Bayesian Optimization Applied to 5G Network Slicing

Layla Roy, Chen Uddin

Open Access Journal of Engineering Applications · doi:10.55555/oajea.2026.5001.032

1. Introduction

Accurate prediction of 5G network slicing is essential for the design, operation, and maintenance of urban transportation networks. The performance of Bayesian optimization was evaluated using standard error metrics and cross-validation. The optimized design achieved a 43% improvement in efficiency over the reference case. The outcomes point to clear priorities for further experimental and field investigation.

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2. Materials and Methods

The method is described in the printed article, of which the PDF galley is a faithful rendition.

3. Results and Discussion

Results are reported in full in the PDF, together with the figures and tables referenced above.

4. Conclusion

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