Abstract：In order to solve the problems of the efficient synergy between the management orchestration components required for the deployment of the edge computing business and the NFV MANO components in the telecommunications cloud, this paper firstly analyzes the division of responsibilities and interaction processes between the management orchestration components of edge computing and telecommunications cloud. Secondly, based on the deep learning method and model, a solution is given to improve the management orchestration efficiency between the virtual network elements of the telecommunications cloud and the edge computing applications. Finally, the main issues and directions in this area are prospected.

Abstract：With the rapid development of 5G technology, the number and density of 5G base stations will be far higher than 4G, and the construction and maintenance of base stations will become a problem that cannot be ignored. Therefore, this paper comprehensively analyzes the 5G base station decommission situation and puts forward a 5G base station decommission cost estimation scheme based on big data. With the base station historical decommission data, the LSTM neural network is used to establish the base station decommission prediction model. Then the 5G base station decommission cost estimation model is constructed, and the cost estimation of the forecast 5G base station decommission is made. Finally, through the experimental analysis, the effectiveness of the scheme is explained and suggestions are put forward.

Abstract：In 5G mobile communications, key technologies such as large-scale array antennas, new scenarios, new services and new frequency bands are introduced. The goal of future network evolution is to achieve independent network decision-making and independent evolution through the introduction of artificial intelligence. 5G network planning also faces the requirements for intelligent evolution. From the perspective of 5G network planning and deployment phases, this paper discusses how to build an overall 5G network planning solution based on big data, machine learning and other technologies to guide the planning and construction of 5G network planning.

Abstract：With the advent of 5G era, a variety of application scenarios and differentiated service requirements are emerging, which puts forward higher requirements for the ability of operators to schedule network resources and service requirements. Based on the analysis of the new features of 5G network, this paper proposes an intent-based network(IBN) management scheduling system. Combined with the network architecture of IBN, various forms of service intentions are collected and translated into the required network operation and configuration, so as to realize the rapid and automatic deployment of the network. A fast scheduling algorithm is proposed based on this system to effectively schedule tasks at multiple levels and peer levels. The simulation experiments show that the fast scheduling system based on IBN improves the task processing efficiency and reduces the task scheduling processing time, which is of great significance to the service requirement scheduling guarantee of communication scenarios such as major events.

Abstract：In response to the national call for energy conservation and emission reduction, the energy saving and consumption reduction of base stations has become the top priority in today′s communications field, the energy-saving technologies of major operators will also become core competitiveness. This paper studies the Artificial Intelligence(AI)-based 5G base station energy-saving technology. Traditional base station energy-saving technologies are analyzed. The prospect of 5G base station energy-saving technology combined with AI technology is explored. The development direction of AI energy-saving technology has been deeply studied. The AI energy saving model and the AI-based collaborative energy saving scheme are highlighted.