Data driven energy efficient base station sleep control for 5G systems

The objective of this project is to develop a software system which can optimally control the base station sleep states in 5G networks to save energy. The 5G wireless networks are required to be green and yield very low carbon dioxide emissions. Compared with that of 4G wireless networks, the power efficiency of 5G is expected to be increased to 100-fold. High energy efficiency is a critical requirement in 5G network design and operation. We propose station sleep strategies based on machine learning, stochastic programming and robust optimization models which, by leveraging demand patterns learned from historical load data, provide statistically optimal energy efficiency and delay-bounded QoS.

Faculty Supervisor:

Chun Wang


Jie Gao;Xiaoming Li


Ericsson Canada


Engineering - computer / electrical


Information and cultural industries


Concordia University



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