Abstract
Cloud radio access network (C-RAN) is a promising mobile wireless sensor network architecture to address the challenges of ever-increasing mobile data traffic and network costs. C-RAN is a practical solution to the strict energy-constrained wireless sensor nodes, often found in Internet of Things (IoT) applications. Although this architecture can provide energy efficiency and reduce cost, it is a challenging task in C-RAN to utilize the resources efficiently, considering the dynamic real-time environment. Several research works have proposed different methodologies for effective resource management in C-RAN. This study performs a comprehensive survey on the state-of-the-art resource management techniques that have been proposed recently for this architecture. The resource management techniques are categorized into computational resource management (CRM) and radio resource management (RRM) techniques. Then both of the techniques are further classified and analyzed based on the strategies used in the studies. Remote radio head (RRH) clustering schemes used in CRM techniques are discussed extensively. In this research work, the investigated performance metrics and their validation techniques are critically analyzed. Moreover, other important challenges and open research issues for efficient resource management in C-RAN are highlighted to provide future research direction.
Highlights
Advances in Internet of Things (IoT) technology have increased the number and usage of wireless sensor nodes [1,2]
In the Base band unit (BBU) pool, multiple BBUs are interconnected via the X2 perspective, BBUs are installed on virtual machines (VMs) using a hypervisor over physical computing interface, which is highly cost‐effective and yields improved performance
C‐radio access networks (RANs) provides the facility to switch off a BBU or an Remote radio head (RRH) depending on the on the RRH side according to the capacity of the BBUs within a BBU pool, when users move from one cell to another
Summary
Advances in Internet of Things (IoT) technology have increased the number and usage of wireless sensor nodes [1,2]. The average traffic generated by smartphones will be 11 GB per month, more than a four and half-fold increase over that in 2017 To tackle this enormous data traffic, the capacity of traditional mobile network architectures and the available resources are not sufficient. Mobile network operators (MNOs) need to increase the number of active base stations to satisfy the increasing user demand. The efficient management of the resources in C-RAN to satisfy user demand is a significant challenge due to the user mobility and dynamic environment. Resource management techniques could be static or dynamic depending on whether the environment is static or dynamic In the latter case, the dynamicity of traffic load, user positions, user mobility, QoS requirement, and base station density are considered. Different RRM strategies for improving spectral efficiency have been proposed, such as efficient static or dynamic channel allocation, transmit power control, spectrum management, cache management, link adaptation, user association, beamforming, and handover criteria
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