5. Smart Energy-Saving Solutions Based on Artificial Intelligence and Other Emerging Technologies for 5G Wireless and Beyond Networks Communications

This chapter reports how to explore the techniques of energy saving which have already appeared since mobile communication era, like carrier/channel/symbol shutdown, etc., can be leveraged to moderate energy consumption in 5G. This chapter also improved deep sleep, symbol accumulation shutdown technologies, which had been introduced in 5G network. However, artificial intelligence and big data technique’s need to be introduced to form more accurate energy-saving strategy based on user traffic and other site-related conditions, which improves the efficiency as well as reduce the man power requirements. The mobile network traffic often experiences the troughs and peaks in terms of time distribution. The fundamental function employed to the whole mobile network is not the site-specific approach, which results to be less efficient because of the varied neighbouring site patterns and the traffic ignorance, particularly in the complex networks. Based on specific site traffic and other site-related condition, big data and AI technologies can be implemented to formulate more accurate strategy for energy saving in this scenario. The AI-driven network based on energy saving provides the solution which can help to forecast the load (traffic) of the base stations (BSs) centred on the conditions of the service type, user behaviour, historical traffic load on BS and the site coverage. AI technology can automatically configure the energy-saving strategy on the basis of coverage and configuration identification. Besides all this, the energy-saving solution centred on the AI-driven network can also certify the proper balance between the power consumption and the performance of the network based on appropriate training of the model.