Unmanned Aerial Vehicle Transmission Research Articles

Joint Unmanned Aerial Vehicle (UAV) Deployment and Power Control for Internet of Things Networks

In this paper, we study unmanned aerial vehicle (UAV) aided internet of things (IoT) networks, where UAVs facilitate data transmission of IoT devices. We focus on uplink transmission from IoT devices to base station (BS). Multiple UAVs are employed as UAV relays between IoT devices and BS to enhance received signal strength at BS. Specifically, IoT devices periodically detect wireless channel quality between IoT devices and BS, as well as that among IoT devices. Based on the wireless channel quality, we propose a distributed user cluster (UC) algorithm to cluster IoT devices as multiple UCs. One IoT device in a UC, which is named cluster head (CH), is selected to connect to the BS and gather uplink signals of IoT devices. If the wireless channel quality between CH and BS is good, a direct connection between CH and the BS can be built. Otherwise, UAVs are divided into multiple UAV cooperative relay clusters (CRCs). The UAVs in a CRC are located between a specific CH and BS to relay uplink signals. We then formulate a system optimization model to minimize system power consumption, where UAV deployment and transmission power of UAV are jointly optimized. We solve this optimization problem by dual decomposition method. By extensive simulations, we demonstrate the effectiveness of the proposed algorithm. We also reveal several interesting insights for practical UAV aided IoT networks.

Hybrid Offline-Online Design for UAV-Enabled Data Harvesting in Probabilistic LoS Channels

This paper considers an unmanned aerial vehicle (UAV)-enabled wireless sensor network (WSN) in urban areas, where a UAV is deployed to collect data from distributed sensor nodes (SNs) within a given duration. To characterize the occasional building blockage between the UAV and SNs, we construct the probabilistic line-of-sight (LoS) channel model for a Manhattan-type city by using the combined simulation and data regression method, which is shown in the form of a generalized logistic function of the UAV-SN elevation angle. We assume that only the knowledge of SNs’ locations and the probabilistic LoS channel model is known a priori , while the UAV can obtain the instantaneous LoS/Non-LoS channel state information (CSI) with the SNs in real time along its flight. Our objective is to maximize the minimum (average) data collection rate from all the SNs for the UAV. To this end, we formulate a new rate maximization problem by jointly optimizing the UAV three-dimensional (3D) trajectory and transmission scheduling of SNs. Although the optimal solution is intractable due to the lack of complete UAV-SNs CSI, we propose in this paper a novel and general design method, called hybrid offline-online optimization, to obtain a suboptimal solution to it, by leveraging both the statistical and real-time CSI. Essentially, our proposed method decouples the joint design of UAV trajectory and communication scheduling into two phases: namely, an offline phase that determines the UAV path prior to its flight based on the probabilistic LoS channel model, followed by an online phase that adaptively adjusts the UAV flying speeds along the offline optimized path as well as communication scheduling based on the instantaneous UAV-SNs CSI and SNs’ individual amounts of data received accumulatively. Extensive simulation results are provided to show the significant rate performance improvement of our proposed design as compared to various benchmark schemes.

Online Trajectory and Radio Resource Optimization of Cache-Enabled UAV Wireless Networks With Content and Energy Recharging

Recently, unmanned aerial vehicle (UAV)-assisted wireless communication technology has been proposed to exploit the favorable propagation property and flexibility of air-to-ground channels to support content-centric caching and enhance wireless network capacity. In this article, we propose an online UAV-assisted wireless caching design via jointly optimizing UAV trajectory, transmission power and caching content scheduling. Specifically, we formulate the joint optimization of online UAV trajectory and caching content delivery as an infinite-horizon ergodic Markov Decision Process (MDP) problem to obtain a QoE-optimal solution based on the concept of request queues in wireless caching networks. By exploiting the fluid approximation approach, we first derive an optimal control policy from an approximated Bellman equation. Based on this, an actor-critic based online reinforcement learning algorithm is proposed to solve the problem. Finally, simulation results are provided to show that the proposed solution can achieve significant gain over the existing baselines.

UAV-Aided Wireless Communication Design With Energy Constraint in Space-Air-Ground Integrated Green IoT Networks

As more vehicles connect to the internet, they become an important and growing segment of Internet of Things (IoT). To enhance the performance of vehicle-to-network (V2N) communication with green objectives, satellite and unmanned aerial vehicle (UAV) are jointly applied to aid ground communication facility by leveraging the coordinated multi-point transmission technique. This paper investigates a novel architecture of V2N communication in the space-air-ground integrated IoT network consisted of ground base stations (BSs), a UAV as well as one satellite. In the V2N communication model, the UAV, receiving the requested data from the satellite, and BS cooperatively serve the ground vehicle. The goal of this paper is to maximize the achievable rate of the ground vehicle by jointly optimizing the transmit power allocations (i.e., UAV and ground BS) and UAV trajectory, subjecting to the UAV energy constraint, UAV transmission constraint as well as UAV mobility constraint. However, there exists an intractable issue of the formulated problem in a complicated non-convex form. To this end, we decompose it into two sub-problems by fixing UAV trajectory and the power allocations alternately. Specifically, the closed-form expressions are derived to solve the sub-problem with the given UAV trajectory. For the sub-problem with the given power allocations, the UAV trajectory is calculated by using successive convex approximation (SCA) technique. Through the alternation of two methods and iterative operation, the joint optimal solution to the problem is achieved. Finally, numerical results verify that UAV plays a pivotal role in overcoming the tradeoff between higher performance and green communication.

Optimal Detection of UAV's Transmission With Beam Sweeping in Covert Wireless Networks

In this work, a detection strategy based on multiple antennas with beam sweeping is developed to detect the potential transmission of unmanned aerial vehicle (UAV) in wireless networks. Specifically, suspicious angle range where the UAV may present is divided into different sectors to potentially increase detection accuracy by using beamforming gain. We then develop the optimal detector and derive its detection error probability in a closed-form expression. We also utilize the Pinsker's inequality and Kullback-Leibler divergence to yield low-complex approximation for the detection error probability, based on which we obtain some significant insights on the detection performance. Our numerical results show that there exists an optimal number of sectors that can minimize the detection error probability in some scenarios (e.g., when the number of measurements is limited). Intuitively, this can be explained by the fact that there exists an optimal accuracy of the telescope used to find an object in the sky within limited time period.

Self-Energized UAV-Assisted Scheme for Cooperative Wireless Relay Networks

Unmanned aerial vehicles (UAVs) have recently been envisaged as an enabling technology of 5G. UAVs act as an intermediate relay node to facilitate uninterrupted, high quality communication between information sources and their destination. However, UAV energy management has been a major issue of consideration due to limited power supply, affecting flight duration. Thus, we introduce in this paper a unified energy management framework by resorting to wireless power transfer (WPT), simultaneous wireless information and power transfer (SWIPT) and self-interference (SI) energy harvesting (EH) schemes, in cooperative relay communications. In our new technique, UAVs are deployed as relays equipped with a decode and forward protocol and EH capability operating in a full-duplex (FD) mode. The UAV assists information transmission between a terrestrial base station and a user. The UAV's transmission capability is powered exclusively by the energy harvested from WPT, radio frequency signal transmitted from the source via time-switching SWIPT protocol and SI exploitation. We improve the overall system throughput by the use of FD based UAV-assisted cooperative system. In this proposed system, we formulate two optimization problems to minimize end-to-end outage probability, subject to UAV's power profile and trajectory for a DF relay scheme, respectively. The KKT conditions have been used to obtain closed-form solutions for the two formulated problems. Numerical simulation results validate all the theoretical results. We demonstrate that the performance of our proposed unified EH scheme outperforms that of existing techniques in the literature.

UAV Transmission Line Inspection Object Recognition based on Mask R-CNN

Currently, unmanned aerial vehicles (UAV) are applied to routine inspection tasks of power transmission devices. Deep-learning algorithm and machine vision have attracted much attention in the field of the UAV’s autonomous control as it’s an effective way to improve the efficiency of inspection. Considering the differences between the distant and close view, this paper adopts Mask R-CNN to detect various components of power transmission devices but use the methods such as processing of the edge, hole filling and Hough Transform identify the wires in distant. Some major components, such as pole, truss, cross arm, insulator string and so on, can be 100% recognized. This proposed model shows the characteristics of high recognition speed and high accuracy, which can assist UAV to inspect well.

Interference modeling and analysis in 3-dimensional directional UAV networks based on stochastic geometry

Abstract In this paper, we model and characterize interference of directional unmanned aerial vehicle (UAV) networks based on stochastic geometry, where each UAV is equipped with a directional antenna and it communicates with another UAV that is located in the three dimensional (3D) space. In particular, the 3D location of UAVs is assumed to be uniformly distributed in a certain volume, which is modeled by Poisson point process. Given a beamwidth, we first design an ideal 3D directional antenna beam pattern with a constant gain of both main-lobe and side-lobe. To model the interference in the UAV network, we analyze the effect of elevation and azimuth between a typical UAV receiver and an interfering UAV transmitter with spherical coordinate system. Then, we investigate distribution of the aggregate interference at a typical UAV receiver from multiple UAVs in terms of side-lobe gain, beamwidth, height of UAV, and distance of a UAV transmitter–receiver pair.

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.

UAV-Relaying-Assisted Secure Transmission With Caching

Unmanned aerial vehicle (UAV) can be utilized as a relay to connect nodes with long distance, which can achieve significant throughput gain owing to its mobility and line-of-sight (LoS) channel with ground nodes. However, such LoS channels make UAV transmission easy to eavesdrop. In this paper, we propose a novel scheme to guarantee the security of UAV-relayed wireless networks with caching via jointly optimizing the UAV trajectory and time scheduling. For every two users that have cached the required file for the other, the UAV broadcasts the files together to these two users, and the eavesdropping can be disrupted. For the users without caching, we maximize their minimum average secrecy rate by jointly optimizing the trajectory and scheduling, with the secrecy rate of the caching users satisfied. The corresponding optimization problem is difficult to solve due to its non-convexity, and we propose an iterative algorithm via successive convex optimization to solve it approximately. Furthermore, we also consider a benchmark scheme in which we maximize the minimum average secrecy rate among all users by jointly optimizing the UAV trajectory and time scheduling when no user has the caching ability. Simulation results are provided to show the effectiveness and efficiency of our proposed scheme.

Transceiver Design and Multihop D2D for UAV IoT Coverage in Disasters

When natural disasters strike, the coverage for Internet of Things (IoT) may be severely destroyed, due to the damaged communications infrastructure. Unmanned aerial vehicles (UAVs) can be exploited as flying base stations to provide emergency coverage for IoT, due to its mobility and flexibility. In this paper, we propose multiantenna transceiver design and multihop device-to-device (D2D) communication to guarantee the reliable transmission and extend the UAV coverage for IoT in disasters. First, multihop D2D links are established to extend the coverage of UAV emergency networks due to the constrained transmit power of the UAV. In particular, a shortest-path-routing algorithm is proposed to establish the D2D links rapidly with minimum nodes. The closed-form solutions for the number of hops and the outage probability are derived for the uplink and downlink. Second, the transceiver designs for the UAV uplink and downlink are studied to optimize the performance of UAV transmission. Due to the nonconvexity of the problem, they are first transformed into convex ones and then, low-complexity algorithms are proposed to solve them efficiently. Simulation results show the performance improvement in the throughput and outage probability by the proposed schemes for UAV wireless coverage of IoT in disasters.

Secure UAV Communication With Cooperative Jamming and Trajectory Control

This letter presents a new cooperative jamming approach to secure the unmanned aerial vehicle (UAV) communication by leveraging jamming from other nearby UAVs to defend against the eavesdropping. In particular, we consider a two-UAV scenario when one UAV transmitter delivers the confidential information to a ground node (GN), and the other UAV jammer cooperatively sends the artificial noise to confuse the ground eavesdropper for protecting the confidentiality of the data transmission. By exploiting the fully controllable mobility, the two UAVs can adaptively adjust their locations over time (a.k.a. trajectories) to facilitate the secure communication and cooperative jamming. We assume that the two UAVs perfectly know the GN's location and partially know the eavesdropper's location a priori. Under this setup, we maximize the average secrecy rate from the UAV transmitter to the GN over one particular time period, by optimizing the UAVs' trajectories, jointly with their communicating/jamming power allocations. Although the formulated problem is non-convex, we propose an efficient solution by applying the techniques of alternating optimization and successive convex approximation.

Three-Dimensional Non-Stationary Wideband Geometry-Based UAV Channel Model for A2G Communication Environments

In this paper, we propose a novel three-dimensional multiple-input multiple-output channel model to describe the air-to-ground (A2G) communication environments. The model introduces the unmanned aerial vehicle (UAV) transmitter and ground mobile receiver (MR) located at the foci points of the boundary ellipsoid, while different ellipsoids represent the propagation properties for different time delays. In light of this, we are able to investigate the propagation properties of the A2G channel model for different time delays. Furthermore, the time-varying parameters of the azimuth angle of departure, the elevation angle of departure, the azimuth angle of arrival, and elevation angle of arrival are derived to properly describe the channel non-stationarity, which is caused by the motion of the UAV transmitter, cluster, and MR. The impacts of the movement properties of the cluster in both the azimuth and elevation planes are investigated on the channel characteristics, i.e., spatial cross-correlation functions, temporal autocorrelation functions, Doppler power spectrum density, and power delay profiles.

Adaptive Hello Interval in FANET Routing Protocols for Green UAVs

With the recent advances in unmanned aerial vehicles (UAVs), the development of energy-efficient networking technology for mission-oriented multiple cooperative UAVs has become crucial. Routing in flying ad-hoc networks (FANETs) with UAVs is a challenging issue because of the high speed and sudden changes in direction of UAVs. Traditional routing protocols in FANETs periodically send hello messages for the establishment and maintenance of the routes. However, sending hello messages periodically after a fixed interval increases bandwidth wastage when the hello interval is excessively short or causes long delays in neighbour discovery when the hello interval is overly long. Moreover, several disconnected UAV groups have been observed in which the group members are connected among themselves but detached from the main network. By exchanging excessive hello messages inside the group, the UAVs maintain an unnecessary neighbourhood, causing wastage of energy. However, FANETs have certain advantages, such as knowledge about mission-related information. To solve the problem of unnecessary energy drain, we propose a novel adaptive hello interval scheme-energy efficient hello (EE-Hello)-based on available mission-related information, such as the volume of the allowed airspace, number of UAVs, UAV transmission range, and UAV speed. We present a method to decide the distance that a UAV needs to travel before sending a hello message. We also specify a technique to determine the number of UAVs necessary to achieve specific network requirements, such as packet delivery ratio or throughput, with the expenditure of minimum energy. We show that the proposed EE-Hello can save about 25% of the energy currently used, by suppressing unnecessary hello messages without degrading the overall network throughput.

Unmanned Aerial Patrol Technology Based on Tracking Algorithm of Target Tracking

In order to study the power patrol technology of unmanned aerial vehicle, the tracking algorithm was applied. The automatic patrolling of power lines was discussed in terms of algorithms. An unmanned aerial vehicle transmission line inspection method based on machine vision was proposed. The image and video of the unmanned aerial vehicle inspection of the power line had a complex background. By Wiener filtering de-noising and probability density functions, the image clarity was improved. According to the existing tracking techniques and algorithms, a Camshaft target tracking algorithm based on lossless Kalman filter was proposed. The method of non-destructive Kalman filter was adopted to predict the region of interest of power line identification. Using the Camshaft algorithm, the prediction of the window was searched and the size of the window was adjusted. Transmission lines were tracked in real time. The results showed that the restoration effect of the algorithm was obvious. The clarity of the image was improved. It prepared for the extraction and tracking of the future transmission lines. Therefore, the proposed method provides a feasible way for the UAV power line inspection technology based on machine vision.

Joint Trajectory and Power Optimization for UAV Sensing Over Cellular Networks

In this letter, we study a cellular unmanned aerial vehicle (UAV) system in which one UAV jointly performs sensing tasks and data transmission to the base station. We maximize the energy efficiency (EE) of the UAV by optimizing UAV sensing and transmission. To solve this non-convex problem, we decouple it into two subproblems: UAV sensing optimization and UAV transmission optimization. In the UAV sensing optimization, we design the UAV trajectory and speed with convex optimization and differential methods. In the UAV transmission optimization, the transmission power is derived by the extremum principles. Simulation results show that the proposed algorithm can efficiently improve the EE in the cellular UAV system.

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.

User-Centric View of Unmanned Aerial Vehicle Transmission Against Smart Attacks

Unmanned aerial vehicle (UAV) systems are vulnerable to smart attackers, who are selfish and subjective end-users and use smart radio devices to change their attack types and policies based on the ongoing UAV transmission and network states. In this paper, we apply prospect theory to formulate a subjective smart attack game for the UAV transmission, in which a smart attacker Eve makes subjective decisions to choose the attack type such as jamming, spoofing, and eavesdropping without knowing the attack detection accuracy of the UAV system, and the UAV transmit power on multiple radio channels is chosen to resist smart attacks. Reinforcement-learning-based UAV power allocation strategies are proposed to achieve the optimal power allocation against smart attacks without knowing the attack model and the channel model in the dynamic game. A deep Q-learning-based UAV power allocation strategy combines Q-learning and deep learning to accelerate the learning speed for the case with a large number of channel states and attack modes. Simulation results show that our proposed UAV power allocation strategy can suppress the attack motivation of subjective smart attackers and increase the secrecy capacity and the utility of the UAV system.

Spectrum Sensing Optimization in an UAV-Based Cognitive Radio

In cognitive radio (CR), the detection performance of traditional spectrum sensing may be decreased due to the multipath fading and shadowing effect. However, unmanned aerial vehicle (UAV) can receive a higher strength signal without suffering severe fading and shadowing. In this paper, an UAV-based CR is proposed to improve spectrum sensing performance and access the idle spectrum. The UAV performs circular flight with the PU locating at the center, and the flight period is divided into sensing radian and transmission radian. Only when the absence of the PU has been detected within sensing radian can the UAV access the spectrum. The spectrum sensing optimization is formulated as an optimization problem, which seeks to maximize the effective throughput of the UAV by optimizing the sensing radian subject to the constraint of the interference throughput. Then, virtual cooperative spectrum sensing is proposed to improve sensing performance in fading channel, and the effective throughput is maximized by jointly optimizing local sensing radian and the number of sensing slots. The simulation results show that better UAV transmission performance can be obtained by optimizing spectrum sensing, and the virtual cooperative spectrum sensing outperforms the single spectrum sensing especially in deep fading channel.