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The development of cellular networks is driving the rapid growth of wireless communications. With the advent of the 5th Generation (5G) towards the future of the 6th Generation (6G) dedicated to achieving strong growth in traffic while reducing energy consumption, there is a need to solve the problems facing leveraging of these networks’ advantages and support both operators and mobile users. The main challenges for wireless communications are power consumption, Quality of Service (QoS), and the blind areas of a Non-Line-Of-Sight (NLOS) between mobile users and the Base Station (BS). The Intelligent Reflective Surface (IRS) of a reconfigurable meta material is a promising solution for solving some of the challenges of wireless communications. Additionally, it enhances the QoS of the received signals without the need for a power source to operate. Hence, it does not constitute an additional burden as it consists of passive elements. From the other hand, it provides the perfect solution to cover mobile users in blind areas without the need to deploy extra expensive BSs. In this work, we propose to equip buses by IRS allowing them to act as mobile IRS. These buses will become a relay for the surrounding moving vehicles, represented as taxies in the performance Section of this paper. Practically speaking, not all buses have to be IRS equipped. We propose various approaches for selecting the best buses equipped with IRS. In the first optimization approach, we adapt the classical IRS selections methods used in static context to the mobile case. It uses a Multi Integer Linear Programming (MILP) which gives optimal results but with a very long processing time. Thus, we propose a neural-network to learn the result of the MILP. As an alternative solution, another approach is proposed using a Markov decision problem (MDP) relying on Long Short-Term Memory (LSTM) to predict the positions of the surrounding moving vehicles. It is used to solve the optimization problem with the performance criteria targeted for each session. The performance of the proposed approaches are validated based on bus and taxi dataset for the city of Rome in Italy.

As data-driven solutions will become an important component of next-generation networks, we were faced with the difficulty of developing cross-domain datasets for training machine learning models. In order to understand how external sources of data should be associated with mobile network data at cell level, we have derived a method for splitting simple geometric coverage models for base stations to obtain coverage models for sectorized cells. Then, we developed a method to compare the coverage models with a ground truth of real measurements. We also proposed to use the notions of convex and concave hulls to quantify when a geometry is undersized or oversized. The two types of geometry that were evaluated were the Voronoi polygons and circular shapes, with sector split. All frequency bands considered, the results have shown that Voronoi sector split model combined with upscaling the coverage shapes was the closest to the ground truth, with an average recall of 0.74. Since upscaling Voronoi polygons should be a good practice to improve coverage modelling, we have also proposed an approach using more affordable data (i.e. cell level aggregates) instead of user locations to find the best scaling factor. All frequencies combined, we have observed an average increase of 0.13 points in the recall between the diagram with the default scale and the scale-tuned diagram.

Edge Computing and Network Function Virtualization (NFV) concepts can improve network processing and multi-resources allocation when intelligent optimization algorithms are deployed. Multiservice offloading and allocation approaches pose interesting challenges in the current and next-generation vehicle networks. The state-of-the-art optimization approaches still formulate exact algorithms, and tune approximation methods to get sufficient solutions. These approaches are data-centric that aim to use heterogeneous data inputs to find the near optimal solutions. In the context of connected and autonomous vehicles (CAVs), these techniques show an exponential computational time and deal only with small and medium scale networks. Therefore, we are motivated by using recent Deep Reinforcement Learning (DRL) techniques to learn the behavior of exact optimization algorithms while enhancing the Quality of Service (QoS) of network operators and satisfying the requirements of the next-generation Autonomous Vehicles (AVs). DRL algorithms can improve AVs service offloading and optimize edge resources. An Optimal Virtual Edge Autopilot Placement (OVEAP) algorithm is proposed using Integer Linear Programming (ILP). Moreover, an autopilot placement protocol is presented to support the algorithm. Optimal allocation and Virtual Network Function (VNF) placement and chaining of the autopilot, based on several new constraints such as computing and networking loads, network edge infrastructure, and placement cost, are designed. Further, a DRL approach is formulated to deal with dense Internet of Autonomous Vehicle (IoAV) networks. Extensive simulations and evaluations are carried out. Results show that the proposed allocation strategies outperform the state-of-the-art solutions and give better performance in terms of Total Edge Servers Utilization, Total Edge Servers Allocation Time, and Successfully Allocated autopilots.

Abstract Background Brain perfusion is most likely to be impaired in border zone regions, and clearance of emboli will be most impaired in these regions of least blood flow. Severe occlusive disease of the internal carotid artery causes both embolization and decreased perfusion as well as some cardiac diseases that cause microembolization. Objectives To differentiate between hypoperfusion and microemboli as etiology of acute ischemic stroke in watershed zone. Subject and methods Fifty patients of acute ischemic stroke in watershed zones were recruited within 7 days from stroke onset. Methods used were transcranial Doppler (TCD) monitoring for the intracranial vessels to detect microembolic signals and magnetic resonance imaging (MRI) perfusion image to detect hypoperfusion signs. Results We detect embolic causes of watershed infarction (WSI) by using TCD with 61.1% sensitivity and 84.4% specificity and hypoperfusion causes of WSI by using MRI perfusion studies with 94.9% sensitivity and 54.5% specificity. Conclusion We detected the etiology of WSI, either embolic by using TCD or hypoperfusion by using MRI perfusion. The embolic causes of WSI usually cause external or mixed WSI, and hypoperfusion causes of WSI cause internal WSI.