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Connectivity is an important key performance indicator and a focal point of research in large-scale wireless networks. Due to path-loss attenuation of electromagnetic waves, direct wireless connectivity is limited to proximate dev...
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Connectivity is an important key performance indicator and a focal point of research in large-scale wireless networks. Due to path-loss attenuation of electromagnetic waves, direct wireless connectivity is limited to proximate devices. Nevertheless, connectivity among distant devices can still be attained through a sequence of consecutive multi-hop communication links, which enables routing and disseminating legitimate information across wireless ad hoc networks. Multi-hop connectivity is also foundational for data aggregation in the Internet of things (IoT) and cyberphysical systems (CPS). On the downside, multi-hop wireless transmissions increase susceptibility to eavesdropping and enable malicious network attacks. Hence, security-aware network connectivity is required to maintain communication privacy, detect and isolate malicious devices, and thwart the spreading of illegitimate traffic (e.g., viruses, worms, falsified data, illegitimate control, etc.). In 5G and beyond networks, an intricate balance between connectivity, privacy, and security is a necessity due to the proliferating IoT and CPS, which are featured with massive number of wireless devices that can directly communicate together (e.g., device-to-device, machine-to-machine, and vehicle-to-vehicle communication). In this regards, graph theory represents a foundational mathematical tool to model the network physical topology. In particular, random geometric graphs (RGGs) capture the inherently random locations and wireless interconnections among the spatially distributed devices. Percolation theory is then utilized to characterize and control distant multi-hop connectivity on network graphs. Recently, percolation theory over RGGs has been widely utilized to study connectivity, privacy, and security of several types of wireless networks. The impact and utilization of percolation theory are expected to further increase in the IoT/CPS era, which motivates this tutorial. Towards this end, we first introduce the preliminaries of graph and percolation theories in the context of wireless networks. Next, we overview and explain their application to various types of wireless networks.
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With the increasing demand for greener and more energy efficient transportation solutions, electric vehicles (EVs) have emerged to be the future of transportation across the globe. However, currently, one of the biggest bottleneck...
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With the increasing demand for greener and more energy efficient transportation solutions, electric vehicles (EVs) have emerged to be the future of transportation across the globe. However, currently, one of the biggest bottlenecks of EVs is the battery. Small batteries limit the EVs driving range, while big batteries are expensive and not environmentally friendly. One potential solution to this challenge is the deployment of charging roads, i.e., dynamic wireless charging systems installed under the roads that enable EVs to be charged while driving. In this paper, we use tools from stochastic geometry to establish a framework that enables evaluating the performance of charging roads deployment in metropolitan cities. We first present the course of actions that a driver should take when driving from a random source to a random destination in order to maximize dynamic charging during the trip. Next, we analyze the distribution of the distance to the nearest charging road. This distribution is vital for studying multiple performance metrics such as the trip efficiency, which we define as the fraction of the total trip spent on charging roads. Next, we derive the probability that a given trip passes through at least one charging road. The derived probability distributions can be used to assist urban planners and policy makers in designing the deployment plans of dynamic wireless charging systems. In addition, they can also be used by drivers and automobile manufacturers in choosing the best driving routes given the road conditions and level of energy of EV battery.
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With the increasing demand for greener and more energy efficient transportation solutions, electric vehicles (EVs) have emerged to be the future of transportation across the globe. However, currently, one of the biggest bottleneck...
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With the increasing demand for greener and more energy efficient transportation solutions, electric vehicles (EVs) have emerged to be the future of transportation across the globe. However, currently, one of the biggest bottlenecks of EVs is the battery. Small batteries limit the EVs driving range, while big batteries are expensive and not environmentally friendly. One potential solution to this challenge is the deployment of charging roads, i.e., dynamic wireless charging systems installed under the roads that enable EVs to be charged while driving. In this paper, we use tools from stochastic geometry to establish a framework that enables evaluating the performance of charging roads deployment in metropolitan cities. We first present the course of actions that a driver should take when driving from a random source to a random destination in order to maximize dynamic charging during the trip. Next, we analyze the distribution of the distance to the nearest charging road. This distribution is vital for studying multiple performance metrics such as the trip efficiency, which we define as the fraction of the total trip spent on charging roads. Next, we derive the probability that a given trip passes through at least one charging road. The derived probability distributions can be used to assist urban planners and policy makers in designing the deployment plans of dynamic wireless charging systems. In addition, they can also be used by drivers and automobile manufacturers in choosing the best driving routes given the road conditions and level of energy of EV battery.
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Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Their mobility and their ability to establish line of sight (LOS) links with the users ma...
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Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Their mobility and their ability to establish line of sight (LOS) links with the users made them key solutions for many potential applications. In the same vein, artificial intelligence (AI) is growing rapidly nowadays and has been very successful, particularly due to the massive amount of the available data. As a result, a significant part of the research community has started to integrate intelligence at the core of UAVs networks by applying AI algorithms in solving several problems in relation to drones. In this article, we provide a comprehensive overview of some potential applications of AI in UAV-based networks. We also highlight the limits of the existing works and outline some potential future applications of AI for UAVs networks.
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Owing to the utilization of part of the high-frequency band, utilizing part of the large available bandwidth in the high-frequency band, wireless backhauling is a feasible technology with no considerable performance loss. In this ...
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Owing to the utilization of part of the high-frequency band, utilizing part of the large available bandwidth in the high-frequency band, wireless backhauling is a feasible technology with no considerable performance loss. In this context, integrated access and backhaul (IAB) has been proposed by the Third Generation Partnership Project (3GPP) to reduce the expenses related to the deployment of fiber optics for 5G and beyond networks. In this paper, first, a brief introduction of IAB based on the 3GPP release is presented. Then, the existing research on IAB networks based on 3GPP specifications and possible non-3GPP research extensions, such as the integration of IAB to cache-enabled, optical communication transport, and the non-terrestrial networks, are surveyed. Finally, the challenges and opportunities related to the development and commercialization of the IAB networks are discussed.
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One of the major challenges slowing down the use of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) is the limited onboard power supply, which reduces the flight time of a UAV. Using a tether to provide UAVs with po...
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One of the major challenges slowing down the use of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) is the limited onboard power supply, which reduces the flight time of a UAV. Using a tether to provide UAVs with power can be considered a reasonable compromise that will enhance the flight time while limiting the mobility of the UAV. In this article, we propose a system where ABSs are deployed at the centers of user hotspots to offload the traffic and assist terrestrial base stations. First, given the location of the ground station in the user hotspot (user cluster) and the spatial distribution of users, we compute the optimal inclination angle and length of the tether. Using these results, we compute the densities of the tethered UAVs deployed at different altitudes, which enables the tractable analysis of the interference in the considered setup. Next, using tools from stochastic geometry and an approach of dividing user clusters into finite frames, we analyze the coverage probability as a function of the maximum tether length, the density of accessible rooftops for UAV ground station deployment, and the density of clusters. We verify our findings using Monte Carlo simulations and draw multiple useful insights. For instance, we show that it is actually better to deploy UAVs at a fraction of the clusters, not all of them as it is usually assumed in the literature.
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The line-of-sight (LoS) condition is important to the quality of wireless communications, especially for millimeter-wave (mmWave) communications which are more sensitive to blockages than typical radio frequency (RF) communication...
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The line-of-sight (LoS) condition is important to the quality of wireless communications, especially for millimeter-wave (mmWave) communications which are more sensitive to blockages than typical radio frequency (RF) communications. Considering that one blockage would obstruct several channels simultaneously, recently, researchers have studied the correlation of LoS probabilities among multiple channels in the horizontal plane and shown its importance in system performance analysis. The main missing aspect of existing literature in this direction is that the heights of transceivers and blockages have not been considered in the analysis of the LoS correlation. However, the emerging hybrid cellular networks with unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) deployed at various altitudes necessitate the LoS analysis accounting for the vertical dimension. In this paper, using stochastic geometry, we formulate a novel joint-LoS-probability framework of two channels among three aerial/terrestrial devices to analyze the LoS correlation both in the horizontal and vertical dimensions. We derive the expression of the conditional and joint LoS probabilities. The proposed framework combines (i) accuracy (verified in simulation), (ii) generality (from adjustable parameters of the channels and environments), and (iii) fitting ability (from a cylindrical blockage model to a non-cylindrical blockage model). The numerical results show that the correlation is quite strong when the angle between the two communication channels is small, while it becomes weaker as this angle increases. Moreover, we introduce a potential application of the joint LoS probability in the optimal deployment of an aerial device. Compared to optimization based on the independence assumption of the LoS probabilities, the improvement in the coverage probability intuitively clarifies the advantage of the proposed joint-LoS-probability framework on the aerial-terrestrial network design.
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Wireless communications over Terahertz (THz)-band frequencies are vital enablers of ultra-high rate applications and services in sixth-generation (6G) networks. However, THz communications suffer from poor coverage because of inhe...
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Wireless communications over Terahertz (THz)-band frequencies are vital enablers of ultra-high rate applications and services in sixth-generation (6G) networks. However, THz communications suffer from poor coverage because of inherent THz features such as high penetration losses, significant molecular absorption, and severe path loss. To surmount these critical challenges and fully exploit the THz band, we explore a coexisting radio frequency (RF) and THz finite indoor network in which THz small cells are deployed to provide high data rates, and RF macrocells are deployed to satisfy coverage requirements. Using stochastic geometry tools, we assess the performance of coexisting RF and THz networks and derive tractable analytical expressions for the coverage probability and average achievable rate. The analytical results are validated with Monte-Carlo simulations. Several insights are devised for accurate tuning and optimization of THz system parameters, including the THz bias, and the fraction of THz access points (APs) to deploy. The obtained results recognize a clear coverage/rate trade-off where a high fraction of THz AP improves the rate significantly but may degrade the coverage performance. Furthermore, the location of a user in the finite area highly affects the fraction of THz APs that optimizes its quality of service.
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The meta distribution of the signal-to-interference-plus-noise ratio (SINR) provides fine-grained information about each link’s performance in a wireless system and the reliability of the whole network. While the UAV-enabled netw...
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The meta distribution of the signal-to-interference-plus-noise ratio (SINR) provides fine-grained information about each link’s performance in a wireless system and the reliability of the whole network. While the UAV-enabled network has been studied extensively, most of the works focus on the spatial average performance, such as coverage probability, while SINR meta distribution has received less attention. In this paper, we use the SINR meta distribution to systematically analyze the improvement and the influence of deploying UAVs on the reliability of a wireless network. We first derive the
$b$
-th moments of the conditional success probability of the UAV-enabled network and give the approximated expressions derived by Gil-Pelaez theorem and the beta approximation of the meta distribution. Our numerical results show that deploying UAVs in wireless networks in most cases can greatly improve the system reliability, which denotes the fraction of users achieving cellular coverage, especially for the spatially-clustered users. In addition, establishing LoS links is not always beneficial since it also increases the interference. For instance, with the increase of the SINR threshold, the system reliability of a high LoS probability environment decreases dramatically and it is even lower than a low LoS probability environment. We also show that in highrise urban areas, UAVs can help in establishing extremely reliable (very high SINR) links.
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Given the necessity of connecting the unconnected, covering blind spots has emerged as a critical task in the next-generation wireless communication network. A direct solution involves obtaining a coverage manifold that visually s...
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Given the necessity of connecting the unconnected, covering blind spots has emerged as a critical task in the next-generation wireless communication network. A direct solution involves obtaining a coverage manifold that visually showcases network coverage performance at each position. Our goal is to devise different methods that minimize the absolute error between the estimated coverage manifold and the actual coverage manifold (referred to as accuracy), while simultaneously maximizing the reduction in computational complexity (measured by computational latency). Simulation is a common method for acquiring coverage manifolds. Although accurate, it is computationally expensive, making it challenging to extend to large-scale networks. In this paper, we expedite traditional simulation methods by introducing a statistical model termed line-of-sight probability-based accelerated simulation. Stochastic geometry is suitable for evaluating the performance of large-scale networks, albeit in a coarse-grained manner. Therefore, we propose a second method wherein a model training approach is applied to the stochastic geometry framework to enhance accuracy and reduce complexity. Additionally, we propose a machine learning-based method that ensures both low complexity and high accuracy, albeit with a significant demand for the size and quality of the dataset. Furthermore, we describe the relationships between these three methods, compare their complexity and accuracy as performance verification, and discuss their application scenarios.
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