A Comprehensive Survey on Resource Allocation for CRAN in 5G and Beyond Networks

The diverse service requirements coming with the advent of sophisticated applications as well as a large number of connected devices demand for revolutionary changes in the traditional distributed radio access network (RAN). To this end, Cloud-RAN (CRAN) is considered as an important paradigm to enhance the performance of the upcoming fifth generation (5G) and beyond wireless networks in terms of capacity, latency, and connectivity to a large number of devices. Out of several potential enablers, efficient resource allocation can mitigate various challenges related to user assignment, power allocation, and spectrum management in a CRAN, and is the focus of this paper. Herein, we provide a comprehensive review of resource allocation schemes in a CRAN along with a detailed optimization taxonomy on various aspects of resource allocation. More importantly, we identity and discuss the key elements for efficient resource allocation and management in CRAN, namely: user assignment, remote radio heads (RRH) selection, throughput maximization, spectrum management, network utility, and power allocation. Furthermore, we present emerging use-cases including heterogeneous CRAN, millimeter-wave CRAN, virtualized CRAN, Non- Orthogonal Multiple Access (NoMA)-based CRAN and fullduplex enabled CRAN to illustrate how their performance can be enhanced by adopting CRAN technology. We then classify and discuss objectives and constraints involved in CRAN-based 5G and beyond networks. Moreover, a detailed taxonomy of optimization methods and solution approaches with different objectives is presented and discussed. Finally, we conclude the paper with several open research issues and future directions

Dynamic network slicing for multitenant heterogeneous cloud radio access networks

Multitenant cellular network slicing has been gaining huge interest recently. However, it is not well-explored under the heterogeneous cloud radio access network (H-CRAN) architecture. This paper proposes a dynamic network slicing scheme for multitenant H-CRANs, which takes into account tenants' priority, baseband resources, fronthaul and backhaul capacities, quality of service (QoS) and interference. The framework of the network slicing scheme consists of an upper-level, which manages admission control, user association and baseband resource allocation; and a lower-level, which performs radio resource allocation among users. Simulation results show that the proposed scheme can achieve a higher network throughput, fairness and QoS performance compared to several baseline schemes

Energy-Efficient NOMA Enabled Heterogeneous Cloud Radio Access Networks

Heterogeneous cloud radio access networks (H-CRANs) are envisioned to be promising in the fifth generation (5G) wireless networks. H-CRANs enable users to enjoy diverse services with high energy efficiency, high spectral efficiency, and low-cost operation, which are achieved by using cloud computing and virtualization techniques. However, H-CRANs face many technical challenges due to massive user connectivity, increasingly severe spectrum scarcity and energy-constrained devices. These challenges may significantly decrease the quality of service of users if not properly tackled. Non-orthogonal multiple access (NOMA) schemes exploit non-orthogonal resources to provide services for multiple users and are receiving increasing attention for their potential of improving spectral and energy efficiency in 5G networks. In this article a framework for energy-efficient NOMA H-CRANs is presented. The enabling technologies for NOMA H-CRANs are surveyed. Challenges to implement these technologies and open issues are discussed. This article also presents the performance evaluation on energy efficiency of H-CRANs with NOMA.Comment: This work has been accepted by IEEE Network. Pages 18, Figure

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

Three convergent processes are likely to shape the future of the internet beyond-5G: The convergence of optical and millimeter wave radio networks to boost mobile internet capacity, the convergence of machine learning solutions and communication technologies, and the convergence of virtualized and programmable network management mechanisms towards fully integrated autonomic network resource management. The integration of network virtualization technologies creates the incentive to customize and dynamically manage the resources of a network, making network functions, and storage capabilities at the edge key resources similar to the available bandwidth in network communication channels. Aiming to understand the relationship between resource management, virtualization, and the dense 5G access and fronthaul with an emphasis on converged radio and optical communications, this article presents a review of how resource management solutions have dealt with optimizing millimeter wave radio and optical resources from an autonomic network management perspective. A research agenda is also proposed by identifying current state-of-the-art solutions and the need to shift all the convergent issues towards building an advanced resource management mechanism for beyond-5G