Gao et al., 2022 - Google Patents
A multi-scale ensemble learning model for cellular traffic predictionGao et al., 2022
- Document ID
- 852047728890194734
- Author
- Gao C
- Feng T
- Wang H
- Jin D
- Feng J
- Wang X
- Zhu L
- Deng C
- Publication year
- Publication venue
- GLOBECOM 2022-2022 IEEE Global Communications Conference
External Links
Snippet
With the widespread use of mobile devices in recent years, accurate prediction of base station traffic is vital for maintaining a good quality of mobile network services. In this paper, we propose an ensemble learning framework to predict the cellular traffic of base stations …
- 230000001413 cellular 0 title abstract description 7
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
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