A comprehensive survey on resource allocation for CRAN in 5G and beyond networks

Abstract

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.

A Survey on Applications of Deep Learning in Cloud Radio Access Network

“…Each of the communication media has advantages and disadvantages. In the beginning, BBU pool and RRHs were interconnected by dedicated fiber links, and the common public radio interface (CPRI) was considered as the data transmission protocol between them [15]. The two main technology that have been considered for centralization and virtualization of C-RAN are software Defined Networking (SDN) and network Function Virtualization (NFV).…”

“…Several existing surveys have investigated the C-RAN architecture, highlighting the resource management [75], [15], [26], and energy efficiency [76], [77] of this architecture. Many surveys discuss about deep learning applications in different cellular networking paradigms such as edge cellular networks [10], the physical layer of 5G [78], ad-hoc networks [79] and cellular network traffic control [80].…”

“…In [75], [91], and [15], surveys on resource management techniques in C-RAN for 5G and beyond networks have been presented. The authors in [75] discussed the approaches of resource management in C-RAN by classifying all the proposed techniques into computational and radio resource management.…”

Performance Evaluation in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic

“…On the one hand, various aspects of the C-RAN architecture have been investigated and analyzed the last few years, such as the capacity demands and possible functional splits on the fronthaul network [25], [26], energy and cost saving issues [27], [28], security challenges [29], resource allocation issues related to RRH selection, spectrum management and throughput maximization [30], as well as the dimensioning problem of the necessary number of V-BBU required to handle a specific number of RRHs [31], [32]. The latter focus only on the V-BBU and model them as a queueing system in which the arrival process of jobs follows a batched Poisson process and the service time is exponentially distributed.…”

A Survey on Weapon Target Allocation Models and Applications

“…[53], Gülpınar et al[25], Wang et al[54], Ma et al[55], Kline et al[27], Rudek and Heppner[43], Kalaiselvi and Selvi[56], Shooli and Javidi[57], Zhu et al[58], Xu et al[59], Ejaz et al[60] …”