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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".