The fifth generation (5G) of mobile communication system aims to deliver a ubiquitous mobile service with enhanced quality of service (QoS). It is also expected to enable new use-cases for various vertical industrial applications-such as automobiles, public transportation, medical care, energy, public safety, agriculture, entertainment, manufacturing, and so on. Rapid increases are predicted to occur in user density, traffic volume, and data rate. This calls for novel solutions to the requirements of both mobile users and vertical industries in the next decade. Among various available options, one that appears attractive is to redesign the network architecture-more specifically, to reconstruct the radio access network (RAN). In this paper, we present an inclusive and comprehensive survey on various RAN architectures toward 5G, namely cloud-RAN, heterogeneous cloud-RAN, virtualized cloud-RAN, and fog-RAN. We compare them from various perspectives, such as energy consumption, operations expenditure, resource allocation, spectrum efficiency, system architecture, and network performance. Moreover, we review the key enabling technologies for 5G systems, such as multi-access edge computing, network function virtualization, softwaredefined networking, and network slicing; and some crucial radio access technologies (RATs), such as millimeter wave, massive multi-input multi-output, device-to-device communication, and massive machinetype communication. Last but not least, we discuss the major research challenges in 5G RAN and 5G RATs and identify several possible directions of future research.

In order to cope with the explosive growth of data traffic which is associated with a wide plethora of emerging applications and services that are expected to be used by both ordinary users and vertical industries, the congestion control mechanism is considered to be vital. In this paper, we proposed a congestion control mechanism that could function within the framework of Multi-Access Edge Computing (MEC). The proposed mechanism is aiming to make real-time decisions for selectively buffering traffic while taking network condition and Quality of Service (QoS) into consideration. In order to support a MEC-assisted scheme, the MEC server is expected to locally store delay-tolerant data traffics until the delay conditions expire. This enables the network to have better control over the radio resource provisioning of higher priority data. To achieve this, we introduced a dedicated function known as Congestion Control Engine (CCE), which can capture Radio Access Network (RAN) condition through Radio Network Information Service (RNIS) function, and use this knowledge to make the real-time decision for selectively offloading traffic so that it can perform more intelligently. Analytical evaluation results of our proposed mechanism confirm that it can alleviate network congestion more efficiently.

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With the soaring demands for high speed data communication, as well as transmission of various types of services with different requirements over cellular networks, having a decent radio resource management is considered vital in Long Term Evolution (LTE) system. In particular, satisfying the Quality of Service (QoS) requirements of different applications is one of the key challenges of radio resource management that needs to be dealt by the LTE system. In this paper, we propose a cross-layer design scheme that jointly optimizes three different layers of wireless protocol stack, namely application, Medium Access Control (MAC), and physical layer. The cross-layer optimization framework provides efficient allocation of wireless resources across different types of applications (i.e., real-time and non real-time) run by different users to maximize network resource utilization and user-perceived quality of service, or also known as Quality of Experience (QoE). Here, Mean Opinion Score (MOS) is used as a unified QoE metric that indicates the user-perceived quality for real-time or multimedia services notably video applications. Along with multimedia services, the proposed framework also takes care of non-real-time traffic by ensuring certain level of fairness. Our simulation, applied to scenarios where users simultaneously run different types of applications, confirms that the proposed QoE-oriented cross-layer framework leads to significant improvement in terms of maximizing user-perceived quality as well as maintaining fairness among users.

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