation cloud radio access networks
Bibliographic record
Abstract
The paper proposes a novel adaptive radio-over-fiber (RoF) system for next-generation cloud radio access network (C-RAN), aiming to optimize the operation cost in terms of power consumption while maintaining required data rate. By jointly considering the nonlinear distortion from Mach-Zehnder modulator (MZM) and high power amplifier (HPA) due to high peak-to-average-power ratio (PAPR) in the electronic domain, we first provide a 2×2 multiple-input mulitple-output orthogonal frequency division multiplexing (MIMO-OFDM) baseband model on electrical SNR (ESNR) for a single RoF transmission line. To take the modulation levels into consideration, we provide the optical signal to noise ratio (OSNR) analysis that jointly considers the electrical SNR (ESNR) model and the non-linear effect of the optical transmission. This optical SNR (OSNR) analysis result is further used in the subsequent power consumption model for both the downlink and uplink of the considered RoF transmission system. Case studies via simulation and numerical experiments are conducted to verify that the proposed RoF system not only can reach the lowest power and spectrum consumptions at same time, but also consumes considerably less power than current RoF system.
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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".