System architecture and key technologies for 5G heterogeneous cloud radio access networks

TLDR

5G is expected to deliver at least tenfold spectral and energy efficiency gains and a 25‑fold increase in area throughput compared with 4G. To meet these targets, the paper proposes a heterogeneous cloud radio access network (H‑CRAN) that centralizes large‑scale cooperative processing via cloud computing to suppress co‑channel interference. The study surveys current H‑CRAN architecture and key technologies, defining Node C as a BBU pool that unifies legacy base stations and remote radio heads, and presents a software‑defined architecture compatible with SDN. Key technologies—adaptive large‑scale cooperative spatial signal processing, cooperative radio resource management, network function virtualization, and self‑organization—are summarized for their principles, performance gains, and open issues, while fronthaul‑constrained resource allocation and energy harvesting pose major challenges to H‑CRAN deployment.

Abstract

Compared with the fourth generation (4G) cellular systems, the fifth generation wireless communication systems (5G) are anticipated to provide spectral and energy efficiency growth by a factor of at least 10, and the area throughput growth by a factor of at least 25. To achieve these goals, a heterogeneous cloud radio access network (H-CRAN) is presented in this article as the advanced wireless access network paradigm, where cloud computing is used to fulfill the centralized large-scale cooperative processing for suppressing co-channel interferences. The state-of-the-art research achievements in aspects of system architecture and key technologies for H-CRANs are surveyed. Particularly, Node C as a new communication entity is defined to converge the existing ancestral base stations and act as the base band unit (BBU) pool to manage all accessed remote radio heads (RRHs), and the software-defined H-CRAN system architecture is presented to be compatible with software-defined networks (SDN). The principles, performance gains and open issues of key technologies including adaptive large-scale cooperative spatial signal processing, cooperative radio resource management, network function virtualization, and self-organization are summarized. The major challenges in terms of fronthaul constrained resource allocation optimization and energy harvesting that may affect the promotion of H-CRANs are discussed as well.