With the proliferation of use-cases envisioned to be supported by 5G networks, the focus is not only on the performance, but also on the service agility and elasticity. Cloud native principal is a methodology of designing lightweight, isolated-context, and deployable at scale applications that natively exploit the features of cloud. However, supporting telco applications (e.g., 4G/5G services) in the cloud brings up many challenges, such as the coexistence of physical and virtual functions, (near) realtime resource provisioning, service continuity, and strict latency and data rates. In this paper, we propose Kube5G, as a realizable agile service platform in support of 4G/5G cloud native applications. Kube5G introduces a novel approach in building and packaging a cloud-native compliant telco network function (NF) in a form of nested well-defined layers. In addition, we present a workflow for continuous integration and development operations in support of multi-version network functions (physical, virtual and cloud functions). We also present a concrete prototype implementation of Kube5G with experimental results indicating its efficiency and highlighting user and network perceived performance.

In this demo, we present 5G network automation in cloud-native environment. Our proposition is to demonstrate the network automation using Kubernets as container orchestration and automating application deployment, while using Openshift Operator as a tool to manage complex services, such as 5G services. For this purpose, we use the containerized OpenAirInterface (OAI) to deploy the network and demonstrate the automatability, such as dynamic switch of RAN between monolithic base station and disaggregated RAN (i.e. Distributed Unit-DU and Centralized Unit-CU), and auto-configuration.

In this paper, we propose a network planning and provisioning framework that optimizes deployment cost in C-RAN based 5G networks. Our framework is based on a Mixed Integer Quadratically Constrained Programming (MIQCP) model which optimizes ``virtualized'' 5G service chain deployment cost while performing adequate provisioning to address user demand and performance requirements. We use two realistic scenarios to showcase that our framework can be applied to different types of deployments and discuss the computational cost and scalability of our solution.

In Long Term Evolution (LTE) networks, User Equipments (UE)s should proceed Random Access CHannel (RACH) procedure to attach to the Base Station and access the channel. One limitation of this procedure is the congestion that may appear when high number of UEs are simultaneously trying to attach to the channel. Such use-case happens when high number of Machine Type Communication (MTC) devices are deployed in one LTE cell. In order to evaluate the RACH performances, in terms of success, collision and idle probabilities, when the traffic load is high, accurate models are needed. However, most of existing models ignore one important constraint, which is the fact that the eNB can knowledge only a limited number of UEs in each RACH round, leading to a mis-formulation of these metrics in the context of LTE, and especially in the presence of high number of devices competing for the channel access. In this paper, we tackle the above-mentioned issue by devising a new model for the RACH procedure taking in consideration this constraint. Computer simulation demonstrates that unlike the existing models, our proposed model achieve high accuracy to estimate the performance of the RACH procedure, whatever the traffic load.

Recently, unmanned aerial vehicles (UAVs), or drones, have attracted a lot of attention, since they represent a new potential market. Along with the maturity of the technology and relevant regulations, a worldwide deployment of these UAVs is expected. Thanks to the high mobility of drones, they can be used to provide a lot of applications, such as service delivery, pollution mitigation, farming, and in the rescue operations. Due to its ubiquitous usability, the UAV will play an important role in the Internet of Things (IoT) vision, and it may become the main key enabler of this vision. While these UAVs would be deployed for specific objectives (e.g., service delivery), they can be, at the same time, used to offer new IoT value-added services when they are equipped with suitable and remotely controllable machine type communications (MTCs) devices (i.e., sensors, cameras, and actuators). However, deploying UAVs for the envisioned purposes cannot be done before overcoming the relevant challenging issues. These challenges comprise not only technical issues, such as physical collision, but also regulation issues as this nascent technology could be associated with problems like breaking the privacy of people or even use it for illegal operations like drug smuggling. Providing the communication to UAVs is another challenging issue facing the deployment of this technology. In this paper, a comprehensive survey on the UAVs and the related issues will be introduced. In addition, our envisioned UAV-based architecture for the delivery of UAV-based value-added IoT services from the sky will be introduced, and the relevant key challenges and requirements will be presented.

Next generation cellular networks (5G) have to deal with massive deployment of machine-type-communication (MTC) devices, expected to cause congestion and system overload in both the radio access network (RAN) and the core network (CN). Moreover, not only would the network suffer from the system overload, but also the MTC devices would experience high latency to access the channel and high power consumption due to the retransmission attempts. Indeed, power consumption is a critical issue in MTC, as the devices are not plugged into the electrical supply, e.g., in the case of sensor devices. To alleviate system overload (caused by the massive MTC deployment), the 3GPP proposed the group paging (GP) method. However, its performances dramatically decrease when increasing the number of MTC devices being paged. In this paper, we devise a novel method, named further improvement-traffic scattering for group paging (FI-TSFGP), which aims to improve the performance of GP when the number of MTC devices is high. FI-TSFGP scatters the paging operation of the MTC devices over a GP interval instead of letting all of the devices start the channel access procedure at nearly the same time. By doing so, FI-TSFGP achieves high-channel access probability for MTC devices, leading to the reduction of both the channel access latency and power consumption. Compared to GP and two other schemes, simulation results clearly demonstrate the high performance of FI-TSFGP in terms of: success and collision probabilities, average access delay, average number of preamble transmissions, and ultimately energy conservation.

Machine-Type-Communication (MTC) is a key enabler for a variety of novel smart systems, such as smart grid, eHealth, Intelligent Transport System (ITS), and smart city, opening the area of the cyber physical systems. These systems may require the use of a huge number of MTC devices, which will put a great pressure on the whole network, i.e. Radio Access Network (RAN) and Core Network (CN) parts, resulting in the shape of congestion and system overload. Aiming at better evaluating the network performance under the existence of MTC traffic and also the effectiveness of the congestion control methods, the 3rd Generation Partnership Project (3GPP) group has proposed two traffic models: Uniform Distribution (over 60 s) and Beta Distribution (over 10 s). In this paper, a recursive operation-based analytical model, namely General Recursive Estimation (GRE), for modeling the performance of RACH procedure in the existence of MTC with Beta traffic is proposed. In order to show the effectiveness of our analytical model GRE, many metrics have been considered, such as the total number of MTC devices in each Random Access (RA) slot, the number of success MTC devices in each RA slot, and the Cumulative Distribution Function (CDF) of preamble transmission. Numerical results demonstrate the accuracy of GRE. Moreover, our model GRE could be used to model the performance of RACH procedure with any type of traffic.

Machine-Type-Communication (MTC) is a promising service of the envisioned 5G mobile networks. However, deploying a massive number of MTC devices in these networks remains a challenge due to the overload that may appear at the Radio Access Network (RAN), hence degrading the Quality of Services (QoS) for both MTC and Non-MTC devices. One of the methods used to address the congestion's problem in RAN is Group Paging (GP), wherein a single message is used to activate a group of devices. Whilst the GP method has several advantages, its performance quickly decreases when the number of MTC devices increases. In this paper, we devise a new method, namely Traffic Scattering For Group Paging (TSFGP) to improve the performance of the GP method for massive deployment of MTC devices. Numerical results demonstrate that TSFGP highly improves the performance of GP in terms of several performance metrics, such as success probability, collision probability, and access delay.

Machine-type-Communication (MTC) is seen as a major service in next generation cellular mobile networks. However, the forecasted very large number of MTC devices may overload the RAN (Radio Access Network) part of the network, which may impact Non-MTC communications. Group paging is considered as one of the most efficient mechanisms proposed to alleviate the problem of the RAN overload. In this paper, we introduce a new solution to improve the performance of the current group paging method and overcome its disadvantages. The proposed solution is intended for MTC devices in the RRC CONNECTED OUT OF SYNC state, in which MTC devices have an RRC context without being synchronized with the network. Numerical results demonstrate that the proposed solution highly improves the performance of existing group paging mechanisms.

Deploying a massive number of MTC (Machine - Type - Communication) devices in the current cellular mobile networks represents a great challenge as they may cause congestion and system overload for both RAN (Radio Access Network) and CN (Core Network) parts. To address this issue, we propose a novel algorithm, named Multi-Channel Slotted ALOHA-Optimal Estimation (MCSA-OE), which estimates the network status (the number of active devices), and thus better controlling the RAN access. Unlike most existing methods that consider only one channel, MCSA-OE uses the statistics of all the channels in order to estimate the number of arrivals (UE and MTC devices) in each RA (Random Access) slot. Simulation results demonstrate that MCSA-OE well tracks the number of arrivals as long as they are smaller than ln(RR), where R is the number of channels. Moreover, we propose to use MCSA-OE estimation to dynamically adjust the acbBarringFactor of the Access Class Barring (ACB) mechanism. Again, simulation results show that the behavior of our proposition merely tends to that of the best acknowledgment case, i.e. when the number of arrivals in each RA slot is well known.