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Performance of mmWave UAV‐Assisted 5G Hybrid Heterogeneous Networks
Abstract
Motivated by the need for unmanned aerial vehicle (UAV) communications in the modern era of wireless communication, this chapter focuses on a case study of millimeter‐Wave (mmWave) and teraHertz (THz) communication and the technical challenges in applying mmWave and THz frequency band for communication with UAVs. Specifically, this chapter focuses on the placement of UAVs to replace the terrestrial backhaul network with an aerial network. In addition, we address the performance of UAV‐enabled hybrid heterogeneous network (HetNet) by considering stringent communication‐related constraints, e.g. bandwidth, data rate, and signal‐to‐noise ratio (SNR). Also, the association of terrestrial small‐cell base stations (SCBs) with UAVs is addressed such that the sum rate of the overall system is maximized. To this end, an association algorithm is presented, which deals not only with the association of SCBs but also with backhaul link capacity. A detailed analysis of simulation‐based results shows a favorable performance of UAV‐assisted wireless network.
... The previous cases (i.e., propulsion and communication energy) considered mainly 811 deal with the energy consumption of only the UAV-BSs when they are deployed alone or 812 as a swarm network comprising multiple UAV-BSs. The UAV-assisted cellular network is 813 the case where a single/multiple UAV-BSs is/are deployed in existing terrestrial BSs in 814 order to enhance certain network performances such as throughput, coverage, etc. [122,123]. ...
Wireless communication networks have been witnessing unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Although there are many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance capacity due to their easy implementation, pop-up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity where it is needed. However, UAVs mostly have limited energy storage, hence, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed—conventional and machine learning (ML). Such classification helps understand the state-of-the-art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above-mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trends in the literature.
... Within a short span of time, unmanned aerial vehicles (UAVs) communication emerged as a new paradigm shift because of their instant deployment, flexible to change position and high probability of line-of-sight (LoS); thus, improving the coverage and rate performances [1][2][3][4][5][6][7][8]. Seeing the popularity of UAV communications, [9] address the real-time deployment of UAVs and a resource allocation scheme. ...
Unmanned aerial vehicles (UAVs) evolution has shown potential in many applications of wireless communication because of their high coverage, promising rates, and flexible installation. Owing to the drastic increase of this technology, one of the major challenges is the availability of on-board energy levels to UAVs to stay aloft for a prolonged time. Due to their limited on-board energy, many of the UAVs can go down, and as a result, many associated ground users can face coverage issues etc. Considering this problem, in this study, we give an idea of collecting the uplink traffic from these disaster-points (where active UAVs were serving previously) with the objective of minimizing the Age-of-Information (AoI) of the entire network. Specifically, we address the optimum trajectory of a UAV for collecting data so that the timely delivery of information to the destination can be possible, which will ultimately reduce the overall AoI. We use three different kinds of trajectories namely, random trajectory, travelling salesman problem (TSP)-based trajectory, and proposed trajectory. Simulations show that the proposed trajectory outperforms in the scenario, where disaster-points are less. Additionally, unsupervised learning-based UAVs distribution helps in reducing the AoI as compared to Matern type-I hard-core process-based UAVs distribution. Furthermore, the proposed trajectory is computationally less expensive than TSP-based trajectory; thus, it can be viable in many applications, e.g., Internet of Things (IoT). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
... The previous cases (i.e., propulsion and communication energy) considered mainly deal with the energy consumption of only the UAV-BSs when they are deployed alone or as a swarm network comprising multiple UAV-BSs. The UAVassisted cellular network is the case where a single/multiple UAV-BSs is/are deployed in existing terrestrial BSs in order to enhance certain network performances such as throughput, coverage, etc. [121], [122]. However, their deployment could result in an increase in the overall energy consumption of the network if not properly managed. ...
Wireless communication networks have been witnessing an unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Albeit many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance the capacity due to their easy implementation, pop up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity to where it is needed. However, because the UAVs mostly have limited energy storage, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed; conventional and machine learning (ML). Such classification helps understand the state of the art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trend in the literature.
Unmanned aerial vehicles (UAVs) evolution has gained an unabated interest for the use in several applications, such as agriculture, aerial surveillance, goods delivery, disaster recovery, intelligent transportation. The main features of this technology are high coverage, strong line-of-sight (LoS) links, promising throughput, cost-effective and flexible deployment. Currently, the Third Generation Partnership Project (3GPP) is working on the specification of release-17 (R-17) new radio (NR) for non-terrestrial networks (NTN). Therefore, owing to the drastic increase of UAV technology, in this paper, we propose channel state information (CSI) compression and its recovery with the aid of machine learning (ML)-based twin channel predictors. Due to the characteristic of gaining higher LoS communication paths in UAV network, the proposed strategy can bring potential benefits such as over-the-air (OTA)-overhead reduction, minimizing mean-squared-error (MSE) of a channel and maximizing precoding gain. Simulation-based results corroborate the validity of the proposed strategy, which can reap benefits in multiple factors.
The staggering growth of the number of vehicles worldwide has become a critical challenge resulting in tragic incidents, environment pollution, congestion, etc. Therefore, one of the promising approaches is to design a smart vehicular system as it is beneficial to drive safely. Present vehicular system lacks data reliability, security, and easy deployment. Motivated by these issues, this paper addresses a drone-enabled intelligent vehicular system, which is secure, easy to deploy and reliable in quality. Nevertheless, an increase in the number of operating drones in the communication networks makes them more vulnerable towards the cyber-attacks, which can completely sabotage the communication infrastructure. To tackle these problems, we propose a blockchain-based registration and authentication system for the entities such as drones, smart vehicles (SVs) and roadside units (RSUs). This paper is mainly focused on the blockchain-based secure system design and the optimal placement of drones to improve the spectral efficiency of the overall network. In particular, we investigate the association of RSUs with the drones by considering multiple communication-related factors such as available bandwidth, maximum number of links a drone can support, and backhaul limitations. We show that the proposed model can easily be overlaid on the current vehicular network reaping benefits of secure and reliable communications.
In this paper, we investigate the downlink performance of a three-tier heterogeneous network (HetNet). The objective is to enhance the edge capacity of a macro cell by deploying unmanned aerial vehicles (UAVs) as flying base stations and small cells (SCs) for improving the capacity of indoor users in scenarios such as temporary hotspot regions or during disaster situations where the terrestrial network is either insufficient or out of service. UAVs are energy-constrained devices with a limited flight time, therefore, we formulate a two layer optimization scheme, where we first optimize the power consumption of each tier for enhancing the system energy efficiency (EE) under a minimum quality-of-service (QoS) requirement, which is followed by optimizing the average hover time of UAVs. We obtain the solution to these nonlinear constrained optimization problems by first utilizing the Lagrange multipliers method and then implementing a sub-gradient approach for obtaining convergence. The results show that through optimal power allocation, the system EE improves significantly in comparison to when maximum power is allocated to users (ground cellular users or connected vehicles). The hover time optimization results in increased flight time of UAVs thus providing service for longer durations.
The enormous increase in cellular users requires novel advancements in the existing cellular infrastructure. Therefore, small cell
networks (SCNs) are a promising solution to meet the ever-growing
demands of cellular users as they are beneficial in terms of coverage
and providing higher data rates. However, one of the challenging
parts is the deployment of small cell base stations (SBs) and their
connectivity with the backhaul network. In this paper, we use the
scalable idea of replacing the terrestrial backhaul network with
an aerial network to provide fronthaul connectivity to SBs. In particular, we address the optimum placement of unmanned aerial
vehicles (UAVs) to associate the SBs such that the sum-rate of the
overall network is maximized. We achieve such an objective by
proposing a two-layer framework, i.e., unsupervised learning and
iterative algorithm (defined as UAV equalizer), and we call this two-layer framework as a hybrid approach. Simulation results show
that the proposed hybrid approach outperforms the traditional approaches in terms of maximizing the sum-rate, minimum bandwidth
consumption, moreover, maximizing link utilization and energy
efficiency.
Terahertz (THz)-band communications are celebrated as a key enabling technology for next-generation wireless systems that promises to integrate a wide range of data-demanding and delay-sensitive applications. Following recent advancements in optical, electronic, and plasmonic transceiver design, integrated, adaptive, and efficient THz systems are no longer far-fetched. In this article, we present a progressive vision of how the traditional "THz gap" will transform into a "THz rush" over the next few years. We posit that the breakthrough the THz band will introduce will not be solely driven by achievable high data rates, but more profoundly by the interaction between THz sensing, imaging, and localization applications. We first detail the peculiarities of each of these applications at the THz band. Then we illustrate how their coalescence results in enhanced environment-aware system performance in beyond-5G use cases. We further discuss the implementation aspects of this merging of applications in the context of shared and dedicated resource allocation, highlighting the role of machine learning.
Unmanned aerial vehicles (UAVs) provide us with ability for rapid, on demand, and infrastructure-less deployment. This capability can be exploited to meet the rising demands of futuristic fifth generation (5G) and Internet of things (IoT) networks. In this study, we consider a downlink scenario in a multi-tier heterogeneous network (HetNet), with sub-6GHz and millimeter wave (mmWave) UAVs coexisting as aerial base stations (ABSs), to meet the network quality-of-service (QoS) requirements. The considered QoS metrics are network coverage and data rates. The network area has no pre-existing communication infrastructure. We study impact of various network configurations, by varying ratio of mmWave UAVs to total UAVs in the HetNet, number of users, and bias factor for mmWave tier. Our results validate that sub-6GHz and mmWave UAVs can be deployed together to complement each other in a multi-tier HetNet where sub-6GHz tier will enhance coverage and mmWave tier will improve data rates. Through extensive simulations, we propose and implement methodology to configure the HetNet for an efficient coverage-rate trade off, while meeting desired QoS metrics at the same time.
The wide bandwidth available at the millimeter wave (mmWave) frequencies is expected to offer high data rates in the fifth generation (5G) of cellular mobile communication systems. The technology implements directional transmission to overcome increased free-space path loss at high frequencies. The dependence on directionality imposes challenges to establish new control layer protocols because the algorithms implemented in conventional omnidirectional long term evolution (LTE) systems are not suitable for these networks. The mmWave base station (BS) and user equipment (UE) need to be properly aligned for directional communication constituting long-lasting initial access (IA) phase. Recently, several research works have been done to devise smart initial access procedures for mmWave systems. Some of these schemes periodically sweep across the cell area to establish the connection while others resort to making use of the available contextual information regarding BS and UE profiles and propagation environment to establish the link. This paper proposes a smart machine learning-based context-aware sequential algorithm for IA in 5G mmWave systems and analyzes its performance in comparison to the conventional exhaustive and iterative search algorithms. The algorithm is shown to provide a comparatively low probability of misdetection and a smaller discovery delay.
Small cell networks (SCNs) offer a cost-effective
coverage solution to wireless applications demanding high data
rates. However in SCNs, a challenging problem is the proper
management of backhaul links to small cell base stations
(SCBSs). To make a good backhaul link, perfect line-of-sight
(LoS) communication between the SCBSs and the core network
plays a vital role. In this study, we use the idea of employing
unmanned aerial vehicles (UAVs) to provide connectivity between
SCBSs and the core network. We focus on the association
of SCBSs with UAVs by considering multiple communicationrelated factors including data rate limit and available bandwidth
resources of the backhaul. In particular, we address the optimum
placement of UAVs to serve a maximum number of SCBSs
while considering available resources using unsupervised k-means
algorithm. Numerical results show that the proposed approach
outperforms the conventional approach in terms of associated
SCBSs, bandwidth consumption, available link utilization, and
sum-rate maximization.
Over the past years, carrier frequencies used for wireless communications have been increasing to meet bandwidth requirements. The engineering community witnessed the development of wide radio bands such as the millimeter-wave (mmW) frequencies to fulfill the explosive growth of mobile data demand and pave the way towards 5G networks. Other research interests have been steered towards optical wireless communication to allow higher data rates, improve physical security and avoid electromagnetic interference. Nevertheless, a paradigm change in the electromagnetic wireless world has been witnessed with the exploitation of the Terahertz (THz) frequency band (0.1-10 THz). With the dawn of THz technology, which fills the gap between radio and optical frequency ranges, ultimate promise is expected for the next generation of wireless networks. In this paper, the light is shed on a number of opportunities associated with the deployment of the THz wireless links. These opportunities offer a plethora of applications to meet the future communication requirements and satisfy the ever increasing user demand of higher data rates.
The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing in a wide range of wireless networking applications. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. For instance, UAVs can be deployed to complement existing cellular systems by providing additional capacity to hotspot areas as well as to provide network coverage in emergency and public safety situations. On the other hand, UAVs can operate as flying mobile terminals within the cellular networks. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as three-dimensional deployment, performance analysis, air-to-ground channel modeling, and energy efficiency are explored along with representative results. Then, fundamental open problems and potential research directions pertaining to wireless communications and networking with UAVs are introduced. To cope with the open research problems, various analytical frameworks and mathematical tools such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems.
Wireless networks comprising unmanned aerial ve- hicles can offer limited connectivity in a cost-effective manner to disaster-struck regions where terrestrial infrastructure might have been damaged. While these drones offer advantages such as rapid deployment to far-flung areas, their operations may be rendered ineffective by the absence of an adequate energy management strategy. This article considers the multi-faceted applications of these platforms and the challenges thereof, in the networks of the future. In addition to providing an overview of the work done by researchers in determining the features of the air-to- ground channel, the article explores the use of drones in fields as diverse as military surveillance and network-rehabilitation for disaster-struck areas. It also goes on to present a case-study which envisages a scenario in which drones operate alongside conventional wireless infrastructure, thereby allowing a greater number of users to establish a line-of-sight link for communica- tion. This study investigates a power allocation strategy for the microwave base station and the small base stations operating at 28 GHz frequency band. The self-adaptive power control strategy for drones is dependent on the maximum allowable interference threshold and minimum data rate requirements. This study highlights the importance of incorporating the drones in the multi-tier heterogeneous network to extend the network coverage and capacity.
Unmanned Aerial Vehicle mounted base stations (UAV-BSs) can provide wireless services in a variety of scenarios. In this letter, we propose an optimal placement algorithm for UAV-BSs that maximizes the number of covered users using the minimum transmit power. We decouple the UAV-BS deployment problem in the vertical and horizontal dimensions without any loss of optimality. Furthermore, we model the UAV-BS deployment in the horizontal dimension as a circle placement problem and a smallest enclosing circle problem. Simulations are conducted to evaluate the performance of the proposed method for different spatial distributions of the users.
Using drones as flying base stations is a promising approach to enhance the network coverage and area capacity by moving supply towards demand when required. However deployment of such base stations can face some restrictions that need to be considered. One of the limitations in drone base stations (drone-BSs) deployment is the availability of reliable wireless backhaul link. This paper investigates how different types of wireless backhaul offering various data rates would affect the number of served users. Two approaches, namely, network-centric and user-centric, are introduced and the optimal 3D backhaul-aware placement of a drone-BS is found for each approach. To this end, the total number of served users and sum-rates are maximized in the network-centric and user-centric frameworks, respectively. Moreover, as it is preferred to decrease drone-BS movements to save more on battery and increase flight time and to reduce the channel variations, the robustness of the network is examined as how sensitive it is with respect to the users displacements.
In this paper, the efficient deployment of multiple unmanned aerial vehicles (UAVs) with directional antennas acting as wireless base stations that provide coverage for ground users is analyzed. First, the downlink coverage probability for UAVs as a function of the altitude and the antenna gain is derived. Next, using circle packing theory, the three-dimensional locations of the UAVs is determined in a way that the total coverage area is maximized while maximizing the coverage lifetime of the UAVs. Our results show that, in order to mitigate interference, the altitude of the UAVs must be properly adjusted based on the beamwidth of the directional antenna as well as coverage requirements. Furthermore, the minimum number of UAVs needed to guarantee a target coverage probability for a given geographical area is determined. Numerical results evaluate the various tradeoffs involved in various UAV deployment scenarios.
We envision small cells mounted on unmanned aerial vehicles, to complement existing macrocell infrastructure. We demonstrate through numerical analysis that clustering algorithms can be used to position the airborne access points and select users to offload from the macrocells. We compare the performance of these deployments against equivalent simulated picocell deployments. We demonstrate that due to their ability to position themselves around exact user locations while maintaining a direct line-of-sight link the airborne access points provide a significantly improved received signal strength than the static picocell alternatives. We also find that the airborne access points provide superior service quality even in the presence of user and access point positioning errors.
The presence of a high-rate, but also cost-efficient and scalable, backhaul/fronthaul framework is essential in the upcoming fifth-generation (5G) wireless networks & beyond. Motivated by the mounting interest in Unmanned Aerial Vehicles (UAVs) for providing communications services and the recent advances in Free-Space Optics (FSO), this article investigates the feasibility of a novel vertical backhaul/fronthaul framework where the UAVs transport the backhaul/fronthaul traffic between the access and core networks via point-to-point FSO links. The performance of the proposed innovative approach is investigated under different weather conditions and system parameters. Simulation results demonstrate that the FSO-based vertical backhaul/fronthaul framework offers data rates higher than the baseline alternatives, and thus can be considered as a promising solution to the emerging backhaul/fronthaul requirements of the 5G+ wireless networks, particularly in the presence of ultra-dense heterogeneous small cells. We also present the challenges that accompany such a framework and provide some key ideas toward overcoming such challenges.
Aerial platforms have recently gained a significant popularity as key enablers for rapid deployable relief networks where coverage is provided by onboard radio heads. These platforms are capable of delivering essential wireless communication for public safety agencies in remote areas or during the aftermath of natural disasters. In this paper we provide an analytical approach to optimize the altitude of such platforms in order to allow the maximum exploitation of their coverage capabilities over an urban environment. The solution is presented in a simple mathematical form, which is a function of the maximum allowed pathloss and of the statistical parameters of the urban environment as defined by the International Telecommunication Union. Furthermore, we present a closed form formula for predicting the probability of geometrical line of sight between a low altitude platform and a corresponding ground receiver based on the same environment statistical parameters.
Unmanned aerial vehicle (UAV) has been widely used in many fields and is arousing global attention. As the resolution of the equipped sensors in the UAV becomes higher and the tasks become more complicated, much higher data rate and longer communication range are required in the foreseeable future. As the millimeter-wave (mmWave) band can provide more abundant frequency resources than the microwave band, much higher achievable rate can be guaranteed to support UAV services such as video surveillance, hotspot coverage, and emergency communications, etc. The flexible mmWave beamforming can be used to overcome the high path loss caused by the long propagation distance. In this paper, we study three typical application scenarios for mmWave-UAV communications, namely communication terminal, access point, and backbone link. We present several key enabling techniques for UAV communications, including beam tracking, multi-beam forming, joint Tx/Rx beam alignment, and full-duplex relay techniques. We show the coupling relation between mmWave beamforming and UAV positioning for mmWave-UAV communications. Lastly, we summarize the challenges and research directions of mmWave-UAV communications in detail.
Massive Multiple-Input Multiple-Output (MIMO) is considered as a key technology for 4G and 5G wireless communication systems to improve spectrum efficiency by supporting large number of concurrent users. In addition, for the target frequency band of 5G system, mmWave band, massive MIMO is pivotal in compensating the high pathloss. In this paper, we investigate the channel state information (CSI) acquisition problem for mmWave massive MIMO. With hybrid analog-digital antenna architecture, how to derive the analog beamforming and digital beamforming is studied. An iterative analog beam acquisition approach is proposed to save system overhead and reduce beam searching complexity. Regarding the digital beamforming, a grouping based codebook is proposed to facilitate CSI feedback. The codebook is then extended to incorporate also analog beam acquisition. Furthermore, channel reciprocity is exploited to save CSI reporting overhead and a two-stage approach is proposed to fully utilize the channel reciprocity at both mobile station and base station side and accelerate the CSI acquisition procedure.
Millimeter wave (mmWave) wideband channels in a multiple-input multiple-output (MIMO) transmission are described by a sparse set of impulse responses in the angle-delay, or space-time (ST), domain. These characteristics will be even more prominent in the THz band used in future systems. We consider two approaches for channel estimation: compressed-sensing (CS), exploiting the sparsity in the angular/delay domain, and low-rank (LR), exploiting the algebraic structure of channel matrix. Both approaches share several commonalities, and this paper provides for the first time i) a comparison of the two approaches, and ii) new versions of CS and LR methods that significantly improve performance in terms of mean squared error (MSE), computational complexity, and latency. We derive the asymptotic MSE bound for any estimator of the ST-MIMO multipath channels with invariant angles/delays and time-varying fading, with unknown angle/delay diversity order: the bound also accounts for the degradation introduced by sub-optimal separable channel models. We will show that in the considered scenarios both CS and LR approaches attain the bound. Our performance assessment over ideal and 3
rd
generation partnership project (3GPP) channel models, suitable for the fifth-generation (5G) and beyond of cellular networks, shows the trade-off obtained by the methods over various metrics: i) CS methods are converging faster than the LR methods, both attaining the asymptotic MSE bound; ii) the CS methods depend on the array manifold, while LR methods are independent of the array calibration; iii) CS solutions are more complex than LR solutions.
Terahertz communications offers a massive potential for the prospective beyond 5G wireless systems, as the band offers huge bandwidth and data rates as compared to the existing sub 6 GHz bands, which are almost saturated. In this paper, we investigate the feasibility of wireless communications over the Terahertz-band (0.75–10 THz) at various atmospheric altitudes, considering different transmission distances and directions by realistically calculating the absorption loss, which is the major limiting factor affecting the propagation of THz waves through the earth’s atmosphere. Four practical altitudes are considered, corresponding to Drone-to-Drone (D2D), Jet plane-to-Jet plane (J2J), Unmanned Aerial Vehicle (UAV)-to-UAV, and near-space Satellite-to-Satellite (S2S) communications. Following comparison and validation with two real-world experimental results from the literature measured at the sea-level, Line by Line Radiative Transfer Models (LBLRTM) is used to obtain realistic THz-band transmittance values against each altitude case and setting. Subsequently, absorption losses and total path loss values are computed and mean total path loss sensitivity is further observed against a range of transmission directions via zenith angle variations from vertically-up to vertically-down communication. Numerical results show that as the altitude increases, the concentration of the water vapor molecules decreases, enabling the communication over the THz-band (0.75–10 THz) to be more feasible as compared to the sea-level communication. Moreover, the total usable bandwidth results over the THz-band (0.75–10 THz) exhibit that the upper bounds of 8.218 THz, 9.142 THz and 9.25 THz are usable up to the transmission distance of 2 km against the total antenna gains of 80 dBi for J2J, U2U and S2S communication cases, respectively.
Heterogeneous networks (HetNets) comprising of various communication technologies with wireless backhaul capabilities are becoming essential ingredients to satisfy the high data rate requirements set for future wireless systems. The use of unmanned aerial vehicles (UAVs) and the addition of teraHertz (THz)-based communication is considered a key pillar in providing such capabilities. UAVs are used to provide infrastructure-less, rapid and on-demand deployment, while THz technology is a new wireless frontier and a potential enabler for providing immense data rates. Wireless backhaul capabilities are necessary to avoid cumbersome wired layouts. In this paper, we consider a multi-tier HetNet comprising of UAVs operating at sub-6GHz, small base stations (BSs) operating at THz frequencies and a macro-BS (MBS) operating at sub-6GHz. Wireless backhauling is provided by the MBS, some UAVs, and by a few THz cells. Network coverage and data rates are taken as quality of service (QoS) performance metrics. We analyze the effectiveness of the HetNet configuration on network coverage and data rates by varying different parameters. Extensive simulation has been performed, which validates the significance of deploying such a HetNet with wireless backhaul capabilities and to provide elevated data rates to the end users.
The integration of unmanned aerial vehicles (UAVs) and millimeter wave (mmWave) wireless systems has been recently proposed to provide high data rate aerial links for next generation wireless networks. However, establishing UAVbased mmWave links is quite challenging due to the random fluctuations of hovering UAVs which can induce antenna gain mismatch between transmitter and receiver. To assess the benefit of UAV-based mmWave links, in this paper, tractable, closedform statistical channel models are derived for three UAV communication scenarios: (i) a direct UAV-to-UAV link, (ii) an aerial relay link in which source, relay, and destination are hovering UAVs, and (iii) a relay link in which a hovering UAV connects a ground source to a ground destination. The accuracy of the derived analytical expressions is corroborated by performing Monte-Carlo simulations. Numerical results are then used to study the effect of antenna directivity gain under different channel conditions for establishing reliable UAV-based mmWave links in terms of achieving minimum outage probability. It is shown that the performance of such links is largely dependent on the random fluctuations of hovering UAVs. Moreover, higher antenna directivity gains achieve better performance at low SNR regime. Nevertheless, at the high SNR regime, lower antenna directivity gains result in a more reliable communication link. The developed results can therefore be applied as a benchmark for finding the optimal antenna directivity gain of UAVs under the different levels of instability without resorting to time-consuming simulations.
To satisfy the stringent capacity and scalability requirements in the fifth generation (5G) mobile networks, both wireless access and backhaul links are envisioned to exploit millimeter wave (mmWave) spectrum. Here, similar to the design of access links, mmWave backhaul connections must also address many challenges such as multipath propagation and dynamic link blockage, which calls for advanced solutions to improve their reliability. To address these challenges, 3GPP New Radio (NR) technology is considering a flexible and reconfigurable backhaul architecture, which includes dynamic link rerouting to alternative paths. In this paper, we investigate the use of aerial relay nodes carried by e.g., unmanned aerial vehicles (UAVs) to allow for such dynamic routing, while mitigating the impact of occlusions on the terrestrial links. This novel concept requires an understanding of mmWave backhaul dynamics that accounts for: (i) realistic 3D multipath mmWave propagation; (ii) dynamic blockage of mmWave backhaul links; and (iii) heterogeneous mobility of blockers and UAV-based assisting relays. We contribute the required mathematical framework that captures these phenomena to analyze the mmWave backhaul operation in characteristic urban environments. We also utilize this framework for a new assessment of mmWave backhaul performance by studying its spatial and temporal characteristics. We finally quantify the benefits of utilizing UAV assistance for more reliable mmWave backhaul. The numerical results are confirmed with 3GPP-calibrated simulations, while the framework itself can aid in the design of robust UAV-assisted backhaul infrastructures in future 5G mmWave cellular.
We consider a drone-based communication network, where several drones hover above an area and serve as mobile remote radio heads for a large number of mobile users. We assume that the drones employ free space optical (FSO) links for fronthauling of the users' data to a central unit. The main focus of this paper is to quantify the geometric loss of the FSO channel arising from random fluctuation of the position and orientation of the drones. In particular, we derive upper and lower bounds, corresponding approximate expressions, and a closed-form statistical model for the geometric loss. Simulation results validate our derivations and quantify the FSO channel quality as a function of the drone's instability, i.e., the variation of its position and orientation.
Agility and resilience requirements of future cellular networks may not be fully satisfied by terrestrial base stations in cases of unexpected or temporary events. A promising solution is assisting the cellular network via low-altitude unmanned aerial vehicles equipped with base stations, i.e., drone-cells. Although drone-cells provide a quick deployment opportunity as aerial base stations, efficient placement becomes one of the key issues. In addition to mobility of the drone-cells in the vertical dimension as well as the horizontal dimension, the differences between the air-to-ground and terrestrial channels cause the placement of the drone-cells to diverge from placement of terrestrial base stations. In this paper, we first highlight the properties of the dronecell placement problem, and formulate it as a 3-D placement problem with the objective of maximizing the revenue of the network. After some mathematical manipulations, we formulate an equivalent quadratically-constrained mixed integer non-linear optimization problem and propose a computationally efficient numerical solution for this problem. We verify our analytical derivations with numerical simulations and enrich them with discussions which could serve as guidelines for researchers, mobile network operators, and policy makers.
Tracking moving ground targets using unmanned air vehicles(UAVs) has important applications in several areas. Keeping a close line of sight from a UAV to a target in a densely populated area is a challenging task because of many constraints. An algorithm for several UAVs to track a moving target cooperatively is proposed. From random samples on the ground and obstacles, a cost inversely proportional to chance to keep the target inside the camera field of view is defined. The centre of the flight path and the separation angles between UAVs along the circular flight path is optimally determined to minimise the cost. The efficiency of the algorithm is tested by Monte-Carlo simulations based on random scenario generators. Copyright © 2007 International Federation of Automatic Control All Rights Reserved.
Tracking moving ground targets using unmanned air vehicles(UAVs) has important applications in several areas. Keeping a close line of sight from a UAV to a target in a densely populated area is a challenging task because of many constraints. An algorithm for several UAVs to track a moving target cooperatively is proposed. From random samples on the ground and obstacles, a cost inversely proportional to chance to keep the target inside the camera field of view is defined. The centre of the flight path and the separation angles between UAVs along the circular flight path is optimally determined to minimise the cost. The efficiency of the algorithm is tested by Monte-Carlo simulations based on random scenario generators.
We present the global k-means algorithm which is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N (with N being the size of the data set) executions of the k-means algorithm from suitable initial positions. We also propose modifications of the method to reduce the computational load without significantly affecting solution quality. The proposed clustering methods are tested on well-known data sets and they compare favorably to the k-means algorithm with random restarts.
For critical civil and military missions, the Unmanned Aerial Vehicles (UAVs) are continuously attracting attention. UAVs should collect data for a defined area with a variety of sensors. When these UAVs are operating in swarm formation, the observation area could be increased and it would also be possible to deal with a loss of a UAV. The big amount of data produced by the UAVs requires high date rate connectivity, therefore the free space optical (FSO) communication is suitable. When using FSO links some important issues need be considered, the beam attenuation due the atmosphere on one side and the alignment of the FSO units on the other side. The alignment raises technical challenges in tracking and acquisition. The combination of UAVs with the FSO communication is an interesting investigative scenario and some of the important aspects of this ambitious design are the main focus of this paper.
Wireless multimedia applications require significant bandwidth,
some of which will be provided by third-generation (3G) services. Even
with substantial investment in 3G infrastructure, the radio spectrum
allocated to 3G will be limited. Cognitive radio offers a mechanism for
the flexible pooling of radio spectrum using a new class of protocols
called formal radio etiquettes. This approach could expand the bandwidth
available for conventional uses (e.g. police, fire and rescue) and
extend the spatial coverage of 3G in a novel way. Cognitive radio is a
particular extension of software radio that employs model-based
reasoning about users, multimedia content, and communications context.
This paper characterizes the potential contributions of cognitive radio
to spectrum pooling and outlines an initial framework for formal
radio-etiquette protocols
A survey on terahertz communications
- Z. Chen
- X. Ma
- B. Zhang
5G Bytes: Small Cells Explained
- A. Nordrum
- K. Clark
High-speed optical links for UAV applications.Free-Space Laser Communication and Atmospheric Propagation XXIX
- Chen C A Grier
- M Malfa
A survey on terahertz communications
- Chen Z.
Association of Small Cell Base Stations with UAVs in HetNets By Muhammad …
- M K Shehzad