The fifth-generation mobile initiative, 5G, is a tremendous and collective effort to specify, standardize, design, manufacture, and deploy the next cellular network generation. 5G networks will support demanding services such as enhanced Mobile Broadband, Ultra-Reliable and Low Latency Communications and massive Machine-Type Communications, which will require data rates of tens of Gbps, latencies of few milliseconds and connection densities of millions of devices per square kilometer. This survey presents the most significant use cases expected for 5G including their corresponding scenarios and traffic models. First, the paper analyzes the characteristics and requirements for 5G communications, considering aspects such as traffic volume, network deployments, and main performance targets. Secondly, emphasizing the definition of performance evaluation criteria for 5G technologies, the paper reviews related proposals from principal standards development organizations and industry alliances. Finally, well-defined and significant 5G use cases are provided. As a result, these guidelines will help and ease the performance evaluation of current and future 5G innovations, as well as the dimensioning of 5G future deployments.

Current forecasts predict that Industrial Internet of Things (IIoT) will account for about ten billion devices by 2020. Simultaneously, unlicensed Low Power Wide Area Networks (LPWAN) are gaining momentum due to their low cost, low power and long range characteristics, which are suitable for many IIoT applications, in addition to the usage of unlicensed bands. In this article, a solution is proposed to seamlessly integrate LoRaWAN, an open and standardized LPWAN technology, with 4G/5G mobile networks, thus allowing mobile network operators to reutilize their current infrastructures. This proposal is transparent to LoRaWAN end-devices and to the Evolved Packet Core (EPC), since only the LoRaWAN gateway is required to be modified. The gateway acts as an evolved Node B (eNB) from the core network perspective, implementing part of the eNB protocol stack. All data packets transported over the core network are both encrypted and integrity protected, hence achieving end-to-end security. As a proof-ofconcept, this solution has been implemented and validated with an open-source EPC.

YouTube currently accounts for a significant percentage of the Internet's global traffic. Hence, understanding the characteristics of the YouTube traffic generation pattern can provide a significant advantage in predicting user video quality and in enhancing network design. In this paper, we present a characterisation of the traffic generated by YouTube when accessed from a regular PC. On the basis of this characterisation, a YouTube server traffic generation model is proposed, which, for example, can be easily implemented in simulation tools. The derived characterisation and model are based on experimental evaluations of traffic generated by the application layer of YouTube servers. A YouTube server commences the download with an initial burst and later throttles down the generation rate. If the available bandwidth is reduced (e.g. in the presence of network congestion), the server behaves as if the data excess that cannot be transmitted because of the reduced bandwidth were accumulated at a server's buffer, which is later drained if the bandwidth availability is recovered. As we will show, the video clip encoding rate plays a relevant role in determining the traffic generation rate, and therefore, a cumulative density function for the most viewed video clips will be presented. The proposed traffic generation model was implemented in a YouTube emulation server, and the generated synthetic traffic traces were compared with downloads from the original YouTube server. The results show that the relative error between downloads from the emulation server and the original server does not exceed 6% for the 90% of the considered videos. Copyright © 2012 John Wiley & Sons, Ltd.

Abstract-Network Function Virtualization is considered one of the key technologies for developing the future mobile networks. In this paper, we propose a theoretical framework to evaluate the performance of an LTE virtualized Mobility Management Entity (vMME) hosted in a data center. This theoretical framework consists of i) a queuing network to model the vMME in a data center, and ii) analytic expressions to estimate the overall mean system delay and the signaling workload to be processed by the vMME. We validate our mathematical model by simulation. One direct use of the proposed model is vMME dimensioning, i.e., to compute the number of vMME processing instances to provide a target system delay given the number of users in the system. Additionally, the paper includes a scalability analysis of the system. In our study we consider the billing model and a data center setup of Amazon Elastic Compute Cloud service, and estimate experimentally the processing time of MME processing instances for different LTE control procedures. For the considered setup, our results show that a vMME is scalable for signaling workloads up to 37000 LTE control procedures per second for a target mean system delay of 1 ms. The database performance assumed imposes this limit in the system scalability.

Large-scale deployments of massive Machine Type Communications (mMTC) involve several challenges on cellular networks. To address the challenges of mMTC, or more generally, Internet of Things (IoT), the 3rd Generation Partnership Project has developed NarrowBand IoT (NB-IoT) as part of Release 13. NB-IoT is designed to provide better indoor coverage, support of a massive number of low-throughput devices, with relaxed delay requirements, and lower-energy consumption. NB-IoT reuses Long Term Evolution functionality with simplifications and optimizations. Particularly for small data transmissions, NB-IoT specifies two procedures to reduce the required signaling: one of them based on the Control Plane (CP), and the other on the User Plane (UP). In this work, we provide an overview of these procedures as well as an evaluation of their performance. The results of the energy consumption show both optimizations achieve a battery lifetime extension of more than 2 years for a large range in the considered cases, and up to 8 years for CP with good coverage. In terms of cell capacity relative to SR, CP achieves gains from 26% to 224%, and UP ranges from 36% to 165%. The comparison of CP and UP optimizations yields similar results, except for some specific configurations.