Long Range Wide Area Network (LoRaWAN) has emerged as a leading Low Power Wide Area Network (LPWAN) solution for enabling connectivity in Internet of Things (IoT) wireless networks. The network slicing paradigm facilitates multi-tenancy and allows heterogeneous applications with diverse Quality of Service (QoS) requirements to coexist in various IoT use cases. This article addresses the radio resource allocation problem in a multi-tenant LoRaWAN network, where each tenant may serve multiple applications supported by Network Slicing (NS). To this end, we propose an optimization model to determine the optimal channel and Spreading Factor (SF) allocation for each end device in a multi-operator environment with heterogeneous applications. The model maximizes aggregated normalized throughput while ensuring fairness among Network Operators (NOs), employing linearization strategies to solve the problem efficiently. Our evaluation demonstrates the maximum achievable LoRaWAN network capacity to accommodate diverse IoT smart city applications from different tenants. The results confirm that optimal resource allocations can be achieved within reasonable timeframes while meeting all requirements, including fair resource distribution among slices, compliance with regulatory constraints such as duty cycle limitations, assurance of QoS metrics (throughput, delay, and packet delivery ratio) for each NO, and full isolation of resources allocated to different NOs.

Industry 4.0 arises to realize smart factories by the assistance and even the full automation of the different industrial manufacturing processes. Such industry digitization requires high-performance wireless connectivity to convey the traffic heterogeneity of the emerging wireless-enabled Industry 4.0 services such as mobile robots-assisted production. Albeit all eyes are on the Fifth Generation (5G) to meet the requirements imposed by this traffic, the use of alternative wireless access technologies is appealing to reduce the deployment costs of private 5G networks. More precisely, part of the network traffic generated by non-critical data-hungry services such as streaming-based X-Reality (XR) assisted workers could be served, for instance, by Wireless Fidelity (Wi-Fi). Recent 5G standards include capabilities for both facilitating the integration of non3GPP Wireless Access Technologies (WATs), such as Wi-Fi, and traffic steering among the available WATs. In this article, we assess the throughput gain brought by 5G multi-connectivity capabilities in an industrial multi-WAT Radio Access Network (RAN). The industrial scenario considered and setup resemble a real smart factory use case. Last, we translate this throughput gain into an increase of the attainable 5G aggregated capacity for Ultra-Reliable and Low-Latency Communications (URLLC) services as a function of their requirements and numerology.

Multi-Wireless Access Technology (WAT) Radio Access Networks (RANs) are becoming a key enabler in 5G and beyond networks due to the public spectrum scarcity, the level of signal confinement and security offered by some wireless technologies (e.g., Light Fidelity (Li-Fi)), and the reduction of the deployment and operational costs. For instance, Wireless Fidelity (Wi-Fi) technology is cheaper and easier to manage than 5G, and leveraging their already deployed infrastructures contributes to capital expenditures saving. Developing autonomous radio resource provisioning (RRP) solutions is fundamental to cost-effectively achieve the zero-touch management in private 5G networks while fulfilling the service requirements. However, modelling the Key Performance Indicators of the radio interface in 5G and beyond is a complex task that requires high-domain knowledge. Furthermore, the resulting models, as well as solving the respective RRP optimization problem using exact methods usually offer a high computational complexity, especially in multi-WAT scenarios. In order to cope with these issues, in this work, we propose an initial design of a Deep Reinforcement Learning-assisted solution for the RRP in a multi-WAT private 5G network. Furthermore, we contex-tualize the solution in the Open RAN architecture framework. A simulation-based proof-of-concept validates the proposal’s proper design and operation considering a realistic private 5G network scenario.

Time-Sensitive Networking (TSN) and Deterministic Networking (DetNet) technologies are increasingly recognized as key levers of the future 5G transport networks (TNs) due to their capabilities for providing deterministic Quality-ofService and enabling the coexistence of critical and best-effort services. Additionally, they rely on programmable and costeffective Ethernet-based forwarding planes. In this article, we address the flow allocation problem in 5G backhaul networks realized as asynchronous TSN networks, whose building block is the Asynchronous Traffic Shaper. We propose an offline solution, dubbed Next Generation Transport Network Optimizer (NEPTUNO), that combines exact optimization methods and heuristic techniques and leverages data analytics to solve the flow allocation problem. NEPTUNO aims to maximize the flow acceptance ratio while guaranteeing the deterministic Qualityof-service requirements of the critical flows. We carried out a performance evaluation of NEPTUNO in terms of the degree of optimality, execution time, and flow rejection ratio. Furthermore, we compare NEPTUNO with two online baseline solutions. Online methods compute the flows allocation configuration right after the flow arrives at the network, whereas offline solutions like NEPTUNO compute a long-term configuration allocation for the whole network. Our results highlight the potential of the data analytics for the self-optimization of the future 5G TNs.

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.

Network Functions Virtualization facilitates the automation of the 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 for a virtualized Mobility Management Entity (vMME) with a three-tiered architecture running on a testbed that resembles a typical data center virtualization environment. We detail the 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, the QNA method achieves less than half of error compared to the standard techniques. For low workloads, the three methods produce an error lower than 10 percent. Finally, we show the usefulness of the model for performing the dynamic resource provisioning of the vMME experimentally.

Time-Sensitive Networking (TSN) and Deterministic Networking (DetNet) standards come to satisfy the needs of many industries for deterministic network services. That is the ability to establish a multi-hop path over an IP network for a given flow with deterministic Quality of Service (QoS) guarantees in terms of latency, jitter, packet loss, and reliability. In this work, we propose a reinforcement learning-based solution, which is dubbed LEARNET, for the flow scheduling in deterministic asynchronous networks. The solution leverages predictive data analytics and reinforcement learning to maximize the network operator's revenue. We evaluate the performance of LEARNET through simulation in a fifth-generation (5G) asynchronous deterministic backhaul network where incoming flows have characteristics similar to the four critical 5GQoS Identifiers (5QIs) defined in Third Generation Partnership Project (3GPP) TS 23.501 V16.1.0. Also, we compared the performance of LEARNET with a baseline solution that respects the 5QIs priorities for allocating the incoming flows. The obtained results show that, for the scenario considered, LEARNET achieves a gain in the revenue of up to 45% compared to the baseline solution.

Unmanned aerial vehicles (UAVs) is one of the promising technology in the future. A recent study claims that by 2026, the commercial UAVs, for both corporate and customer applications, will have an annual impact of 31 billion to 46 billion on the country's GDP. Shortly, many UAVs will be flying everywhere. For this reason, there is a need to suggest efficient mechanisms for preventing the collisions among the UAVs. Traditionally, the collisions are prevented using dedicated sensors, however, those would generate uncertainty in their reading due to their external conditions sensitivity. From another side, the use of those sensors could create an extra overhead on the UAVs in terms of cost and energy consumption. To deal with these challenges, in this paper, we have suggested a solution that leverages the chance-constrained optimization technique for avoiding the collision in an energy-efficient manner. Building on the expressions for the non-central Chi-square CDF and expected value, and through the convexification of the resulting expressions, the chance-constrained optimization program is transformed into a convex Mixed Binary Nonlinear one. The resulting program allows us to find the optimal safety distance that extends UAVs life-time and allows every UAV to move with a guaranteed probability of collision between any pair of UAVs.

Network slicing allows different applications and network services to be deployed on virtualized resources running on a common underlying physical infrastructure. Developing a scalable system for the orchestration of end-to-end (E2E) mobile network slices requires careful planning and very reliable algorithms. In this paper, we propose a novel E2E Network Slicing Orchestration System (NSOS) and a Dynamic Auto-Scaling Algorithm (DASA) for it. Our NSOS relies strongly on the foundation of a hierarchical architecture that incorporates dedicated entities per domain to manage every segment of the mobile network from the access, to the transport and core network part for a scalable orchestration of federated network slices. The DASA enables the NSOS to autonomously adapt its resources to changes in the demand for slice orchestration requests (SORs) while enforcing a given mean overall time taken by the NSOS to process any SOR. The proposed DASA includes both proactive and reactive resource provisioning techniques. The proposed resource dimensioning heuristic algorithm of the DASA is based on a queuing model for the NSOS, which consists of an open network of G/G/m queues. Finally, we validate the proper operation and evaluate the performance of our DASA solution for the NSOS by means of system-level simulations.

5G is the next telecommunications standards that will enable the sharing of physical infrastructures to provision ultra shortlatency applications, mobile broadband services, Internet of Things, etc. Network slicing is the virtualization technique that is expected to achieve that, as it can allow logical networks to run on top of a common physical infrastructure and ensure service level agreement requirements for different services and applications. In this vein, our paper proposes a novel and complete solution for planning network slices of the LTE EPC, tailored for the enhanced Mobile BroadBand use case. The solution defines a framework which consists of: i) an abstraction of the LTE workload generation process, ii) a compound traffic model, iii) performance models of the whole LTE network, and iv) an algorithm to jointly perform the resource dimensioning and network embedding. Our results show that the aggregated signaling generation is a Poisson process and the data traffic exhibits self-similarity and long-range-dependence features. The proposed performance models for the LTE network rely on these results. We formulate the joint optimization problem of resources dimensioning and embedding of a virtualized EPC and propose a heuristic to solve it. By using simulation tools, we validate the proper operation of our solution.

HTTP Adaptive Streaming (HAS) is becoming the de-facto video delivery technology over best- effort networks nowadays, thanks to the myriad advantages it brings. However, many studies have shown that HAS suffers from many Quality of Experience (QoE)-related issues in the presence of competing players. This is mainly caused by the selfishness of the players resulting from the decentralized intelligence given to the player. Another limitation is the bottleneck link that could happen at any time during the streaming session and anywhere in the network. These issues may result in wobbling bandwidth perception by the players and could lead to missing the deadline for chunk downloads, which result in the most annoying issue consisting of rebuffering events. In this paper, we leverage the Software-Defined Networking paradigm to take advantage of the global view of the network and its powerful intelligence that allows reacting to the network changing conditions. Ultimately, we aim at preventing the re-buffering events, resulting from deadline misses, and ensuring high QoE for the accepted clients in the system. To this end, we use Deterministic Network Calculus (DNC) to guarantee a maximum delay for the download of the video chunks while maximizing the perceived video quality. Simulation results show that the proposed solution ensures high efficiency for the accepted clients without any rebuffering events which result in high user QoE. Consequently, it might be highly useful for scenarios where video chunks should be strictly downloaded on- time or ensuring low delay with high user QoE such as serving video premium subscribers or remote control/driving of an autonomous vehicle in future 5G mobile networks.

Network Function Virtualization ecosystem enables the automation of deployment and scaling of softwarized network services (SNSs), thus reducing their operational expenditures. This enables operators to handle workload fluctuations, to keep the desired performance, with great agility and reduced costs. However, to realize the automation of such management practices, it is needed to determine the amount of required resources to allocate the SNS so that its performance requirements are met. This problem is commonly referred to as resources dimensioning problem. In this paper, we address the derivation of a closed-form expression for the optimal resources dimensioning of an SNS in terms of cost or energy efficiency. The performance requirement considered for the SNS is a limit on its mean response time. The performance model considered for the SNS is practical and accurate. The usefulness of the derived closed-form expression is successfully validated by means of simulation. The scenario considered for the validation is a video optimization chain located at the SGi-LAN of a mobile network.

The key concept beneath Multi-Access Edge Computing (MECs) is to place cloud resources in closer proximity to end-users, through the installation of small-scale cloud infrastructures at the network edge. In MEC environments, we identify two issues: 1) data about users' activities are not always available, and 2) the available virtual resource planning mechanisms (i.e., algorithms for the placement of Virtual Network Functions - VNFs) are not efficient enough to fulfill the QoS requirements and deployment costs. In this vein, we design a layered framework to define the presence of Mobile BroadBand User Equipments (UEs) and automate the underlying virtual resource placement and management based on the Fuzzy Logic Controller paradigm (FLC). Experimentation results show that our framework, compared to baseline solutions, achieves good performance results; the end-to-end delay is enhanced by 25%, the resource consumption is reduced by 30%, and the environmental impact, reflected by the carbon footprint that depends on the amount of deployed Virtual Machines (VMs), is reduced by 50%.

Narrowband Internet of Things (NB-IoT) technology emerged in Release 13 as one of the solutions to provide cellular IoT connectivity. NB-IoT is designed to achieve better indoor coverage, support of a massive number of low-throughput devices, with relaxed delay requirements, and lower energy consumption. Particularly, the extensive coverage of NB-IoT poses a great challenge. The goal is to cover devices in areas previously inaccessible by cellular networks due to penetration losses or remote locations. To solve this, NB-IoT utilizes bandwidth reduction and repetitions. However, for the targeted low range of signal to noise ratio (SNR), the coverage gain due to repetitions can be significantly limited by the performance of the channel estimator. In this paper, we provide an analytical evaluation framework to study the performance of NB-IoT. Our analysis includes the limitations due to realistic channel estimation (CE) and delves into the estimation of the SNR. Additionally, the conducted evaluation shows the impact of the coverage extension in the final performance of the NB-IoT user equipment (UE) in terms of uplink packet transmission latency and battery lifetime. Specifically, regarding UE's battery lifetime, for a maximum coupling loss (MCL) of 164 dB, realistic CE evaluations obtain a battery lifetime reduction of approximately 90% compared to ideal CE.

Several solutions have been proposed in the literature to address the Unmanned Aerial Vehicles (UAVs) collision avoidance problem. Most of these solutions consider that the ground controller system (GCS) determines the path of a UAV before starting a particular mission at hand. Furthermore, these solutions expect the occurrence of collisions based only on the GPS localization of UAVs as well as via object-detecting sensors placed on board UAVs. The sensors' sensitivity to environmental disturbances and the UAVs' influence on their accuracy impact negatively the efficiency of these solutions. In this vein, this paper proposes a new energy- and delay-aware physical collision avoidance solution for UAVs. The solution is dubbed EDCUAV. The primary goal of EDC-UAV is to build in-flight safe UAVs trajectories while minimizing the energy consumption and response time. We assume that each UAV is equipped with a global positioning system (GPS) sensor to identify its position. Moreover, we take into account the margin error of the GPS to provide the position of a given UAV. The location of each UAV is gathered by a cluster head, which is the UAV that has either the highest autonomy or the greatest computational capacity. The cluster head runs the EDC-UAV algorithm to control the rest of the UAVs, thus guaranteeing a collision free mission and minimizing the energy consumption to achieve different purposes. The proper operation of our solution is validated through simulations. The obtained results demonstrate the efficiency of EDC-UAV in achieving its design goals.

The network softwarization paradigm, enabled by Network Function Virtualization (NFV), facilitates the automation of management operations and orchestration of future networks, thus reducing their operational expenditures. The envisioned management practices include the introduction of automation in the scaling of network services. This may enable operators to handle workload fluctuations, to keep the desired performance, with great agility and reduced costs. This procedure introduces a non-negligible delay in allocating or releasing virtual resources. Therefore, waiting until the system is overloaded or underutilized so as to scale resources up or down could negatively impact the users' Quality of Experience, or lead to inefficient resource utilization. In this vein, this paper proposes a novel and agile Dynamic Auto Scaling Algorithm for the Control Plane (CP) of the Long Term Evolution' (LTE) virtualized Evolved Packet Core (vEPC). The resources dimensioning stage of the algorithm is based on an original queuing model for the CP. To model the CP, we use an open network of G/G/m queues. We also provide expressions to derive the steady state transition probabilities of the queuing network. Finally, we validate the proper operation of our solution using accurate simulation tools.

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.

In this paper, we study the performance of the YouTube service over 3G Long Term Evolution (LTE) by means of dynamic network simulations. We consider a typical configuration of an LTE network for TCP traffic and the traffic generation model for YouTube `Flash' videos downloaded onto a personal computer (PC). Furthermore, in order to achieve more reliable results in the simulations, the main configuration parameters are obtained for TCP Cubic congestion control algorithm used by YouTube media servers. The results obtained show that: the number of pauses experimented by users during video download are heavily influenced by the cell load, but the same is not true for pause duration; most of the packet losses occur during initial burst due to the TCP adaptation to the BDP of the link, and, unlike packet losses during throttling phase, these are not depend on radio link quality; and in most cases the user do not use the maximum data rate achievable in the LTE interface.