Unmanned aerial vehicles (UAVs) have recently attracted both academia and industry representatives due to their utilization in tremendous emerging applications. Most UAV applications adopt visual line of sight (VLOS) due to ongoing regulations. There is a consensus between industry for extending UAVs’ commercial operations to cover the urban and populated area-controlled airspace beyond VLOS (BVLOS). There is ongoing regulation for enabling BVLOS UAV management. Regrettably, this comes with unavoidable challenges related to UAVs’ autonomy for detecting and avoiding static and mobile objects. An intelligent component should either be deployed onboard the UAV or at a multiaccess-edge computing (MEC) that can read the gathered data from different UAV’s sensors, process them, and then make the right decision to detect and avoid the physical collision. The sensing data should be collected using various sensors but not limited to Lidar, depth camera, video, or ultrasonic. This article proposes probabilistic and deep-reinforcement-learning (DRL)-based algorithms for avoiding collisions while saving energy consumption. The proposed algorithms can be either run on top of the UAV or at the MEC according to the UAV capacity and the task overhead. We have designed and developed our algorithms to work for any environment without a need for any prior knowledge. The proposed solutions have been evaluated in a harsh environment that consists of many UAVs moving randomly in a small area without any correlation. The obtained results demonstrated the efficiency of these solutions for avoiding the collision while saving energy consumption in familiar and unfamiliar environments.

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.

Fifth Generation (5G) is here to accelerate the digitization of economies and society, and open up innovation opportunities for verticals. A myriad of 5G-enabled use cases has been identified across disparate sectors like tourism, retail industry, and manufacturing. Many of the networks of these use cases are expected to be private networks, that is, networks intended for the exclusive use of an enterprise customer. This article provides an overview of the technical aspects in private 5G networks. We first identify the key requirements and enabling solutions for private 5G networks. Then, we review the latest 3rd Generation Partnership Project (3GPP) Release 16 capabilities to support private 5G networks. Next, we provide architecture proposals for single site private networks, including the scenario in which the radio access network (RAN) is shared. Afterwards, we address mobility and multi-site private 5G network scenarios. Finally, we identify key challenges for private 5G networks.

The exponential growth of data traffic and connected devices, and the reduction of latency and costs, are considered major challenges for future mobile communication networks. The satisfaction of these challenges motivates revisiting the architecture of these networks. We propose an SDN-based design of a hierarchical architecture for the 5G packet core. In this article we focus on the design of its access cloud with the goal of providing low latency and scalable Ethernet-like support to terminals and MTC devices including mobility management. We examine and address its challenges in terms of network scalability and support for link-level mobility. We propose a link-level architecture that forwards frames from and to edge network elements (AP and routers) with a label that identifies the APs through which the terminal is reachable. An SDN local controller tracks and updates the users' location information at the edge network elements. Additionally, we propose to delegate in SDN local controllers the handling of non-scalable operations, such as broadcast and multicast messages, and network management procedures.

In this work we present the characterization of the mobile network traffic generated by one of the most relevant social networking applications: YouTube. Understanding its characteristics is of major importance to evaluate its impact on mobile networks and optimize network or application design. For this purpose, we have performed a set of experiments capturing traffic from Apple iOS and Android terminals connected to 3G mobile networks. Our results show that the video formats are 3GP formats, which use lower encoding rates than in other networks. As in accesses from other networks, YouTube servers transfer the video clip with an initial burst and a throttling phase, but they apply different parameters for clients located in mobile networks. Additionally, the mobile terminal can strongly influence the download. Our results with high-end Android terminals show that the client implements a dual-threshold buffer policy that interrupts and resumes the download depending on its buffer occupancy. Our results with iOS terminals show that the video clip download is not interrupted by the client, and a large amount of data may be accumulated at the player buffer. In mid-range Android terminals the TCP receive window may additionally throttle the download if the player buffer is filled up.

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.