Unmanned Aerial Vehicle Scenario Research Articles

Cognitive UAV Communication via Joint Maneuver and Power Control

This paper investigates a new scenario of spectrum sharing between unmanned aerial vehicle (UAV) and terrestrial wireless communication, in which a cognitive/secondary UAV transmitter communicates with a ground secondary receiver (SR), in the presence of a number of primary terrestrial communication links that operate over the same frequency band. We exploit the UAV's mobility in three-dimensional (3D) space to improve its cognitive communication performance while controlling the co-channel interference at the primary receivers (PRs), such that the received interference power at each PR is below a prescribed threshold termed as interference temperature (IT). First, we consider the quasi-stationary UAV scenario, where the UAV is placed at a static location during each communication period of interest. In this case, we jointly optimize the UAV's 3D placement and power control to maximize the SR's achievable rate, subject to the UAV's altitude and transmit power constraints, as well as a set of IT constraints at the PRs to protect their communications. Next, we consider the mobile UAV scenario, in which the UAV is dispatched to fly from an initial location to a final location within a given task period. We propose an efficient algorithm to maximize the SR's average achievable rate over this period by jointly optimizing the UAV's 3D trajectory and power control, subject to the additional constraints on UAV's maximum flying speed and initial/final locations. Finally, numerical results are provided to evaluate the performance of the proposed designs for different scenarios, as compared to various benchmark schemes. It is shown that in the quasi-stationary scenario the UAV should be placed at its minimum altitude while in the mobile scenario the UAV should adjust its altitude along with horizontal trajectory, so as to maximize the SR's achievable rate in both scenarios.

On the application of three dimensional HAP MIMO model in UAV environment

The increasing availability of Unmanned Aerial Vehicle (UAV) platforms leads to a variety of applications including surveillance, remote sensing, and traffic and crowd monitoring. Many of these applications rely on communication between the UAV and the ground receiver which is characterized by the presence of a Line-of-Sight (LOS) component most of the time that might lead to a multiple-input multiple-output (MIMO) communication capacity degradation. In order to design high throughput and efficient signal communication schemes for these emerging scenarios, a deep understanding of communication channel behavior is required. Based on the geometric similarity between High Altitude Platform (HAP) and UAV scenario, in this paper we analyze the application of a recently proposed three-dimensional (3D) reference MIMO channel model for HAP to UAV operation scenarios, showing the suitability of the model for lower altitude flights. A thorough capacity study of spatially and temporally correlated UAV-MIMO channels is presented. The results reveal design criteria for transmitter and receiver antenna arrays to achieve an almost constant capacity throughout the flight mission even in a non-uniform distribution of local scatterers.

Energy-Constrained Multi-UAV Coverage Path Planning for an Aerial Imagery Mission Using Column Generation

This paper presents a new Coverage Path Planning (CPP) method for an aerial imaging mission with multiple Unmanned Aerial Vehicles (UAVs). In order to solve a CPP problem with multicopters, a typical mission profile can be defined with five mission segments: takeoff, cruise, hovering, turning, and landing. The traditional arc-based optimization approaches for the CPP problem cannot accurately estimate actual energy consumption to complete a given mission because they cannot account for turning phases in their model, which may cause non-feasible routes. To solve the limitation of the traditional approaches, this paper introduces a new route-based optimization model with column generation that can trace the amount of energy required for all different mission phases. This paper executes numerical simulations to demonstrate the effectiveness of the proposed method for both a single UAV and multiple UAV scenarios for CPP problems.

Joint Maneuver and Beamwidth Optimization for UAV-Enabled Multicasting

Unmanned aerial vehicles (UAVs)-assisted communications have been an essential complement of conventional wireless networks. In this paper, we consider a UAV-enabled multicasting system, where a UAV with a directional antenna of adjustable beamwidth is employed to disseminate a common file to a group of ground users. By minimizing the mission completion time, we investigate how beamwidth control would affect the UAV's three-dimensional (3D) location/trajectory. First, we consider the quasi-stationary UAV scenario, where the UAV is deployed at a static location. In this case, we jointly optimize the 3D UAV location and antenna beamwidth under the practical constraints on the UAV's altitude and beamwidth, while ensuring that all users are covered by the main lobe of the UAV antenna. Although this problem is nonconvex, its global optimality can be obtained by using a two-step algorithm, where semi-closed solutions of the 3D location and beamwidth are derived. Next, in the mobile UAV scenario, a joint 3D UAV trajectory and beamwidth design is proposed, additionally constrained by the horizontal and vertical speed. To tackle this nonconvex problem, we develop an iterative optimization algorithm based on successive convex approximation techniques, which updates the 3D trajectory and beamwidth simultaneously in each iteration. Numerical results demonstrate the effectiveness of the proposed design as compared to benchmark schemes.

A modeling and simulation framework for UAVs utilizing 4G-LTE cellular networks

Only a small amount of work has been published on the topic of exploiting existing Long-Term Evolution-Advanced (LTE) cellular communication network infrastructure for Unmanned Aerial Vehicles (UAV) data links. This paper documents a modeling and simulation (m&s) framework that has been developed utilizing the powerful OMNeT[Formula: see text] simulation tool for assessing the feasibility and effectiveness of this prospect in various UAV scenarios. Using multiple scenarios, we have studied the data rate requirements for communications between small and medium-sized UAVs and base stations. Using this framework, we have shown that the data rate requirements for the links are within the data throughput achieved by LTE networks. The developed framework implements a propagation model endorsed by the 3GPP LTE project team and also accurately models the high mobility of UAVs. The framework is highly configurable and extensible and boasts of automatic aggregation of results and chart plotting. The outcomes of this research may be utilized by industry for rapidly deploying highly mobile, low-cost UAVs in a wide range of applications and scenarios.

Network-Coded Multiple Access on Unmanned Aerial Vehicle

This paper presents the first network-coded multiple access (NCMA) downlink system on unmanned aerial vehicle (UAV). The use of UAV as a mobile aerial base station has received much attention in the 5G community in the context of highly mobile and flexible-configurable communication systems. As UAVs are limited by their flight time in the air, achieving high spectral and power efficiency while they are inflight is of great importance. Non-orthogonal multiple access (NOMA) is a promising technique to increase the spectral and power efficiency. Conventional NOMA downlink that makes use of superposition coding in combination with successive interference cancellation (SIC) decoding does not work well in scenarios where the channel conditions of different downlink users are not readily available at the transmitter side. This is the case, for example, in the UAV scenario in which the UAV transmitter moves quickly, causing the channel conditions to vary in a very dynamic manner. This paper investigates a new NOMA downlink architecture, referred to as network-coded multiple access . In the absence of channel information, an NCMA transmitter allocates equal power to the superposed signals of different downlink users. A key challenge is how to achieve high NOMA throughput under such equal power allocation. Toward this end, NCMA makes joint use of physical-layer network coding (PNC) and multiuser decoding (MUD) together with a new superposition coding scheme, referred to as NCMA-based superposition coding . In NCMA-based superposition coding, equal powers are allocated to the signals of different users, but a relative phase offset between the signals is introduced to optimize PNC and MUD decodings. To demonstrate the feasibility and advantage of the NCMA downlink, we implemented our designs on a software-defined radio and UAV. Our experimental results show that NCMA is robust against varying channel conditions. Moreover, the throughput of NCMA can outperform the state-of-the-art SIC-based superposition coding system and the time-division multiple access system by 50% and 80%, respectively, demonstrating that NCMA is a practical solution to boost throughput in UAV NOMA.

Equalization Techniques of Control and Non-Payload Communication Links for Unmanned Aerial Vehicles

In the next years, several new applications involving unmanned aerial vehicles (UAVs) for public and commercial uses are envisaged. In such developments, since UAVs are expected to operate within the public airspace, a key issue is the design of reliable control and non-payload communication (CNPC) links connecting the ground control station to the UAV. At the physical layer, CNPC design must cope with timeand frequency-selectivity (so-called double selectivity) of the wireless channel, due to lowaltitude operation and flight dynamics of the UAV. In this paper, we consider the transmission of continuous phase modulated (CPM) signals for UAV CNPC links operating over doubly-selective channels. Leveraging on the Laurent representation for a CPM signal, we design a two-stage receiver: the first one is a linear time-varying (LTV) equalizer, synthesized under either the zero-forcing (ZF) or minimum mean-square error (MMSE) criterion; the second one recovers the transmitted symbols from the pseudo-symbols of the Laurent representation in a simple recursive manner. In addition to LTV-ZF and LTV-MMSE equalizers, their widely-linear versions are also developed, to take into account the possible noncircular features of the CPM signal. Moreover, relying on a basis expansion model of the doubly-selective channel, we derive frequencyshift versions of the proposed equalizers, by discussing their complexity issues and proposing simplified implementations. Monte Carlo numerical simulations show that the proposed receiving structures are able to satisfactorily equalize the doubly-selective channel in typical UAV scenarios.

Modelling unmanned aerial vehicles base station in ground‐to‐air cooperative networks

After disasters, road side units, which can provide wireless communications to vehicles, may fail due to physical damages or power outages. Vehicle-to-vehicle communication also may not perform well owing to lack of neighbours in low vehicle density scenario or collisions in high vehicle density circumstance which may happen in disaster areas. In this study, the deployment of unmanned aerial vehicles (UAVs) as flying base stations to improve connectivity among vehicles in disaster affected areas is analysed. First, the connectivity among vehicles as a function of the altitude of the UAV is derived. Furthermore, multiple UAVs scenario is analysed to determine the minimum number of UAVs needed to guarantee a target connectivity threshold among vehicles for a given geographical area and vehicle density. Numerical and simulation results verify that there exist optimal UAV altitudes for different scenarios as shown in the authors' analyses, and the simulation results also verify that the number of UAVs must be larger than a certain value to guarantee a target connectivity threshold among vehicles.

Towards Autonomous Modular UAV Missions: The Detection, Geo-Location and Landing Paradigm.

Nowadays, various unmanned aerial vehicle (UAV) applications become increasingly demanding since they require real-time, autonomous and intelligent functions. Towards this end, in the present study, a fully autonomous UAV scenario is implemented, including the tasks of area scanning, target recognition, geo-location, monitoring, following and finally landing on a high speed moving platform. The underlying methodology includes AprilTag target identification through Graphics Processing Unit (GPU) parallelized processing, image processing and several optimized locations and approach algorithms employing gimbal movement, Global Navigation Satellite System (GNSS) readings and UAV navigation. For the experimentation, a commercial and a custom made quad-copter prototype were used, portraying a high and a low-computational embedded platform alternative. Among the successful targeting and follow procedures, it is shown that the landing approach can be successfully performed even under high platform speeds.