TY  - JOUR
AU  - Roy, Layla
AU  - Uddin, Chen
TI  - Bayesian Optimization Applied to 5G Network Slicing
JO  - Open Access Journal of Engineering Applications
PY  - 2026
VL  - 50
IS  - 1
SP  - 701
EP  - 721
AB  - 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.
KW  - 5G network slicing
KW  - Bayesian optimization
KW  - renewable energy
KW  - flood forecasting
KW  - genetic algorithm
KW  - energy efficiency
SN  - 3105-7004
DO  - 10.55555/oajea.2026.5001.032
UR  - https://journal.bsaps.com/article/10296
ER  -