Network function virtualization (NFV) has drawn significant attention from both industry and academia as an important shift in telecommunication service provisioning. By decoupling network functions (NFs) from the physical devices on which they run, NFV has the potential to lead to significant reductions in operating expenses (OPEX) and capital expenses (CAPEX) and facilitate the deployment of new services with increased agility and faster time-to-value. The NFV paradigm is still in its infancy and there is a large spectrum of opportunities for the research community to develop new architectures, systems and applications, and to evaluate alternatives and trade-offs in developing technologies for its successful deployment. In this paper, after discussing NFV and its relationship with complementary fields of software defined networking (SDN) and cloud computing, we survey the state-of-the-art in NFV, and identify promising research directions in this area. We also overview key NFV projects, standardization efforts, early implementations, use cases, and commercial products.

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Network Function Virtualization: State-of-the-Art and Research Challenges

Massive MIMO is a reality—What is next?: Five promising research directions for antenna arrays

Compared with fourth generation cellular systems, fifth generation wireless communication systems 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 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 the areas 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 pool to manage all accessed remote radio heads. Also, the software-defined H-CRAN system architecture is presented to be compatible with software-defined networks. 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.

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

Imagine a coverage area with many wireless access points that cooperate to jointly serve the users, instead of creating autonomous cells. Such a cell-free network operation can potentially resolve many of the interference issues that appear in current cellular networks. This ambition was previously called Network MIMO (multiple-input multiple-output) and has recently reappeared under the name Cell-Free Massive MIMO. The main challenge is to achieve the benefits of cell-free operation in a practically feasible way, with computational complexity and fronthaul requirements that are scalable to large networks with many users. We propose a new framework for scalable Cell-Free Massive MIMO systems by exploiting the dynamic cooperation cluster concept from the Network MIMO literature. We provide a novel algorithm for joint initial access, pilot assignment, and cluster formation that is proved to be scalable. Moreover, we adapt the standard channel estimation, precoding, and combining methods to become scalable. A new uplink and downlink duality is proved and used to heuristically design the precoding vectors on the basis of the combining vectors. Interestingly, the proposed scalable precoding and combining outperform conventional maximum ratio (MR) processing and also performs closely to the best unscalable alternatives.

Scalable Cell-Free Massive MIMO Systems

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, social-aware 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.

Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues

This paper presents the latest progress on cloud RAN (C-RAN) in the areas of centralization and virtualization. A C-RAN system centralizes the baseband processing resources into a pool and virtualizes soft base-band units on demand. The major challenges for C-RAN including front-haul and virtualization are analyzed with potential solutions proposed. Extensive field trials verify the viability of various front-haul solutions, including common public radio interface compression, single fiber bidirection and wavelength-division multiplexing. In addition, C-RANs facilitation of coordinated multipoint (CoMP) implementation is demonstrated with 50%-100% uplink CoMP gain observed in field trials. Finally, a test bed is established based on general purpose platform with assisted accelerators. It is demonstrated that this test bed can support multi-RAT, i.e., Time-Division Duplexing Long Term Evolution, Frequency-Division Duplexing Long Term Evolution, and Global System for Mobile Communications efficiently and presents similar performance to traditional systems.

Jesse Xiaogen Jiang

Papers

Recent Progress on C-RAN Centralization and Cloudification