As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including system architectures, key techniques, and open issues. The system architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues, and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, socialaware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test is introduced as well. Index Terms-Cloud radio access network (C-RAN), fronthaul compression, large-scale collaborative processing, channel estimation, resource allocation.
In REF , the effect of cloud radio access networks structure (C-RAN) on 5G resource management is studied considering different functional splitting in this context.
Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
IEEE Communications Surveys & Tutorials
IEEE Communications Surveys & Tutorials
Computer Science Mathematics