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

By Mugen Peng, Yaohua Sun, Xuelong Li, Zhendong Mao, Chonggang Wang. IEEE Communications Surveys and Tutorials, 2016

377 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/bf1592067ef5bb935ebdaae2fb67c7c36816059f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems (2018, background): “network) [187] or at the level of a ground macrocell base station that is capable to control some of the UAVs.”
Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond (2021, background): “Fortunately, the C-RAN architecture designed for 5G-andbeyond communications provides a flexible and reconfigurable framework that enables a variety of the sensing modes discussed above [170], [171].”
Scalable Cell-Free Massive MIMO Systems (2019, neutral): “There are many other cloud-RAN solutions that can be used to distribute the computations over the network; see [55], [56] for some examples.”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, background): “as fog computing [140], can decrease the overall computational latency by reducing the reliance on the remote cloud while effectively offloading computational resources across”
A Survey of the Functional Splits Proposed for 5G Mobile Crosshaul Networks (2019, background): “Reference [24] looks into the state of the art at the time of writing and brings a summary of the contributions from different papers.”
A Tutorial on Near-Field XL-MIMO Communications Toward 6G (2023, neutral): “Some typical distributed antenna systems include coordinated multipoint (CoMP) [57], cloud radio access network (C-RAN) [58], network MIMO [59], and cell-free massive MIMO [60]– [62].”
Deep reinforcement learning based computation offloading and resource allocation for MEC (2018, background): “The Cloud Radio Access Network (C-RAN) is considered to be a core technology to enable these services in 5G networks with unprecedented spectral efficiency and energy efficiency [1].”
Foundations of User-Centric Cell-Free Massive MIMO (2020, background): “Aligned with the ongoing cloudification of wireless networks [82], [146], known as cloud radio access network (C-RAN), the CPU-related processing tasks can be distributed between the local processors in different ways [25].”
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues (2018, methodology): “The cloud computing based BBU pool can provide high data rates with the use of large-scale collaborative signal processing among BSs [9], [10] and can save much energy via statistical multiplexing [11].”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, background): “In particular, cloud radio access network (Cloud-RAN) [198], [199] provides a cost-effective way to implement distributed antenna aided edge training systems, for which reliable model aggregation via AirComp can be achieved by centralized signal processing and shortening the communication distances…”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, neutral): “s divided into distributed remote radio heads (RRHs) and a centralized baseband unit (BBU), and the baseband signal processing functionalities in the traditional BS are moved to the central BBU [20], [21]. By that decoupling, RRHs are responsible only for basic radio frequency functions and can supp”
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, background): “Mobile edge computing, which includes related concepts such as fog computing [190], can reduce the overall computational latency by reducing the reliance on the remote cloud while effectively offloading computational resources across multiple local and remote devices.”
Prospective Multiple Antenna Technologies for Beyond 5G (2019, background): “These are often called central processing units (CPUs) in the literature and the structure is reminiscent of the cloud radio access network (C-RAN) architecture [40].”