Cost dynamics of converged optical-wireless networks: enabling low-latency xRANs through a reconfigurable hybrid split
Bibliographic record
Abstract
Future 6G and beyond wireless networks are anticipated to be highly versatile, accommodating a wide range of services, from ultra-low-latency applications like autonomous vehicles and extended reality to enhanced mobile broadband and massive connectivity for the Internet of Things. In tackling this, xRANs (cloud/virtualized/open radio access networks) encounter significant challenges, including automation, interoperability, scalability, reconfigurability, and standardization, within crosshaul (comprising fronthaul, midhaul, and backhaul) networks. Therefore, the development of programmable converged optical-wireless networks with exceptional flexibility is crucial. This study concentrates on the design of integrated optical and wireless networks to achieve the reconfigurability necessary for automation and to fulfill diverse latency requirements. Initially, we analyze the latency contributions from different network segments and traffic factors in the xRAN, followed by a comprehensive examination of the associated cost dynamics. Subsequently, we investigate the feasibility of integrating high-layer and low-layer splits within the same network to achieve different latency levels. Finally, our study delves into the relationship between latency and cost for converged optical-wireless networks with varying mixed split scenarios and throughput levels. Overall, this article aims to assist network planners in making well-informed decisions that balance throughput performance, cost, and latency requirements in upcoming network deployments.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".