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Network slicing is a key feature of forthcoming fifth generation (5G) systems to facilitate the partitioning of the network into multiple logical networks customized 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 materialization 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 characterize 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 analyzed under standard and disaster situations, thus illustrating the capability to properly manage the different multi-tenant and multi-service traffic loads. INDEX TERMS Admission control, Markov processes, mobile communication, multi-tenancy, radio access networks, RAN slicing.

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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. INDEX TERMS Markov processes, radio access networks, RAN slicing, radio resource management, quality of service.

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While the support of Mission Critical (MC) communications on commercial cellular networks has already been incorporated in the latest releases of Long Term Evolution (LTE), it is expected that the network slicing feature of Fifth Generation (5G) systems will further boost the provision of these services thanks to the possibility of creating customized and isolated network slices adapted to the specific requirements of MC communications. At the Radio Access Network (RAN), the realization of a network slice requires to specify how the pool of available radio resources is split between the different slices in accordance with their service requirements. In this context, this paper addresses the use of RAN slicing for provisioning MC services taking as a reference the emergency scenario defined by the 5G ESSENCE project. It is characterized by different stages associated to the occurrence of an incident and its evolution, thus involving different communication needs. For each stage, an estimation of the capacity requirements to be granted to the MC RAN slice is provided. Then, the architecture of the project is discussed, focusing on the components that enable the RAN slicing management to properly support MC services.

Network slicing is a key feature of forthcoming 5G systems to facilitate the partitioning of the network into multiple logical networks customised according to different operation and application needs. Network slicing allows the materialisation of multi-tenant networks, in which the same infrastructure is shared among multiple communication providers, each one using a different slice. The support of multi-tenancy through slicing in the Radio Access Network (RAN) is particularly challenging because it involves the configuration and operation of multiple and diverse RAN behaviour over a common pool of radio resources while guaranteeing a certain Quality of Service (QoS) and isolation to each of the slices. This paper presents a Markovian approach to model different QoS aware Admission Control (AC) policies in a multi-tenant scenario with Guaranteed Bit Rate (GBR) services. From the analytical model, different metrics are defined to later analyse the effect of AC mechanisms on the performance achieved in various scenarios. Results show the impact of priorities for services of different tenants and isolation between tenants when different AC polices are adopted.

The use of multi-connectivity has become a useful tool to manage the traffic in heterogeneous cellular network deployments, since it allows a device to be simultaneously connected to multiple cells. The proper exploitation of this technique requires to adequately configure the traffic sent through each cell depending on the experienced conditions. This motivates this work, which tackles the problem of how to optimally split the traffic among the cells when the multi-connectivity feature is used. To this end, the paper proposes the use of a deep reinforcement learning solution based on a Deep Q-Network (DQN) in order to determine the amount of traffic of a device that needs to be delivered through each cell, making the decision as a function of the current traffic and radio conditions. The obtained results show a near-optimal performance of the DQN-based solution with an average difference of only 3.9% in terms of reward with respect to the optimum strategy. Moreover, the solution clearly outperforms a reference scheme based on Signal to Interference Noise Ratio (SINR) with differences of up to 50% in terms of reward and up to 166% in terms of throughput for certain situations. Overall, the presented results show the promising performance of the DQN-based approach that establishes a basis for further research in the topic of multi-connectivity and for the application of this type of techniques in other problems of the radio access network.