We aim to provide a comprehensive overview of the progress in research and development of the reconfigurable intelligent surface (RIS) over the last 2–3 years in this paper, especially when the RIS is used as relays in next-generation mobile networks. Major areas of research in academia are outlined, including fundamental performance, channel estimation, joint optimization with antenna precoding at base stations, propagation channel modeling and meta-material devices of RIS elements. Development in industry is surveyed from the aspects of performance potentials and issues, realistic joint optimization algorithms, control mechanisms, field trials and related activities in standardization development organizations (SDOs). Our views on how to carry out the engineering-aspect study on RIS for 6G systems are also presented, which cover the realistic performance, the comparison with other topological improvements, approaches for channel modeling, factors for designing control mechanisms and the timeline for RIS standardization.

The emerging fifth generation (5G) network has the potential to satisfy the rapidly growing traffic demand and promote the transformation of smartphone-centric networks into an Internet of Things (IoT) ecosystem. Due to the introduction of new communication technologies and the increased density of 5G cells, the complexity of operation and operational expenditure (OPEX) will become very challenging in 5G. Self-organizing network (SON) has been researched extensively since 2G, to cope with the similar challenge, however by predefined policies, rather than intelligent analysis. The requirement for better quality of experience and the complexity of 5G network demands call for an approach that is different from SON. In several recent studies, the combination of machine learning (ML) technology with SON has been investigated. In this paper, we focus on the intelligent operation of wireless network through ML algorithms. A comprehensive and flexible framework is proposed to achieve an intelligent operation system. Two use cases are also studied to use ML algorithms to automate the anomaly detection and fault diagnosis of key performance indicators (KPIs) in wireless networks. The effectiveness of the proposed ML algorithms is demonstrated by the real data experiments, thus encouraging the further research for intelligent wireless network operation.

This paper provides an overview of the physical layer of 5G new radio (NR) system. A general framework of 5G NR is first described, which glues together various key components, all of them helping to fulfill the requirements of three major deployment scenarios: enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC) and massive machine type communications (mMTC). Then, several key components of the 5G NR physical layer are discussed in more detail that include multiple access, channel coding, multiple antennas, frame structures, and initial access. The two-phase approach of NR is also discussed and the key technologies expected to be specified in Phase 1 and Phase 2 are listed.

Multiple access scheme is one of the key techniques in wireless communication systems. Each generation of wireless communication is featured by a new multiple access scheme from 1G to 4G. In this article we review several non?orthogonal multiple access schemes for 5G. Their principles, advantages and disadvantages are discussed, and followed by a comprehensive comparison of these solutions from the perspective of user overload, receiver type, receiver complexity and so on. We also discuss the application challenges of non?orthogonal multiple access schemes in 5G.