Special Issue on Learning-based Mobile Services in 5G Network
• 大类 : 工程技术 - 3区
• 小类 : 计算机：信息系统 - 3区
• 小类 : 工程：电子与电气 - 3区
• 小类 : 电信学 - 3区
The fifth generation (5G) wireless communications are expected to meet various services requirement including residence, work, entertainment and transport in our daily life, which aim to connect everything benefiting from connections. Specifically, the 5G mobile system is a complex network that is difficult to be managed, which has to meet a series of strict requirements. Thus, it should be flexible to the changes in traffic and environment, which brings many challenges and problems.
Recently, artificial intelligence (AI) technologies has made major breakthroughs in the fields of computer vision, speech recognition, healthcare and the automotive industry, which brings new ideas to solve the thorny problems in the emerging complex communication systems. Therefore, for improving the user’s quality of experience (QoE), quality of service (QoS) and effectively coping with the situations in the 5G services, it is necessary to implement machine learning and deep learning into the communication systems. In the new service paradigm, AI will focus on traffic control, resource optimization, network management and in-depth knowledge discovery in complex mobile network environments.
This special issue (SI) will bring together academic and industrial researchers to discuss the technical challenges and recently developments in mobile services and network optimization scheduling in 5G networks. In order to meet the stringent requirements of user experience, efficiency, and performance in a complex wireless network environment, this issue will discuss how to improve QoE/QoS in mobile services through AI. Submitted papers in this SI are expected to focus on mobile services research, especially the application of machine learning to the prediction and processing of mobile issues. Topics of interest include, but are not limited to, the following areas:
• Learning-based network traffic control and distribution scheme
• Learning-based mobile traffic prediction and feature selection and characterization
• Intelligent Cache allocation and management scheme in user mobile services
• Popular and preferred content analysis in mobile traffic
• Network resource scheduling in mobile services
• In-depth knowledge discovery of user associations, switching policies, and traffic content in mobile services
• Security and privacy issues in mobile services
• Energy-saving and deployment scenarios for traffic in learning-based mobile services