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

5G Dimensioning And Optimization Through Use Analysis Of A Real Scenario

Mobile networks have become essential to our daily communications. The growth of mobile traffic and users has increased exponentially in recent years, with increasing demands on throughput and latency. To handle this growing traffic, a scaling strategy that guarantees quality of service over time is essential. This thesis proposes the dimensioning of a mobile network based on a real 4G scenario, using techniques such as the implementation of new carriers and 5G technology. It also proposes the dynamic implementation of Cloud RAN, assigning the location of BBU pools according to network characteristics

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

In this paper, a cloud radio access network (C-RAN) is considered where the remote radio heads (RRHs) are separated from the baseband units (BBUs). The RRHs in the C-RAN are grouped in different clusters according to their capacity while the BBUs form a centralized pool of computational resource units. Each RRH services a finite number of mobile users, i.e., the call arrival process is the quasi-random process. A new call of a single service-class requires a radio and a computational resource unit in order to be accepted in the C-RAN for a generally distributed service time. If these resource units are unavailable, then the call is blocked and lost. To analyze the multi-cluster C-RAN, we model it as a single-rate loss system, show that a product form solution exists for the steady state probabilities and propose a convolution algorithm for the accurate determination of congestion probabilities. The accuracy of this algorithm is verified via simulation. The proposed model generalizes our recent model where the RRHs in the C-RAN are grouped in a single cluster and each RRH accommodates quasi-random traffic

Cost-Effective Delay-Constrained Optical Fronthaul Design for 5G and Beyond

With the rapid growth of the telecom sector heading towards 5G and 6G and the emergence of high-bandwidth and time-sensitive applications, mobile network operators (MNOs) are driven to plan their networks to meet these new requirements in a cost-effective manner. The cloud radio access network (CRAN) has been presented as a promising architecture that can decrease capital expenditures (Capex) and operating expenditures (Opex) and improve network performance. The fronthaul (FH) is a part of the network that links the remote radio head (RRH) to the baseband unit (BBU); these links need high-capacity and low latency connections necessitating costeffective implementation. On the other hand, the transport delay and FH deployment costs increase if the BBU is not placed in an appropriate location. In this paper, we propose an integer linear program (ILP) that simultaneously optimizes BBU and FH deployment resulting in minimal capital expenditures (Capex). Simulations are run to compare the performance of star and tree topologies with the varying line of sight probabilities (LoS) and delay thresholds. We consider fiber-optic (FO) and free-space optics (FSO) technologies as FH for the CRAN. Finally, we provide an analysis of Opex and the total costs of ownership (TCO), i.e., a technoeconomic analysis

Software-Defined Networks for Resource Allocation in Cloud Computing: A Survey

Cloud computing has a shared set of resources, including physical servers, networks, storage, and user applications. Resource allocation is a critical issue for cloud computing, especially in Infrastructure-as-a-Service (IaaS). The decision-making process in the cloud computing network is non-trivial as it is handled by switches and routers. Moreover, the network concept drifts resulting from changing user demands are among the problems affecting cloud computing. The cloud data center needs agile and elastic network control functions with control of computing resources to ensure proper virtual machine (VM) operations, traffic performance, and energy conservation. Software-Defined Network (SDN) proffers new opportunities to blueprint resource management to handle cloud services allocation while dynamically updating traffic requirements of running VMs. The inclusion of an SDN for managing the infrastructure in a cloud data center better empowers cloud computing, making it easier to allocate resources. In this survey, we discuss and survey resource allocation in cloud computing based on SDN. It is noted that various related studies did not contain all the required requirements. This study is intended to enhance resource allocation mechanisms that involve both cloud computing and SDN domains. Consequently, we analyze resource allocation mechanisms utilized by various researchers; we categorize and evaluate them based on the measured parameters and the problems presented. This survey also contributes to a better understanding of the core of current research that will allow researchers to obtain further information about the possible cloud computing strategies relevant to IaaS resource allocation