Machine learning techniques for spatiotemporal traffic prediction in 5G cellular networks

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Opis bibliograficzny
Wydane w:	SN Applied Sciences vol. 7, no. 10 (Oct 2025), p. 1047
1. autor:	Hussien, Alaa A.
Kolejni autorzy:	Nashaat, Heba, Abdel-Kader, Rehab F.
Wydane:	Springer Nature B.V.
Hasła przedmiotowe:	Resource allocation Wireless networks City centres Datasets Deep learning Forecasting Trends Communication Pattern recognition Communications traffic Artificial neural networks Social networks Digital transmission Computer applications Machine learning Time series Cellular communication Energy consumption Internet of Things Efficiency Performance measurement Artificial intelligence Internet access Internet Network management systems Predictions Network reliability Traffic Neural networks Computational efficiency Connectivity Real time Computing time Economic
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