ANFIS-based Regression for vBS Computing Usage Prediction in Open Radio Access Networks
Bibliographic record
Abstract
The 5 G networks and their Open Radio Access Networks (O-RAN) architecture face significant challenges in resource management due to their extended flexibility and technological diversity. O-RAN’s open, disaggregated architecture creates a heterogeneous environment that requires effective integration and analysis of data from various components. In this context, accurately predicting the computational utilization of virtual base stations (vBS) emerges as a critical challenge, essential for optimizing resource allocation and addressing the dynamic demands of 5 G and 6 G O-RAN networks. Traditional forecasting techniques often struggle with the complexity and variability of data in this scenario, necessitating advanced AI and ML approaches. We propose an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for multi-target regression to predict CPU utilization in vBS. By combining neural networks and fuzzy logic, ANFIS enhances both prediction accuracy and interpretability, making it ideal for the complexities of O-RAN 5G/6G networks. Our model, tested on publicly available O-RAN datasets, outperforms traditional ML methods. These results position ANFIS as an effective tool for optimizing resource management and enabling transparent decision-making in 5G/6G infrastructures, providing valuable support for network operators seeking efficient and scalable solutions in the evolving ORAN landscape.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".