Functional Split-Aware Optimal BBU Placement for 5G Cloud-RAN Over WDM Access/Aggregation Network
Bibliographic record
Abstract
Fifth generation (5G) cloud-radio access network (C-RAN) aims at providing better performance and support to various applications with stringent data rate and latency requirements. In C-RAN, the processing units, known as the baseband units (BBUs), are segregated from the individual remote radio heads (RRHs) and moved to a convenient location to serve more than one RRH. This migration leads to an efficient resource allocation and cost-effective solution at the expense of a huge fronthaul traffic between the RRH and BBU hotel. The required fronthaul data rate largely depends on the employed functional split options. In this article, we propose a novel optimal BBU placement with a mixed functional split scheme to combat the fronthaul latency challenge while providing the network with greater flexibility and cost reduction. We introduce an integer linear programming (ILP)-based BBU placement problem with a mixed functional split selection approach to simultaneously minimize the number of BBU hotels and fibers, thereby reducing the network cost. Furthermore, a heuristic algorithm is proposed to solve the proposed model for large network scenarios. The obtained results show that an improvement of 25% and 50% is realized with the proposed scheme over the conventional fixed split option scheme for small and large network scenarios, respectively.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".