C-RAN Uplink Optimization Using Mixed Radio and FSO Fronthaul
Bibliographic record
Abstract
Cloud radio access networks (C-RANs) are a promising architecture for 5G systems in which simple radio units (RUs) fronthaul signal to a central processor (CP) for joint decoding. Although the C-RAN has reduced cost and complexity, high data rate fronthaul links are necessary. In this paper, we investigate the joint design of wireless fronthaul networks using both radio frequency (RF) and radio-over-free-space optical (RoFSO) links in the uplink of a C-RAN. Unlike earlier work which focuses on performance characterization of RF/FSO fronthaul networks, this paper presents a novel optimization approach to jointly design the quantizers for the RF fronthaul links and the amplifier gains of the RoFSO fronthaul links which suffer from clipping distortion. A subset of RUs fronthaul data via radio links using Wyner–Ziv source coding subject to a shared sum capacity constraint, while other RUs employ RoFSO fronthaul which converts the incoming RF receptions to optical signals by analog modulation of a laser. The optimization problem jointly designs both RF fronthaul and RoFSO fronthaul links to maximize the weighted sum user rates. Simulation results of a simple C-RAN using measured weather data for two locations demonstrate that adding RoFSO links results in drastic improvements in end user rates but requires careful design of RF and RoFSO fronthaul links.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".