In the emerging 5G architecture, the Cloud-Radio Access Network (Cloud-RAN) offers the possibility to dynamically configure virtual resources and network functionalities very close to end-users, while jointly considering bandwidth, computing, latency, and memory capabilities requested by heterogeneous applications, the channel quality experienced by end-users, mobility, and any kind of system constraints. By capitalizing on recent scientific results and standardization activities on 5G, this short paper presents a preliminary design of an ETSI-NFV compliant architecture willing to support the implementation of advanced protocols, algorithms, and methodologies for the optimal management of the 5G Cloud-RAN. Its components and functionalities have been sketched by harmoniously integrating Software-Defined Networking (SDN) facilities, Multi-access Edge Computing (MEC), and deep learning. Herein, spatio-temporal users' dynamics are collected by SDN controllers and predicted by a high-level orchestrator through a Convolutional Long Short-Term Memory scheme. Then, the outcomes of the prediction process are adopted to dynamically configure the Cloud-RAN (i.e., by using any kind of customizable algorithm). Some of the capabilities of the proposed approach are preliminarily evaluated by considering the autonomous driving use case and real mobility traces. Moreover, the paper concludes by reporting an overview of future directions of this research activity.
Multi-access Edge Computing represents a key enabling technology for emerging mobile networks. It offers intensive computational resources very close to the end-users, useful for task offloading purposes. Many scientific contributions already proposed approaches for optimally allocating these resources over time. However, most of them fail to take advantage of the prediction of both users' mobility and service demands over a look-ahead temporal horizon. To bridge this gap, this paper formulates a novel methodology for anticipatorily allocating communication and computational resources at the network edge, based on the prediction of spatio-temporal dynamics of mobile users. The conceived architecture exploits a Software-Defined Networking approach to monitor users' mobility, a Convolutional Long Short-Term Memory to predict over different look-ahead horizons the number of users within a given number of cells and their related service demands, and Dynamic Programming to optimally allocate users' requests among available Multi-access Edge Computing servers. Computer simulations investigate the effectiveness of the proposed approach in a realistic autonomous driving use case and compare its behavior against a baseline solution. Obtained results demonstrate its unique ability to dynamically and fairly distribute users' requests among the resources available at the network edge, while ensuring the targeted quality of service level.
Understanding mobile traffic dynamics is a key issue to properly manage the radio resources in next generation mobile networks and meet the stringent requirements of emerging heterogeneous services, such as enhanced mobile broadband, autonomous driving, and extended reality (just to name a few). However, radio resource utilization patterns of real mobile applications are mostly unknown. This paper aims at filling this gap by tailoring an unsupervised learning methodology (i.e. K-means), able to identify similar radio resource utilization patterns of mobile traffic from an operating mobile network. Our analysis is based on datasets referring to residential and campus areas and containing wireless link level information (e.g., scheduling, channel conditions, transmission settings, and duration) with a very precise level of granularity (e.g., 1 ms). Obtained results reveal the properties of groups of sessions with similar characteristics, expressed in terms of bandwidth demands and application level requirements.
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