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Challenges of using UAVs to enhance the quality of 5G network-survey
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At present, there is a growing demand for drones with diverse capabilities that can be used in both civilian and military applications, and this topic is receiving increasing attention. When it comes to drone operations, the amount of energy they consume is a determining factor in their ability to achieve their full potential. According to this, it appears that it is necessary to identify the factors affecting the energy consumption of the unmanned air vehicle (UAV) during the mission process, as well as examine the general factors that influence the consumption of energy. This chapter aims to provide an overview of the current state of research in the area of UAV energy consumption and provide general categorizations of factors affecting UAV’s energy consumption as well as an investigation of different energy models.
Unmanned aerial vehicles (UAVs), commonly known as drones, are being increasingly deployed throughout the globe as a means to streamline monitoring, inspection, mapping, and logistic routines. When dispatched on autonomous missions, drones require an intelligent decision-making system for trajectory planning and tour optimization. Given the limited capacity of their onboard batteries, a key design challenge is to ensure the underlying algorithms can efficiently optimize the mission objectives along with recharging operations during long-haul flights. With this in view, the present work undertakes a comprehensive study on automated tour management systems for an energy-constrained drone: (1) We construct a machine learning model that estimates the energy expenditure of typical multi-rotor drones while accounting for real-world aspects and extrinsic meteorological factors. (2) Leveraging this model, the joint program of flight mission planning and recharging optimization is formulated as a multi-criteria Asymmetric Traveling Salesman Problem (ATSP), wherein a drone seeks for the time-optimal energy-feasible tour that visits all the target sites and refuels whenever necessary. (3) We devise an efficient approximation algorithm with provable worst-case performance guarantees and implement it in a drone management system, which supports real-time flight path tracking and re-computation in dynamic environments. (4) The effectiveness and practicality of the proposed approach are validated through extensive numerical simulations as well as real-world experiments.
The massive deployment of small-sized cells for the Fifth Generation (5G) mobile network will increase the Handover Probability (HOP), potentially causing higher Handover Ping-Pong Probability (HPPP) and/or Radio Link Failure (RLF). Inappropriate usage of Handover Control Parameter (HCP) settings may further exasperate this issue. Therefore, Mobility Robustness Optimisation (MRO) has been introduced and further developed as a significant Self-Optimisation Network (SON) function in the 5G network and beyond. The main aim of MRO is to address Mobility Management (MM) issues during user mobility between cells to ensure a smooth connection. Although various algorithms were suggested in the literature, they mostly cater to 4G networks which may not be effective for the 5G network due to different network characterisations. This paper analyses the performance of various MRO algorithms with various system settings and scenarios for the 5G network. The investigated algorithms from the literature include the Distance (Dis), Cost Function (CF), Fuzzy Logic Controller (FLC) and Handover Performance Indicator (HPI). Validation has been accomplished for different mobility conditions in the 5G network. A simulation based on the MATLAB software has been conducted using various system tools. The evaluation analysis is in terms of Signal to-Interference-plus-Noise-Ratio (SINR), HPPP and RLF effects since these are major indicators in assessing system performance and selecting the handover decision during user mobility. The simulation outcomes show that the HPI algorithm performance is more reactive to mobile speed scenarios over time, significantly reducing the HPPP compared to the other algorithms which do not provide large reactions in the same conditions. Simultaneously, the HPI algorithm exhibits the highest RLF and SINR from among the other algorithms. The distance algorithm is the best in terms of RLF and SINR, achieving an acceptable level in terms of HPPP. These results point to that the MRO algorithm that operate based on distance is the most robust compared to the other investigated algorithms, confirming the potential of the Dis approach for the 5G network.
Massive MIMO will improve the performance of future 5G systems in terms of data rate and spectral efficiency, while accommodating a large number of users. Furthermore, it allows for 3D beamforming in order to provide more degrees of freedom and increase the number of high-throughput users. Massive MIMO is expected to provide more advantages compared to other solutions in terms of energy and spectral efficiency. This will be achieved by focusing the radiation towards the direction of the intended users, thus implementing simultaneous transmission to many users while keeping interference low. It can boost the capacity compared to a conventional antenna solution, resulting in a spectral efficiency up to 50 times greater than that provided by actual 4G technology. However, to take full advantage of this technology and to overcome the challenges of implementation in a real environment, a complicated radio system is required. The purpose of this work is to present the MIMO technology evolution and challenges in a simple introductory way and investigate potential system enhancements.
Internet of Drones (IoD) is a decentralized network and management framework that links drones’ access to the controlled airspace and provides inter-location navigation services. The interconnection of drones in the IoD network is through the Internet of Things (IoT). Hence the IoD network is vulnerable to all the security and privacy threats that affect IoT networks. It is highly required to safeguard a good atmosphere free from security and privacy threats to get the desired performance from IoD applications. Security and privacy issues have significantly restricted the overall influence of the IoD paradigm. There are existing survey studies that helped lay a vital foundation for understanding the IoD security and privacy issues. However, not all have thoroughly investigated the level of security and privacy threats associated with the various drone categories. Besides, most existing review studies do not examine secured IoD architecture. This paper aims to assess the recent trends in the security and privacy issues that affect the IoD network. We investigate the level of security and privacy threats of the various drone categories. We then highlight the need for secured IoD architecture and propose one. We also give a comprehensive taxonomy of the attacks on the IoD network. Moreover, we review the recent IoD attack mitigating techniques. We also provide the performance evaluation methods and the performance metrics employed by the techniques. Finally, we give research future direction to help researchers identify the latest opportunities in IoD research.
The advancement of Unmanned Aerial Vehicle (UAV) technology in terms of industrial processes and communication and networking technologies has led to an increase in their use in civil, business, and social applications. Global rules in most countries had previously limited the use of drones to military applications due to their deployment in the open air, drones are likely to be lost, destroyed, or physically hijacked. However, more recently, the presence of COVID-19 has forced the world to present new implementing measures which will also widen the use of drones in civil and commercial and social applications, especially now in the delivery of medicines for medical home care. In the period of required public isolation as a consequence of the SARS-COV-2 pandemic, this knowledge has become one of the principal partners in the fight against the coronavirus. This paper offers a summary of the medical drone manufacturing, with a specific emphasis on its approval by the pharmaceutical sector to solve logistical problems in healthcare during times of sensitive need. We also discuss the numerous challenges to be met in the integration of drones to save our lives and suggest future research directions. The question that arises for this problem, how to optimize delivery medical supplies times in-home health care made up of drones? We conducted a synthesis literature review devoted to the use of UAVs in healthcare with their different aspects. A total of different research made are given to describe the role of UAV in Home healthcare with the presence of SARS-COV-2. We conclude that the drones will be able to optimize the way of eliminating contamination with a very high percentage (through the reduction of human contact) with the increase of the flexibility of the flight (reaching the less accessible regions every hour of the day).
The market of small drones has been recently increasing due to their use in many fields of application. The most popular drones are multirotors, in particular quadcopters. They are usually supplied with batteries of limited capacity, and for this reason their total flight time is also limited.As a consequence of the non linear characteristics of batteries, estimation of the real flight time may become an issue, since most battery models do not include all the non idealities. Consequently, applications such as delivery service, search and rescue, surveillance might not be accomplished correctly because of inaccurate energy estimations.This paper describes a battery-aware model for an accurate analysis of the drone energy consumption; this model is then applied to a scenario of drone delivery. Results show an accuracy greater of about 16% with respect to the traditional estimation model.
We investigate multi-user (MU) multiple-input multiple-output (MIMO) scheduling under the practical constraints in long-term evolution advanced (LTE-A) downlink cellular networks. The authors first derive the received signal model in MUMIMO systems when there exist both inter-stream interference (ISI) and inter-user interference (IUI) at each user device. Based on this, they formulate the optimisation problem as joint user pairing, precoding matrix indicator (PMI) selection, and resource block (RB) allocation to maximise the total system throughput. The authors then develop a codebook grouping technique for user pairing and PMI selection in MU-MIMO. With the help of codebook grouping, the authors propose some suboptimal and
low-complexity scheduling algorithms to improve system throughput. From system-level simulation, the proposed algorithms can improve the network throughput significantly when exploiting only limited feedback designed for single-user (SU) MIMO in the LTE-A specification.
Unmanned Aerial Vehicles (UAVs) are envisioned as flexible and fast-deploying communication network for disaster scenarios, where the typical communication infrastructure is likely to be malfunctioning. A few works propose UAVs for building communication links autonomously between rescue team’s members in disaster scenarios. The techniques used are usually based on navigation, positioning, and signal strength processing. However, these techniques may not be enough if the objective is to provide communication services to the maximum number of victims and rescuers and not only to a few rescuers. In this situation, dissimilarity metrics, like the Jaccard distance, can provide information about whether the communication service provided to victims is efficient or not (e.g., providing a better distribution of the victims assigned to each UAV acting as service provider). We propose an intelligent strategy that allows UAVs to perform tactical movements in a disaster scenario, combining the Jaccard distance and artificial intelligence algorithms like hill climbing and simulated annealing. Our strategy maximizes the number of victims that are serviced by the UAVs while avoiding network disconnections. Also, a mobility model specifically developed for modelling the victims’ movements within the incident site of a disaster scenario is proposed.
The widespread availability and demand for multimedia capable devices and multimedia content have fueled the need for high-speed wireless connectivity beyond the capabilities of existing commercial standards. While fiber optic data transfer links can provide multigigabit- per-second data rates, cost and deployment are often prohibitive in many applications. Wireless links, on the contrary, can provide a cost-effective fiber alternative to interconnect the outlining areas beyond the reach of the fiber rollout. With this in mind, the ever increasing demand for multi-gigabit wireless applications, fiber segment replacement mobile backhauling and aggregation, and covering the last mile have posed enormous challenges for next generation wireless technologies. In particular, the unbalanced temporal and geographical variations of spectrum usage along with the rapid proliferation of bandwidth- hungry mobile applications, such as video streaming with high definition television (HDTV) and ultra-high definition video (UHDV), have inspired millimeter-wave (mmWave) communications as a promising technology to alleviate the pressure of scarce spectrum resources for fifth generation (5G) mobile broadband.
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.
The reliable prediction of coverage footprint resulting from an airborne wireless radio base station, is at utmost
importance, when it comes to the new emerging applications of air-to-ground wireless services. These applications include the rapid recovery of damaged terrestrial wireless infrastructure due to a natural disaster, as well as the fulfillment of sudden wireless traffic overload in certain spots due to massive movement of crowds. In this paper, we propose a statistical propagation model for predicting the air-to-ground path loss between a low altitude platform and a terrestrial terminal. The prediction is based on the urban environment properties, and is dependent on the elevation angle between the terminal and the platform. The model shows that air-to-ground path loss is following two main propagation groups, characterized by two different path loss profiles. In this paper we are illustrating the methodology of which the model was deduced, as well as we are providing the different path loss profiles including the occurrence probability of each.
Due to the flexibility and agility, unmanned aerial vehicle (UAV) is a promising way for gathering data generated by wireless sensor networks. However, the limited battery capacity of the UAV restricts its application on many occasions, e.g., the networks deployed in the wild. In this paper, we propose a cooperative trajectory planning scheme to deal with the energy issue of the UAV, where a truck carrying backup batteries moves along with the UAV acting as a “mobile recharging station”. Our optimization task is to minimize the total mission time for gathering data from all the sensor nodes, which can be achieved by solving two problems: First, we need to divide the entire mission area into multiple subregions so that the UAV can hover over each subregion to collect the data of the sensor nodes through just one taking-off and landing under the constraint of battery capacity; second, we should find out the optimal trajectory of the truck so that the UAV can get to the hovering positions of each subregion from the truck and fly back to it before the battery drains considering the road condition in real world. We introduce an efficient clustering algorithm to partition the area into subregions in a load-balanced way to minimize the number of movements of the UAV. The trajectory planning task is formulated as a coordinated traveling salesman problem, which is solved by a three-step trajectory planning algorithm heuristically, and we also give the analysis of the upper bound and lower bound to demonstrate the performance guarantee. Numerical results show that our proposed scheme provides an effective and cost-efficient way for the data collection of large-scale wireless sensor networks in practical scenarios.
Energy consumption is a critical constraint for drone delivery operations to achieve their full potential of providing fast delivery, reducing cost, and cutting emissions. This paper provides a uniform framework to facilitate understanding different drone energy consumption models and the inter-relationships between key factors and performance measures to facilitate decision making for drone delivery operations. We review, classify and assess drone energy consumption models. We then document the very wide variations in the modeled energy consumption rates resulting from differences in: (1) the scopes and features of the models; (2) the specific designs of the drones; and (3) the details of their assumed operations and uses. The results show that great care must be taken in adopting a particular drone energy consumption model and that more research is needed, especially empirical research, to ensure the selected model accurately reflects delivery drone designs and uses.
Drones, which are also known as Unmanned Aerial Vehicles (UAVs), are very useful in delivering the packages, and real-time object detection and tracking with minimal human interference. However, there may be several security threats in such an environment, for instance, a malicious user can spy unauthorized drones, transfer malicious packages, or even damage the network reliability that can have direct impact on drones control. This may lead to a potential threat for people, governments, and business sectors. To deal with these issues, in this paper, we propose a novel access control scheme for unauthorized UAV detection and mitigation in an Internet of Drones (IoD) environment, called ACSUD-IoD. With the help of the blockchain-based solution incorporated in ACSUD-IoD, the transactional data having both the normal secure data from a drone (UAV) to the Ground Station Server (GSS) and the abnormal (suspected) data for detection of unauthorized UAVs by the GSS are stored in private blockchain, that are authentic and genuine. As a result, the Big data analytics can be performed on the authenticated transactional data stored in the blockchain. Through the detailed security analysis including formal security under the broadly-accepted Real-Or-Random (ROR) model, formal security verification using the widely-applied Automated Validation of Internet Security Protocols and Applications (AVISPA) tool and non-mathematical security analysis show the robustness of the proposed scheme against a number of potential attacks needed in an IoD environment. The testbed experiments for various cryptographic primitives using the broadly-accepted Multiprecision Integer and Rational Arithmetic Cryptographic Library (MIRACL) have been performed under both server and Raspberry PI 3 configurations. Furthermore, a detailed comparative analysis among the proposed scheme and other existing competing schemes shows the efficacy and more robustness as compared to the existing schemes. Finally, the blockchain-based practical demonstration shows the effectiveness of the proposed scheme.
As Multi-UAV systems are still in early design stages, threat modeling can play a crucial role in attaining the “secure by design” notion in such complex cyber-physical systems (CPS). In this paper, we employ a threat modeling methodology, known as threat trees to analyze and enumerate threats impacting the Internet of Drones (IoD) architecture. The proposed threat tree is meant to provide a holistic perspective of threats impacting an IoD system.
Drone delivery is known as a potential contributor in improving efficiency and alleviating last-mile delivery problems. For this reason, drone routing and scheduling has become a highly active area of research in recent years. Unlike the vehicle routing problem, however, designing drones’ routes is challenging due to multiple operational characteristics including multi-trip operations, recharge planning, and energy consumption calculation. To fill some important gaps in the literature, this paper solves a multi-trip drone routing problem, where drones’ energy consumption is modeled as a nonlinear function of payload and travel distance. We propose adding logical cuts and subgradient cuts in the solution process to tackle the more complex nonlinear (convex) energy function, instead of using the linear approximation method as in the literature, which can fail to detect infeasible routes due to excess energy consumption. We use a 2-index formulation to model the problem and develop a branch-and-cut algorithm for the formulation. Benchmark instances are first generated for this problem. Numerical tests indicate that even though the original model is nonlinear, the proposed approach can solve large problems to optimality. In addition, in multiple instances, the linear approximation model yields routes that under the nonlinear energy model would be energy infeasible. Use of a linear approximation for drone energy leads to differences in energy consumption of about 9% on average compared to the nonlinear energy model.
In recent years, the Internet of Drones (IoD) has emerged as an important research topic in the academy and industry because it has several potential applications ranging from the civilian to military. In IoD environment, several drones, called Unmanned Aerial Vehicles (UAVs), are deployed in different flying zones that communicate each other to exchange crucial information, and then the information are collected by the Ground Station Server (GSS). All the drones and the GSS are registered with a central trusted authority, Control Room (CR) prior to their deployment. Since the drones and the GSS communicate over open channel (e.g., wireless medium), there are security and privacy issues in the IoD environment. To handle such issues, in this paper we introduce a blockchain-based access control scheme in the IoD environment that allows secure communication among the drones, and also among the drones and the GSS. Secure data gathered by the GSS form transactions, and those transactions are made into the blocks. The blocks are finally added in the blockchain by the cloud servers connected with the GSS via the Ripple Protocol Consensus Algorithm (RPCA) in a peer-to-peer cloud server network. Once the blocks are added into the blockchain, the transactions containing in the blocks cannot be altered, modified or even removed. We provide all sorts of security analysis including formal security under the random oracle model, informal security and simulation-based formal security verification to assure that the proposed scheme can resist various potential attacks with high probability needed in an IoD environment. In addition, a meticulous comparative analysis among the proposed scheme and other closely related existing schemes shows that our scheme offers more functionality attributes and better security, and also low communication and computation costs as compared to other schemes.
Millimeter-wave (mmWave) communication is one of the most promising technologies in the fifth generation (5G) mobile networks due to its capability to access a large amount of available spectrum resources. Despite theoretical potential of a high data rate, there are still several key technical challenges to use mmWave in mobile networks, such as severe path loss, high penetration loss, and narrow beamwidth. Hence, accurate and reliable knowledge on mmWave channel propagation characteristics is essential for developing 5G wireless communication systems. In this article, the fundamental characteristics of mmWave are presented first. Then two main channel modeling methods are discussed. In order to investigate the channel characteristics at mmWave bands, measurements using three different large-scale array topologies are carried out, and typical channel parameters are extracted and analyzed.
The topic of unmanned aerial vehicle (UAV) routing is transitioning from an emerging topic to a growing research area with UAVs being used for inspection or even material transport as part of multi-modal networks. The nature of the problem has revealed a need to identify the factors affecting the energy consumption of UAVs during execution of missions and examine the general characteristics of the consumption, as these are critical constraining factors in UAV routing. This paper presents the unique characteristics that influence the energy consumption of UAV routing and the current state of research on the topic. This paper provides the first overview of the current state of and contributions to the area of energy consumption in UAVs followed by a general categorization of the factors affecting energy consumptions of UAVs.
The fast and cost-efficient home delivery of goods ordered online is logistically challenging. Many companies are looking for new ways to cross the last mile to their customers. One technology-enabled opportunity that recently has received much attention is the use of drones to support deliveries. An innovative last-mile delivery concept in which a truck collaborates with a drone to make deliveries gives rise to a new variant of the traveling salesman problem (TSP) that we call the TSP with drone. In this paper, we model this problem as an integer program and develop several fast route-first, cluster-second heuristics based on local search and dynamic programming. We prove worst-case approximation ratios for the heuristics and test their performance by comparing the solutions to the optimal solutions for small instances. In addition, we apply our heuristics to several artificial instances with different characteristics and sizes. Our experiments show that substantial savings are possible with this concept compared to truck-only delivery.
The online appendix is available at https://doi.org/10.1287/trsc.2017.0791 .
The rapid emergence of 5G communications technology and standardization has seen an accelerated transfer of theoretical concepts to advanced development and implementation. Not only are 5G baseband signal processing algorithms becoming more important, but the co-design and implementation of corresponding circuits, architectures, and platforms are becoming necessary due to rapid standardization of 5G communications. This timely overview paper introduces circuits and systems (CAS) for key enabling technologies for the new 5G era: massive MIMO, mmWave baseband systems, NOMA schemes, advanced channel coding, and so on. State-of-the-art research progress in these areas is summarized for interested readers to initiate discussion on limitations of existing solutions and open research problems that are looking for innovative solutions, especially in the CAS area. We hope this paper can bridge the gap between the theoretical investigation and application implementation for 5G communications.
Wireless communication systems that include unmanned aerial vehicles (UAVs) promise to provide cost-effective wireless connectivity for devices without infrastructure coverage. Compared to terrestrial communications or those based on high-altitude platforms (HAPs), on-demand wireless systems with low-altitude UAVs are in general faster to deploy, more flexibly re-configured, and are likely to have better communication channels due to the presence of short-range line-of-sight (LoS) links. However, the utilization of highly mobile and energy-constrained UAVs for wireless communications also introduces many new challenges. In this article, we provide an overview of UAV-aided wireless communications, by introducing the basic networking architecture and main channel characteristics, highlighting the key design considerations as well as the new opportunities to be exploited.
In the September 2014 issue of IEEE Communications Magazine, the first part of this Feature Topic included five articles that covered the fundamentals of mmWave communications with topics ranging from propagation to coverage, presenting a holistic view of research challenges and opportunities in the emerging area of mmWave radio systems and 5G mobile broadband. The use of this technology is expected to surge in the next few years and to transform the Internet industry in the next 10 years. This part of the Feature Topic will address in more detail many technical and application issues related to beamforming, device-to-device communications, heterogeneous networks, and multimedia transmission.
With the severe spectrum shortage in conventional cellular bands, large-scale antenna systems in the mmWave bands can potentially help to meet the anticipated demands of mobile traffic in the 5G era. There are many challenging issues, however, regarding the implementation of digital beamforming in large-scale antenna systems: complexity, energy consumption, and cost. In a practical large-scale antenna deployment, hybrid analog and digital beamforming structures can be important alternative choices. In this article, optimal designs of hybrid beamforming structures are investigated, with the focus on an N (the number of transceivers) by M (the number of active antennas per transceiver) hybrid beamforming structure. Optimal analog and digital beamforming designs in a multi-user beamforming scenario are discussed. Also, the energy efficiency and spectrum efficiency of the N × M beamforming structure are analyzed, including their relationship at the green point (i.e., the point with the highest energy efficiency) on the energy efficiency-spectrum efficiency curve, the impact of N on the energy efficiency performance at a given spectrum efficiency value, and the impact of N on the green point energy efficiency. These results can be conveniently utilized to guide practical LSAS design for optimal energy/ spectrum efficiency trade-off. Finally, a reference signal design for the hybrid beamform structure is presented, which achieves better channel estimation performance than the method solely based on analog beamforming. It is expected that large-scale antenna systems with hybrid beamforming structures in the mmWave band can play an important role in 5G.
In a conventional cellular system, devices are not allowed to directly communicate with each other in the licensed cellular bandwidth and all communications take place through the base stations. In this article, we envision a two-tier cellular network that involves a macrocell tier (i.e., BS-to-device communications) and a device tier (i.e., device-to-device communications). Device terminal relaying makes it possible for devices in a network to function as transmission relays for each other and realize a massive ad hoc mesh network. This is obviously a dramatic departure from the conventional cellular architecture and brings unique technical challenges. In such a two-tier cellular system, since the user data is routed through other users?? devices, security must be maintained for privacy. To ensure minimal impact on the performance of existing macrocell BSs, the two-tier network needs to be designed with smart interference management strategies and appropriate resource allocation schemes. Furthermore, novel pricing models should be designed to tempt devices to participate in this type of communication. Our article provides an overview of these major challenges in two-tier networks and proposes some pricing schemes for different types of device relaying.
Road freight transportation is a major contributor to carbon dioxide equivalent emissions. Reducing these emissions in transportation route planning requires an understanding of vehicle emission models and their inclusion into the existing optimization methods. This paper provides a review of recent research on green road freight transportation.
This paper considers the problem of employing multiple unmanned aerial vehicles (UAVs) to the mobile ad hoc network (MANET) as relay backbone nodes to construct the backbone network, to improve the network connectivity, and to address many issues in the MANET such as linkage, capacity, load balance, and reliability. With considering the dynamic nature of the problem, this study provides several linear location problem models and their extensions to accommodate these issues. Due to the size of linear location models associated with a large number of constraints, the problem becomes computational challenging even with modest size of nodes. To overcome the computational barrier, we recast these location problem models using a quadratic unconstrained binary optimization (QUBO) framework and solve these QUBO models with a Tabu search heuristic with preprocessing. The analysis of the solutions that are produced by QUBO together with the comparisons made with the linear model highlight both the attractiveness and robustness of the proposed approach. The results of this study provide support to future advanced routing protocol development.
The likelihood of line-of-sight (LoS) is an essential component in any radio channel model. It is particularly useful for radio network planning and urban coverage prediction. Empirical LoS models are hard to derive due to a strong dependency on local topology and the need for large measurement datasets. Since buildings are the major obstructions in a dense urban environment, we propose a new theoretical model to determine the LoS probability for air-to- ground channels based on local building geometry and knife-edge diffraction theory. The model takes into account key statistical parameters such as building height, building size, building coverage, street width and street angle distribution. The theoretical model is shown to agree well with ray tracing simulation results. The statistical parameters, such as mean building height, percentage of area covered by buildings (building coverage) and building density, can all be easily obtained for a specific location. We also derive equations for the likelihood of LoS for a direct slant path. These equations can be used in the analysis of air-to-ground channels.
This paper provides new statistical models for air-to-ground channels in an urban environment. The model is derived to operate at frequencies from 200 MHz to 5 GHz. Issues such as path loss and shadowing are evaluated as a function of the elevation angle to the airborne platform, rather than the more usual separation distance used for terrestrial mobile communications. Results demonstrate the advantages of an air-to-ground channel for urban communication, and relayed peer-to-peer links in particular
This tutorial presents an overview of the technological advances in millimeter-wave (mm-wave) circuit components, antennas, and propagation that will soon allow 60-GHz transceivers to provide multigigabit per second (multi-Gb/s) wireless communication data transfers in the consumer marketplace. Our goal is to help engineers understand the convergence of communications, circuits, and antennas, as the emerging world of subterahertz and terahertz wireless communications will require understanding at the intersections of these areas. This paper covers trends and recent accomplishments in a wide range of circuits and systems topics that must be understood to create massively broadband wireless communication systems of the future. In this paper, we present some evolving applications of massively broadband wireless communications, and use tables and graphs to show research progress from the literature on various radio system components, including on-chip and in-package antennas, radio-frequency (RF) power amplifiers (PAs), low-noise amplifiers (LNAs), voltage-controlled oscillators (VCOs), mixers, and analog-to-digital converters (ADCs). We focus primarily on silicon-based technologies, as these provide the best means of implementing very low-cost, highly integrated 60-GHz mm-wave circuits. In addition, the paper illuminates characterization techniques that are required to competently design and fabricate mm-wave devices in silicon, and illustrates effects of the 60-GHz RF propagation channel for both in-building and outdoor use. The paper concludes with an overview of the standardization and commercialization efforts for 60-GHz multi-Gb/s devices, and presents a novel way to compare the data rate versus power efficiency for future broadband devices.
Rosdiadee Nordin, Nor Fadzilah Abdulah, Beamforming techniques for massive MIMO systems in 5G: overview, classification, and trends for future research
- Ali Ehab
- Imahamod Sahlli
- Ismail
A Game of Drones: Cyber Security in UAVs
- E Dahlman
- K Lagrelius