10 research outputs found

    Multi-frequency backhaul analysis for UABS in disaster situations

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    When a disaster occurs, the land-based cellular network could go offline for some days. Using an Unmanned Aerial Base Station (UABS) network is a promising solution to serve unconnected ground users. In this article, we propose a multifrequency backhaul architecture, which considers power and capacity constraints, to support the UABS network in a realistic 3D scenario in the city of Ghent, Belgium. Simulations results show that at the optimal flight height (80 m), up to 87% of the users could be supported using the multifrequency scenario compared with single frequency scenarios where coverage is about 70%

    Aerial Base Station Deployment for Post-Disaster Public Safety Applications

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    Earthquakes and floods are constant threats to most of the countries in the world. After such catastrophes, a rapid response is needed, which includes communications not only for first responders but also for local civilians. Even though there are technologies and specialized personnel for rapid deployment, it is common that external factors will hinder the arrival of help while communication requirements are urgently required. Such communication technologies would aid tasks regarding organization and information dissemination from authorities to the civilians and vice-versa. This necessity is due to protocols and applications to allocate the number of emergency resources per location and to locate missing people. In this thesis, we investigate the deployment problem of Mobile Aerial Base Stations (MABS). Our main objective is to ensure periodic wireless communication for geographically spread User Equipment (UE) based on LTE technology. First, we establish a precedent of emergency situations where MABS would be useful. We also provide an introduction to the study and work conducted in this thesis. Second, we provide a literature review of existing solutions was made to determine the advantages and disadvantages of certain technologies regarding the described necessity. Third, we determine how MABS, such as gliders or light tactical balloons that are assumed to be moving at an average speed of 50 km/h, will be deployed. These MABS would visit different cluster centroids determined by an Affinity Propagation Clustering algorithm. Additionally, a combination of graph theory and Genetic Algorithm (GA) is implemented through mutators and fitness functions to obtain best flyable paths through an evolution pool of 100. Additionally, Poisson, Normal, and Uniform distributions are utilized to determine the amount of Base Stations and UEs. Then, for every distribution combination, a set of simulations is conducted to obtain the best flyable paths. Serviced UE performance indicators of algorithm efficiency are analyzed to determine whether the applied algorithm is effective in providing a solution to the presented problem. Finally, in Chapter 5, we conclude our work by supporting that the proposed model would suffice the needs of mobile users given the proposed emergency scenario. Adviser: Yi Qia

    Towards UAV Assisted 5G Public Safety Network

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    Ensuring ubiquitous mission-critical public safety communications (PSC) to all the first responders in the public safety network is crucial at an emergency site. The first responders heavily rely on mission-critical PSC to save lives, property, and national infrastructure during a natural or human-made emergency. The recent advancements in LTE/LTE-Advanced/5G mobile technologies supported by unmanned aerial vehicles (UAV) have great potential to revolutionize PSC. However, limited spectrum allocation for LTE-based PSC demands improved channel capacity and spectral efficiency. An additional challenge in designing an LTE-based PSC network is achieving at least 95% coverage of the geographical area and human population with broadband rates. The coverage requirement and efficient spectrum use in the PSC network can be realized through the dense deployment of small cells (both terrestrial and aerial). However, there are several challenges with the dense deployment of small cells in an air-ground heterogeneous network (AG-HetNet). The main challenges which are addressed in this research work are integrating UAVs as both aerial user and aerial base-stations, mitigating inter-cell interference, capacity and coverage enhancements, and optimizing deployment locations of aerial base-stations. First, LTE signals were investigated using NS-3 simulation and software-defined radio experiment to gain knowledge on the quality of service experienced by the user equipment (UE). Using this understanding, a two-tier LTE-Advanced AG-HetNet with macro base-stations and unmanned aerial base-stations (UABS) is designed, while considering time-domain inter-cell interference coordination techniques. We maximize the capacity of this AG-HetNet in case of a damaged PSC infrastructure by jointly optimizing the inter-cell interference parameters and UABS locations using a meta-heuristic genetic algorithm (GA) and the brute-force technique. Finally, considering the latest specifications in 3GPP, a more realistic three-tier LTE-Advanced AG-HetNet is proposed with macro base-stations, pico base-stations, and ground UEs as terrestrial nodes and UABS and aerial UEs as aerial nodes. Using meta-heuristic techniques such as GA and elitist harmony search algorithm based on the GA, the critical network elements such as energy efficiency, inter-cell interference parameters, and UABS locations are all jointly optimized to maximize the capacity and coverage of the AG-HetNet

    5G and beyond networks

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    This chapter investigates the Network Layer aspects that will characterize the merger of the cellular paradigm and the IoT architectures, in the context of the evolution towards 5G-and-beyond, including some promising emerging services as Unmanned Aerial Vehicles or Base Stations, and V2X communications

    Deployment of drone-based small cells for public safety communication system

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    In the event of a natural disaster, communications infrastructure plays an important role in organizing effective rescue services. However, the infrastructure-based communications are often affected during severe disaster events such as earthquakes, landslides, floods, and storm surges. Addressing this issue, the article proposes a novel drone-based cellular infrastructure to revive necessary communications for out-of-coverage user equipment (UE) which is in the disaster area. In particular, a matching game algorithm is proposed using one-to-many approach wherein several drone small cells (DSCs) are deployed to match different UEs to reach a stable connection with optimal throughput. In addition, a medium access control framework is then developed to optimize emergency and high priority communications initiated from the rescue workers and vulnerable individuals. The simulation results show that the throughput for the out-of-coverage UEs are significantly improved when the DSCs are deployed in public safety network while the channel access delay is also notably reduced for emergency communications within the affected areas

    Deployment of drone-based small cells for public safety communication system

    Get PDF
    In the event of a natural disaster, communications infrastructure plays an important role in organizing effective rescue services. However, the infrastructure-based communications are often affected during severe disaster events such as earthquakes, landslides, floods, and storm surges. Addressing this issue, the article proposes a novel drone-based cellular infrastructure to revive necessary communications for out-of-coverage user equipment (UE) which is in the disaster area. In particular, a matching game algorithm is proposed using one-to-many approach wherein several drone small cells (DSCs) are deployed to match different UEs to reach a stable connection with optimal throughput. In addition, a medium access control framework is then developed to optimize emergency and high priority communications initiated from the rescue workers and vulnerable individuals. The simulation results show that the throughput for the out-of-coverage UEs are significantly improved when the DSCs are deployed in public safety network while the channel access delay is also notably reduced for emergency communications within the affected areas

    Stochastic Geometry Based Analysis of Capacity, Mobility and Energy Efficiency for Dense Heterogeneous Networks

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    In recent years, the increase in the population of mobile users and the advances in computational capabilities of mobile devices have led to an exponentially increasing traffic load on the wireless networks. This trend is foreseen to continue in the future due to the emerging applications such as cellular Internet of things (IoT) and machine type communications (MTC). Since the spectrum resources are limited, the only promising way to keep pace with the future demand is through aggressive spatial reuse of the available spectrum which can be realized in the networks through dense deployment of small cells. There are many challenges associated with such densely deployed heterogeneous networks (HetNets). The main challenges which are considered in this research work are capacity enhancement, velocity estimation of mobile users, and energy efficiency enhancement. We consider different approaches for capacity enhancement of the network. In the first approach, using stochastic geometry we theoretically analyze time domain inter-cell interference coordination techniques in a two-tier HetNet and optimize the parameters to maximize the capacity of the network. In the second approach, we consider optimization of the locations of aerial bases stations carried by the unmanned aerial vehicles (UAVs) to enhance the capacity of the network for public safety and emergency communications, in case of damaged network infrastructure. In the third approach, we introduce a subsidization scheme for the service providers through which the network capacity can be improved by using regulatory power of the government. Finally, we consider the approach of device-to-device communications and multi-hop transmissions for enhancing the capacity of a network. Velocity estimation of high speed mobile users is important for effective mobility management in densely deployed small cell networks. In this research, we introduce two novel methods for the velocity estimation of mobile users: handover-count based velocity estimation, and sojourn time based velocity estimation. Using the tools from stochastic geometry and estimation theory, we theoretically analyze the accuracy of the two velocity estimation methods through Cramer-Rao lower bounds (CRLBs). With the dense deployment of small cells, energy efficiency becomes crucial for the sustained operation of wireless networks. In this research, we jointly study the energy efficiency and the spectral efficiency in a two-tier HetNet. We optimize the parameters of inter-cell interference coordination technique and study the trade-offs between the energy efficiency and spectral efficiency of the HetNet

    Internet Of Things and Humans

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    The never ending demand for capacity and the need for ubiquitous radio coverage requires attention to the design of new radio networks. Incoming paradigms (industry 4.0, machine to machine communication and Internet of Things) will overburden even more cellular networks. Current (4G) and near-future (5G) architecture will not be able to support such traffic increase. Moreover, space-time and content heterogeneity of data should be exploited to improve network performance. However, current networks performance are deteriorated by this heterogeneity. Pico- and femto-cell networks, with cell densification, are proposed as solution. A drawback, is the urgency of high-speed backhaul to connect the cells among themselves and the core network. Current research trends assume that the density of cells will be comparable to user density. In such a situation, deploying high-speed backhaul will be expensive. Moreover, regardless whatever deployment of cells, connectivity is a commodity given as always granted. Modern technologies and services rely on stable networks. Nonetheless, whenever also a basic connectivity fails because of a disaster, not even a basic form of radio communication can be provided. Flexible networks adapting to the environment "on the go", could reduce this problem. A to alleviate the aforementioned problems, My work unfolds starting from a couple of intuitions. 1- Traffic demand is not just a data to be processed, transmitted and answered to. The kind of data producing the traffic matters. Thus, we should treat different traffic streams accordingly. This facet of my work is treated under different points of view in the dissertation. 2- In current networks, users are seen as "passive", being just source and/or destination of a traffic stream. There are reasons to envision that users could be exploited as "active" users participating to the network itself fostering its performance. This considerations are accounted in the so called Delay Tolerant Networks

    Drone Base Station Trajectory Management for Optimal Scheduling in LTE-Based Sparse Delay-Sensitive M2M Networks

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    Providing connectivity in areas out of reach of the cellular infrastructure is a very active area of research. This connectivity is particularly needed in case of the deployment of machine type communication devices (MTCDs) for critical purposes such as homeland security. In such applications, MTCDs are deployed in areas that are hard to reach using regular communications infrastructure while the collected data is timely critical. Drone-supported communications constitute a new trend in complementing the reach of the terrestrial communication infrastructure. In this study, drones are used as base stations to provide real-time communication services to gather critical data out of a group of MTCDs that are sparsely deployed in a marine environment. Studying different communication technologies as LTE, WiFi, LPWAN and Free-Space Optical communication (FSOC) incorporated with the drone communications was important in the first phase of this research to identify the best candidate for addressing this need. We have determined the cellular technology, and particularly LTE, to be the most suitable candidate to support such applications. In this case, an LTE base station would be mounted on the drone which will help communicate with the different MTCDs to transmit their data to the network backhaul. We then formulate the problem model mathematically and devise the trajectory planning and scheduling algorithm that decides the drone path and the resulting scheduling. Based on this formulation, we decided to compare between an Ant Colony Optimization (ACO) based technique that optimizes the drone movement among the sparsely-deployed MTCDs and a Genetic Algorithm (GA) based solution that achieves the same purpose. This optimization is based on minimizing the energy cost of the drone movement while ensuring the data transmission deadline missing is minimized. We present the results of several simulation experiments that validate the different performance aspects of the technique
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