Digitally Linearized Radio-Over Fiber Transmitter Architecture for Cloud Radio Access Network’s Downlink
Bibliographic record
Abstract
We propose a digitally linearized radio-over fiber (RoF) downlink transmitter architecture for cloud radio access networks (C-RANs), and we demonstrate its proof of principle in the near-millimeter wave (mm-wave) range (24 GHz). Amplification of input radio frequency signal power is commonly adopted to minimize the impact of photodetection noise on the dynamic range at the receiver. Unfortunately, this amplification causes the RoF system to behave nonlinearly, leading to distortions during the electrical-optical-electrical conversion process that degrades the overall signal quality. To overcome this problem and linearize the RoF link, we propose and implement effective digital predistortion (DPD) using a memory polynomial model. Experimentally, comparing the error vector magnitude (EVM) of a 64-quadratic-amplitude modulation (QAM) 20-MHz bandwidth (BW) long-term evolution (LTE) signal modulated onto a 24-GHz carrier with and without linearization, we found a signal quality improvement by 4.2%, resulting in an EVM value of 2%. Broader LTE signals of BWs up to 100 MHz were experimentally tested to achieve EVM values below 3.5% after DPD, both for 64 and 256 QAM. It is worth highlighting that the remote radio head (RRH) unit does not require any frequency up conversion to generate the mm-wave signals and the centralized baseband unit can serve multiple remote RRHs operating at different frequencies as in C-RANs. Our results demonstrate the suitability of the proposed C-RAN transmitter architecture for next generation 5G wireless communication networks.
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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) | 0.002 | 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".