Large-scale deployments of massive machine type communications involve several challenges on cellular networks. To address the challenges of massive machine-type communications, or more generally, the Internet of Things (IoT), the 3GPP 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 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 two years for a large range in the considered cases, and up to eight years for CP with good coverage. In terms of cell capacity relative to Service Request, CP achieves gains from 26 to 224 percent, and UP ranges from 36 to 165 percent. The comparison of CP and UP optimizations yields similar results, except for some specific configurations.

The recent standardization of 3GPP Narrowband Internet of Things (NB-IoT) paves the way to support low-power wide-area (LPWA) use cases in cellular networks. NB-IoT design goals are extended coverage, low power and low cost devices, and massive connections. As a new radio access technology, it is necessary to analyze the possibilities NB-IoT provides to support different traffic and coverage needs. In this paper, we propose and validate an NB-IoT energy consumption model. The analytical model is based on a Markov chain. For the validation, an experimental setup is used to measure the energy consumption of two commercial NB-IoT user equipments (UEs) connected to a base station emulator. The evaluation is done considering three test cases. The comparison of the model and measurements is done in terms of the estimated battery lifetime and the latency needed to finish the control plane procedure. The conducted evaluation shows the analytical model performs well, obtaining a maximum relative error of the battery lifetime estimation between the model and the measurements of 21% for an assumed interarrival time (IAT) of 6 min.

Network function virtualization is considered one of the key technologies for developing future mobile networks. In this paper, we propose a theoretical framework to evaluate the performance of a Long-Term Evolution (LTE) virtualized mobility management entity (vMME) hosted in a data center. This theoretical framework consists of 1) a queuing network to model the vMME in a data center and 2) 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 the processing time of MME processing instances for different LTE control procedures experimentally. For the considered setup, our results show that the vMME is scalable for signaling workloads up to 37 000 LTE control procedures per second for a target mean system delay of 1 ms. The system design and database performance assumed imposes this limit in the system scalability.

Requirements for 5G mobile networks includes a higher flexibility, scalability, cost effectiveness and energy efficiency. Towards these goals, Software Defined Networking (SDN) and Network Functions Virtualization have been adopted in recent proposals for future mobile networks architectures because they are considered critical technologies for 5G. In this paper, we propose an X2-based handover implementation in an SDN-based and partially virtualized LTE architecture. Moreover, the architecture considered operates at link level, which provides lower latency and higher scalability. In our implementation, we use MPLS tunnels for user plane instead of GTP-U protocol, which introduces a significant overhead. To verify the correct operation of our system, we developed a simulator. It implements the messages exchange and processing of the primary network entities. Using this tool we measured the handover preparation and completion times, whose estimated values were roughly 6.94 ms and 8.31 ms, respectively, according to our experimental setup. These latencies meet the expected requirements concerning control plane delay budgets for 5G networks.

Network Functions Virtualization facilitates the automation of the management operations and orchestration of future networks. The envisioned management practices include the introduction of automation in the deployment and scaling of softwarized network services (SNSs). However, the realization of such a scenario requires a way to determine the needed amount of resources so that the SNSs performance requisites are met for a given workload. This problem is known as resource dimensioning, and it can be efficiently tackled by performance modeling. In this vein, this paper describes an analytical model based on an open queuing network of G/G/m queues to evaluate the response time of SNSs. We validate our model experimentally using a virtualized Mobility Management Entity with a three-tiered architecture. Our experimentalsetup resembles a typical data centervirtualization environment. We provide a detailed description of our experimental setup and procedures. We solve our resulting queueing network by using the Queueing Networks Analyzer (QNA), Jackson's networks, and Mean Value Analysis methodologies, and compare them in terms of estimation error. Results show that, for medium and high workloads, QNA method achieves less than half of error compared to the standard techniques. For low workloads, the threemethods produce an error lower than 10%.

The introduction of Narrowband Internet of Things (NB-IoT) as a cellular (NB-IoT) technology aims to support massive Machine-Type Communications applications. These applications are characterized by massive connections from a large number of low-complexity and low-power devices. One of the goals of NB-IoT is to improve coverage extension beyond existing cellular technologies. In order to do that, NB-IoT introduces transmission repetitions and different bandwidth allocation configurations in uplink. These new transmission approaches yield many transmission options in uplink. In this paper, we propose analytical expressions that describe the influence of these new approaches in the transmission. Our analysis is based on the Shannon theorem. The transmission is studied in terms of the required Signal to Noise Ratio, bandwidth utilization, and energy per transmitted bit. Additionally, we propose an uplink link adaptation algorithm that contemplates these new transmission approaches. The conducted evaluation summarizes the influence of these approaches. Furthermore, we present the resulting uplink link adaptation from our proposed algorithm sweeping the device's coverage.