Mitigate the Errors of 5G Backhaul in Radio-over-Fiber (RoF) System
Bibliographic record
Abstract
The utilization of fiber systems for transmitting millimeter-wave (MMW) signals has gained significant traction in recent years, particularly for advanced wireless communication applications such as 5G and beyond.This paper explores the integration of wireless and optical networks to enhance performance by reducing the error vector magnitude (EVM) and symbol error rate (SER).The proposed system employs a 2×2 multiple-input multiple-output (MIMO) configuration, which improves coverage and increases capacity through spatial multiplexing.MIMO systems are critical to modern wireless networks, providing superior spectrum and energy efficiency compared to earlier single-input-single-output systems.Following MIMO processing, a millimeter-wave signal is modulated onto the subcarrier using a Mach-Zehnder modulator (MZM).The signal is sent across a (50 and 70)-kilometer optical cable, which boosts data rate and frequency but introduces errors.The proposed method uses a convolutional neural network (CNN) correction to lower these errors and equalize to balance the SER and EVM.VPIphotonics and Python programming are utilized to put the system into practice.The proposed system has a bandwidth of 17 GHz and a data rate of 56.656 Gb/s; the center frequency is 160 GHz with EVM ≈ 3% at the 50 km distance and ≈ 4% at the 70 km fiber channel length.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".