BackHauling-as-a-Service (BHaaS) for 5G Optical Sliced Networks: An Optimized TCO Approach
Bibliographic record
Abstract
Due to their initial overestimation of demand, many network operators are over-provisioning their infrastructure. Overdesigned networks vastly increase operational costs without generating expected revenues. In particular, high-density cell architecture in future 5G networks will face big technical and financial challenges due to avalanche of traffic volume and massive growth in connected devices. Planning scalable 5G mobile backhaul (MBH) transport networks becomes one of the most challenging issues. However, existing planning solutions are no longer appropriate for coming 5G requirements. New 5G MBH architecture emphasizes on multitenancy and network slicing, which requires new methods to optimize MBH planning resource utilization. In this paper, we introduce an algorithm based on a stochastic geometry model (Voronoi Tessellation) to define backhauling zones within a geographical area and optimize their estimated traffic demands and MBH resources. Then, we propose a novel method called backhauling-as-a-service (BHaaS) for network planning and total cost of ownership (TCO) analysis based on “you-pay-only-for-what-you-use” approach. Finally, we enhanced the BHaaS performance by introducing a more service-aware method called trafficprofile-as-a-service (TPaaS) to further drive down the costs based on yearly activated traffic profiles. Results show BHaaS and TPaaS may control and enhance 22% of the project benefit compared to traditional TCO model.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".