Читать книгу The Smart Cyber Ecosystem for Sustainable Development - Группа авторов - Страница 55

With the steady increase in the number of users of wireless networks and the need to deploy large number of base stations, and since base stations consume large energy; operating the network with minimum energy is a challenge. One way to reduce energy consumption is the idea of turning off some base stations if users can be served from others, while maintaining a reasonable QoS level. Learning the operation of the network over time helps in improving decisions about which base stations might be switched off.

An SDN-based ML system for energy saving is proposed in [15]. Performance of neural networks and SVM algorithms is compared. The network trains itself using data collected from base stations and recommends the operator time periods during which some base stations are predicted to handle very low traffic and therefore can be switched off.

The authors of [16] propose a Q learning method for base station on-off switching. The switching of base stations is defined as the actions, while the traffic load is defined as the state. The overall objective is to minimize energy consumption. Policy values are used by the controller to decide on switching. After performing a switch operation, the system state is changed and the energy cost of the former state is computed. If the energy cost of the newly executed action is smaller than energy costs with other actions, then the controller updates the policy value in order to increase the probability of selecting this action. With time, the optimal switching mechanism is obtained.