Deep Reinforcement Learning for Resource Management in Network Slicing

Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201

SymbioCity: Smart Cities for Smarter Networks

The "Smart City" (SC) concept revolves around the idea of embodying cutting-edge ICT solutions in the very fabric of future cities, in order to offer new and better services to citizens while lowering the city management costs, both in monetary, social, and environmental terms. In this framework, communication technologies are perceived as subservient to the SC services, providing the means to collect and process the data needed to make the services function. In this paper, we propose a new vision in which technology and SC services are designed to take advantage of each other in a symbiotic manner. According to this new paradigm, which we call "SymbioCity", SC services can indeed be exploited to improve the performance of the same communication systems that provide them with data. Suggestive examples of this symbiotic ecosystem are discussed in the paper. The dissertation is then substantiated in a proof-of-concept case study, where we show how the traffic monitoring service provided by the London Smart City initiative can be used to predict the density of users in a certain zone and optimize the cellular service in that area.Comment: 14 pages, submitted for publication to ETT Transactions on Emerging Telecommunications Technologie

Network Slicing in 5G: Admission, Scheduling, and Security

In the past few decades, there was an increase in the number of devices that have wireless capabilities such as phones, televisions, and home appliances. With the high demand for wireless networking, the fifth generation (5G) of mobile networks was designed to support the different services of new applications. In addition, one of the technical issues that 5G would evolve is the increase in traffic and the need to satisfy the user’s experience. With the evolution of wireless networking and 5G, Network Slicing has been introduced to accommodate the diverse requirements of the applications. Thus, network slicing is the concept of partitioning the physical network infrastructure into multiple self-contained logical pieces which can be identified as slices. Each slice can be customized to serve and meet different network requirements and characteristics. In terms of security, network security has allowed for new security vulnerabilities such as Distributed Denial of Service (DDoS) and resource exhaustion. However, slices can be isolated to provide better resource isolation. In addition, each slice is considered an end-to-end virtual network, operators would be able to allocate resources to the tenants which are the service providers. The isolated resources are controlled by the tenants; each tenant has control over how to use them to meet the requirements of the clients. One of the challenges in network slicing is RAN slicing. The target of RAN Slicing is to meet the QoS requirements of different services for each end-user. However, the coexistence of different services is challenging because each service has its requirements. Each slice must estimate its network demands based on the QoS requirements and control the admission to the slice. To solve this issue, we consider the scenario for the enhanced mobile broadband (eMBB) and the ultra-reliable-low-latency communication (URLLC) use cases’ coexistence, and we slice the RAN based on the priority of the user applicatio