5G is envisioned to simultaneously provide diverse service types with heterogeneous needs under very different application scenarios and business models. Therefore, network slicing is included as a key feature of the 5G architecture to allow sharing a common infrastructure among different tenants, such as mobile communication providers, vertical market players, etc. In order to provide the Radio Access Network (RAN) with network slicing capabilities, mechanisms that efficiently distribute the available capacity among the different tenants while satisfying their needs are required. For this purpose, this paper proposes a multi-agent reinforcement learning approach for RAN capacity sharing. It makes use of the Deep Q-Network algorithm in a way that each agent is associated to a different tenant and learns the capacity to be provided to this tenant in each cell while ensuring that the service level agreements are satisfied and that the available radio resources are efficiently used. The consideration of multiple agents contributes to a better scalability and higher learning speed in comparison to single-agent approaches. In this respect, results show that the policy learnt by the agent of one tenant can be generalised and directly applied by other agents, thus reducing the complexity of the training and making the proposed solution easily scalable, e.g. to add new tenants in the system. The proposed approach is well aligned with the on-going 3GPP standardization work and guidelines for the parametrization of the solution are provided, thus enforcing its practical applicability.

5G systems are envisaged to support a wide range of application scenarios with variate requirements. To handle this heterogeneity, 5G architecture includes network slicing capabilities that facilitate the partitioning of a single network infrastructure into multiple logical networks on top of it, each tailored to a given use case and provided with appropriate isolation and Quality of Service (QoS) characteristics. Network slicing also enables the use of multi-tenancy networks, in which the same infrastructure can be shared by multiple tenants by associating one slice to each tenant, easing the cost-effective deployment and operation of future 5G networks. Concerning the Radio Access Network (RAN), slicing is particularly challenging as it implies the configuration of multiple RAN behaviors over a common pool of radio resources. In this context, this work presents a Markov model for RAN slicing capable of characterizing diverse Radio Resource Management (RRM) strategies in multi-tenant and multi-service 5G scenarios including both guaranteed and non-guaranteed bit rate services. The proposed model captures the fact that different radio links from diverse users can experience distinct spectral efficiencies, which enables an accurate modeling of the randomness associated with the actual resource requirements. The model is evaluated in a multi-tenant scenario in urban micro cell and rural macro cell environments to illustrate the impact of the considered RRM polices in the QoS provisioning.

Network slicing is a key feature of forthcoming Fifth Generation (5G) systems to facilitate the partitioning of the network into multiple logical networks customised according to different business and application needs. Network slicing is a fundamental capability for enabling a cost-effective deployment and operation of 5G, as it allows the materialisation of multi-tenant networks in which the same infrastructure is shared among multiple communication providers, each one using a different slice. This paper proposes a Markovian approach to characterise the resource sharing in multi-tenant scenarios with diverse Guaranteed Bit Rate services by considering a slice-aware Admission Control policy. After describing the Markov model and its implementation and discussing its suitability, the model is applied to study the performance attained in a scenario with two different slices, one for enhanced Mobile Broadband communications and the other for Mission Critical services. The system is analysed under standard and disaster situations, thus illustrating the capability to properly manage the different multi-tenant and multiservice traffic loads.

Network slicing is a fundamental feature of 5G systems to partition a single network into a number of segregated logical networks, each optimized for a particular type of service or dedicated to a particular customer or application. The realization of network slicing is particularly challenging in the Radio Access Network (RAN) part, where multiple slices can be multiplexed over the same radio channel and Radio Resource Management (RRM) functions shall be used to split the cell radio resources and achieve the expected behaviour per slice. In this context, this paper describes the key design and implementation aspects of a Software-Defined RAN (SD-RAN) experimental testbed with slicing support. The testbed has been designed consistently with the slicing capabilities and related management framework established by 3GPP in Release 15. The testbed is used to demonstrate the provisioning of RAN slices (e.g., preparation, commissioning, and activation phases) and the operation of the implemented RRM functionality for slice-aware admission control and scheduling.

This paper illustrates a Universal Software Radio Peripheral (USRP)-based real-time testbed that is able to evaluate different spectrum management solutions that exploit the Cognitive Radio (CR) paradigm. The main objective of this testbed is to provide an accurate and realistic platform by which the performance of innovative spectrum management solutions for a wide set of scenarios and use cases in the context of Opportunistic Networks (ONs) and Cognitive Radio Networks (CRNs) can be entirely validated and assessed before their implementation in real systems. Real-time platforms are essential to carry out significant studies and to accurately assess the performance of innovative solutions before their implementation in the real world. This work provides a comprehensive description of the testbed, highlighting many interesting implementation details and illustrating its applicability for different studies that rely on the CR paradigm. Then, a particular application in a realistic Digital Home (DH) scenario is also illustrated, which allows demonstrating the effectiveness of the real-time testbed and assessing its practicality in terms of user-perceived end-to-end Quality of Experience (QoE) in a realistic environment. (C) 2015 Elsevier B.V. All rights reserved.

Next generation wireless networks will encompass a wide range of heterogeneous technologies in the radio access part. In such networks, the all-IP paradigm has been identified as a promising solution that will contribute benefits by providing IP-based transport through the radio and core network parts. However, this concept requires a precise management of the userpsilas mobility, especially in order to preserve userpsilas quality of service (QoS) throughout the sessionpsilas lifetime. The aim of this paper is to evaluate the quality of experience (QoE) that users perceive when the different QoS-aware mobility management strategies adopted in the AROMA project are applied. A real-time testbed that provides end-to-edge QoS in all-IP heterogeneous wireless access networks has been employed to obtain QoE results that hardly could be obtained by means of simulations.