Unmanned Aerial Vehicle Relay Research Articles

Proactive optimization of transmission power and 3D trajectory in UAV-assisted relay systems with mobile ground users

In this paper, an Unmanned Aerial Vehicle (UAV)-assisted relay communication system is studied, where a UAV is served as a flying relay to maintain a communication link between a mobile source node and a remote destination node. Specifically, an average outage probability minimization problem is formulated firstly, with the constraints on the transmission power of the source node, the maximum energy consumption budget, the transmission power, the speed and acceleration of the flying UAV relay. Next, the closed-form of outage probability is derived, under the hybrid line-of-sight and non-line-of-sight probability channel model. To deal with the formulated nonconvex optimization, a long-term proactive optimization mechanism is developed. In particular, firstly, an approximation for line-of-sight probability and a reformulation of the primal problem are given, respectively. Then, the reformulated problem is transformed into two subproblems: one is the transmission power optimization with given UAV’s trajectory and the other is the trajectory optimization with given transmission power allocation. Next, two subproblems are tackled via tailoring primal–dual subgradient method and successive convex approximation, respectively. Furthermore, a proactive optimization algorithm is proposed to jointly optimize the transmission power allocation and the three-dimensional trajectory. Finally, simulation results demonstrate the performance of the proposed algorithm under various parameter configurations.

Q-LBR: Q-learning Based Load Balancing Routing for UAV-assisted VANET.

Although various unmanned aerial vehicle (UAV)-assisted routing protocols have been proposed for vehicular ad hoc networks, few studies have investigated load balancing algorithms to accommodate future traffic growth and deal with complex dynamic network environments simultaneously. In particular, owing to the extended coverage and clear line-of-sight relay link on a UAV relay node (URN), the possibility of a bottleneck link is high. To prevent problems caused by traffic congestion, we propose Q-learning based load balancing routing (Q-LBR) through a combination of three key techniques, namely, a low-overhead technique for estimating the network load through the queue status obtained from each ground vehicular node by the URN, a load balancing scheme based on Q-learning and a reward control function for rapid convergence of Q-learning. Through diverse simulations, we demonstrate that Q-LBR improves the packet delivery ratio, network utilization and latency by more than 8, 28 and 30%, respectively, compared to the existing protocol.

Automatic UAV Relay Communication System Based on Microwave Directional Antenna

To tackle the communication problem of unmanned aerial vehicle (UAV) complex aerial operations, we propose an automatic UAV relay communication system based on microwave directional antenna. Our system is organized by the design scheme, overall structure and working process. The system has the ability to transmit information at a distance, which solves the problem of limited communication distance caused by the transmission power limitation of UAV communication system. The system can automatically switch into different communication mode according to communication network environments to improve the communication quality. In addition, it increases the mobility and enhances endurance in severe environment to meet the emergency communication covered on demand.

URLLC Facilitated by Mobile UAV Relay and RIS: A Joint Design of Passive Beamforming, Blocklength, and UAV Positioning

Upcoming fifth-generation (5G) networks need to support novel ultrareliable and low-latency (URLLC) traffic that utilizes short packets. This requires a paradigm shift as traditional communication systems are designed to transmit only long data packets based on Shannon’s capacity formula, which poses a challenge for system designers. To address this challenge, this article relies on an unmanned aerial vehicle (UAV) and a reconfigurable intelligent surface (RIS) to deliver short URLLC instruction packets between ground Internet-of-Things (IoT) devices. In this context, we perform passive beamforming of RIS antenna elements as well as nonlinear and nonconvex optimization to minimize the total decoding error rate and find the UAV’s optimal position and blocklength. In this article, a novel, polytope-based method from the class of direct search methods (DSMs) named Nelder–Mead simplex (NMS) is used to solve the optimization problem based on its computational efficiency; in terms of lesser number of required iterations to evaluate objective function. The proposed approach yields better convergence performance than the traditional gradient-descent optimization algorithm and a lower computation time and equivalent performance for the blocklength variable as the exhaustive search. Moreover, the proposed approach allows ultrahigh reliability, which can be attained by increasing the number of antenna elements in RIS as well as increasing the allocated blocklengths. Simulations demonstrate the RIS’s performance gain and conclusively show that the UAV’s position is crucial for achieving ultrahigh reliability in short packet transmission.

Statistical-QoS Guarantee for IoT Network Driven by Laser-Powered UAV Relay and RF Backscatter Communications

Resource optimization is investigated for an unmanned aerial vehicle (UAV)-mounted relay assisted Internet-of-Things (U-IoT) network. A comprehensive network structure is proposed by incorporating laser-driven adaptive wireless-power-transfer at the UAV relay, wirelessly powered backscatter communication in the radio-frequency access links, and modulating retro-reflector based free space optical backhaul link in an optimization framework. Our objective is to maximize the number of the connected IoT devices with the UAV relay for uplink data transmission while satisfying the heterogeneous quality-of-service requirements of the IoT devices. Towards this objective, a novel optimization problem is formulated by considering queueing-overflow probability constraints of the IoT devices with stochastic data arrival, backhaul capacity constraint, and energy causality constraint at the UAV relay. The considered resource optimization is NP-hard, and an iterative solution is proposed by exploiting structure of the optimization problem. Furthermore, a three-stage optimization is devised to solve an NP-complete fractional optimization problem at each iteration of the proposed solution. An algorithm of polynomial computational complexity is developed for joint connectivity maximization and resource allocation, and convergence of the developed algorithm is proved. Using extensive simulations, efficiency of the proposed algorithm is demonstrated for improving the supportable arrival rate per IoT device and the number of the connected IoT devices in uplink of a U-IoT network.

UAV-Aided Multi-Way NOMA Networks With Residual Hardware Impairments

In this letter, we study an unmanned aerial vehicle (UAV)-aided non-orthogonal multiple access (NOMA) multi-way relaying networks (MWRNs). Multiple terrestrial users aim to exchange their mutual information via an amplify-and-forward (AF) UAV relay. Specifically, the realistic assumption of the residual hardware impairments (RHIs) at the transceivers is taken into account. To evaluate the performance of the considered networks, we derive the analytical expressions for the achievable sum-rate (ASR). In addition, we carry out the asymptotic analysis by invoking the affine expansion of the ASR in terms of high signal-to-noise ratio (SNR) slope and high SNR power offset. Numerical results show that: 1) Compared with orthogonal multiple access (OMA), the proposed networks can significantly improve the ASR since it can reduce the time slots from [(M}-1)/2] + 1 to 2; and 2) RHIs of both transmitter and receiver have the same effects on the ASR of the considered networks.

A Multilevel Mobile Fog Computing Offloading Model Based on UAV-Assisted and Heterogeneous Network

The mobile fog computing (MFC) network that integrates unmanned aerial vehicles (UAV) fully exerts its advantages of flexible deployment, load balance, and rapid response. Under complex network environment, proposing a reasonable offloading model and according resource optimization of the MFC network is important to satisfy high-requirement offloading standard. In this paper, a multilevel MFC offloading model where UAV and fog nod undertake relay nodes and offloading computing nodes are established for computation-intensive and latency-critical tasks, considering heterogeneous network selection , dynamic channel quality and central cloud access . With the total system utility optimality function including reward function maximization as the goal, the MDP algorithm is applied to solve the best offloading decision of the computing task and the balanced load mode of the MFC network. Finally, the simulation section verifies the excellent performance of the proposed multilevel MFC offloading model in network resource utilization. Simulation results show that the model can optimize the relative position of service nodes in MFC network and ensure the offloading reliability of terminal equipment.

Secrecy Performance Analysis of UAV Assisted Relay Transmission for Cognitive Network With Energy Harvesting

The unmanned aerial vehicle (UAV) assisted transmission can significantly improve the spectrum efficiency and coverage of the wireless communication network. The paper proposed a cooperative secrecy transmission mechanism, which can take advantage of transmitter signal of primary user (PU) as a dedicated radio frequency (RF) source with decode and forward (DF) UAV selection and energy harvesting (EH) under cognitive network. Specifically, the transmitting signal of PU in the cognitive network can be viewed and used as energy to drive secondary user (SU) nodes and multi-relay nodes to send signals. It was worth noting that the DF UAV relay may bring about the unexpected noise and decrease the received signal to noise ratio (SNR), therefore, the multiple UAV assisted relays selected with secrecy capacity maximization criteria under cognitive network was proposed and investigated in detail, where the destination node equipped with multiple antennas and adopted the optimal antenna selection receiver. Moreover, By using the function analysis method, the closed expressions of the system secrecy outage probability (SOP) and the probability of non-zero secrecy capacity were calculated and derived accurately. Furthermore, the asymptotic expressions of the UAV assisted relay system under the large SNR cases were analyzed in detail, where the expressions of secrecy diversity order and secrecy diversity gain of the UAV assisted relay cognitive system were obtained thoroughly. Simulation results verified the effectiveness of the proposed mechanism and the correctness of the calculation.

Optimal Power and Bandwidth Allocation for Multiuser Video Streaming in UAV Relay Networks

We all have encountered unsatisfactory video viewing experience with mobile devices in hot spot areas where a crowd of people are demanding video streaming simultaneously. One possible solution to relieve the problem is to use the unmanned aerial vehicle (UAV) as a relay to improve the quality-of-experience (QoE). In this paper, we focus on the allocation of bandwidth and transmission power for multiuser video streaming in UAV relay networks. Our goal is to maximize the total long-term QoE of users, which are determined by the freezing time and the bit rate of video. We employ the Lyapunov optimization theory to transform the original problem with long-term average form into the drift-plus-penalty problem, and propose a decentralized allocation scheme using alternative ascending clock auction to derive the optimal solution. Simulations are conducted to demonstrate the superiority of the proposed method.

A cost-efficient elastic UAV relay network construction method with guaranteed QoS

Recently, Unmanned Aerial Vehicle (UAVs) have been widely adopted to collect data in dangerous or inaccessible areas due to their unique characteristics, including on-demand deployment, flexibility, low-cost, and mobility. However, limited power supply severely restricts the transmission range of a UAV, thus the sensed data needs to be transmitted to the ground control station (GCS) with the help of multiple UAVs in a multi-hop manner. This paper firstly describes the general process of the UAV relay network deployment, and analyzes the number of relay UAVs needed by two types of straightforward static model, i.e. track-based dynamic routing model with planned path and the infrastructure-based dynamic routing model with uncertain path. Deploying the relay UAVs in a static way as current works do cannot fulfill the requirements for an ideal UAV relay network, such as high reliability, low-cost and guaranteed quality of service. Thus, we detail our prediction-based dynamic relay network deployment model, and prove that our proposal is feasible and requires less number of relay UAVs while providing higher communication quality compared with state-of-the-art methods. Based on this model, an Elastic Relay Network Construction (ERNetC) algorithm is proposed. Finally, a series of simulation experiments are conducted on OMNeT++ simulation platform to evaluate the performance of ERNetC algorithm. Simulation results show that ERNetC algorithm outperforms existing algorithms in total interruption time as well as goodput.

Joint Transmit Power and Placement Optimization for URLLC-Enabled UAV Relay Systems

This letter considers an unmanned aerial vehicle (UAV)-enabled relay communication system for delivering latency-critical messages with ultra-high reliability, where the relay is operating under amplifier-and-forward (AF) mode. We aim to jointly optimize the UAV location and power to minimize decoding error probability while guaranteeing the latency constraints. Both the free-space channel model and three-dimensional (3-D) channel model are considered. For the first model, we propose a low-complexity iterative algorithm to solve the problem, while globally optimal solution is derived for the case when the signal-to-noise ratio (SNR) is extremely high. For the second model, we also propose a low-complexity iterative algorithm to solve the problem. Simulation results confirm the performance advantages of our proposed algorithms.

Mobility Control of Unmanned Aerial Vehicle as Communication Relay to Optimize Ground-to-Air Uplinks.

In recent years, unmanned aerial vehicles (UAVs) have been considered an ideal relay platform for enhancing the communication between ground agents, because they fly at high altitudes and are easy to deploy with strong adaptabilities. Their maneuvering allows them to adjust their location to optimize the performance of links, which brings out the relay UAV autonomous mobility control problem. This work addressed the problem in a novel scene with mobile agents and completely unknown wireless channel properties, using only online measured information of received signal strength (RSS) and agent positions. The problem is challenging because of the unknown and dynamic radio frequency (RF) environment cause by agents and UAV maneuvering. We present a framework for both end-to-end communication and multi-agent-inter communication applications, and focus on proposing: (1) least square estimation-based channel approximation with consideration of environment effects and, (2) gradient-based optimal relay position seeking. Simulation results show that considering the environmental effects on channel parameters is meaningful and beneficial in using UAV as relays for the communication of multiple ground agents, and validate that the proposed algorithms optimizes the network performance by controlling the heading of the UAV.

Research on multi-UAV communication connectivity maintenance based on dynamic programming

With the continuous development of unmanned aerial vehicle (UAV) technology and its falling cost, UAV has been widely used in important fields, such as agriculture, industry, security, and military. In operation, UAVs often need to maintain communication connectivity with ground stations. However, in the case of long distance or shadowing, the quality of the wireless channels will be seriously degraded, which will affect the communication connectivity. To solve this problem, this paper considers deploying multiple UAVs as communication relays. In this paper, we select the long-term channel capacity as the optimization objective and propose two planning methods for UAV relays based on dynamic programming (DP): CMMP-AT and CMMP-OBO. The CMMP-AT method emphasizes the deployment of all relay UAVs at the beginning of the task. This method is simpler to implement, but the computational complexity is intolerable. The CMMP-OBO method proposes to deploy relay UAVs one by one according to the task requirements. Although the CMMP-OBO method may lose performance, it has a lower computational complexity and better scalability and can save more motion energy. The experimental results reveal that the CMMP-AT method requires 40.03 h to obtain the planning results for a two-relay scenario, whereas the CMMP-OBO method only needs 57.66 s and can save 3.87% motion energy. In addition, to precisely control the relay UAVs to follow the planned trajectory, this paper implements trajectory tracking, based on model predictive control (MPC), and realizes the simulation of the multi-relay UAV scenario in the VREP environment. The simulation results reveal that compared with the PID control method, the MPC method can track the planned trajectory more accurately. In two simulation scenarios, the tracking error of MPC is only 43.97% and 41.42% of the PID control method.

Multilevel Task Offloading and Resource Optimization of Edge Computing Networks Considering UAV Relay and Green Energy

Unmanned aerial vehicle (UAV)-assisted relay mobile edge computing (MEC) network is a prominent concept, where network deployment is flexible and network coverage is wide. In scenarios such as emergency communications and low-cost coverage, optimization of offloading methods and resource utilization are important ways to improve system effectiveness due to limited terminal and UAV energy and hardware equipment. A multilevel edge computing network resource optimization model on the basis of UAV fusion that provides relay forwarding and offload services is established by considering the initial energy state of the UAV, the green energy charging function, and the reliability of computing offload. With normalized system utility function maximization as the goal, a Markov decision process algorithm meets the needs of the practical application scene and provides a flexible and effective unloading mode. This algorithm is adopted to solve the optimal offloading mode and the optimal resource utilization scheme. Simulations verify the effectiveness and reliability of the proposed multilevel offloading model. The proposed model can optimize system resource allocation and effectively improve the utility function and user experience of computing offloading systems.

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.

Placement optimisation method for multi‐UAV relay communication

With the popularisation of unmanned aerial vehicle (UAV) technology, relay communication based on multi-UAVs has advantages and application prospects. However, determining the location of UAV relay nodes is a fundamental problem. In particular, when multiple UAVs are required, it is important to design an efficient node placement method that optimises the layout of UAV nodes to minimise the number of required UAVs and provide reliable connectivity for all ground terminals. In this study, the node placement models for multi-UAV relay communication are constructed based on the non-linear constraint optimisation problem for typical application scenarios, and the solving algorithm of the models is proposed based on the smallest enclosing circle and genetic algorithm. Through the new placement method, the authors can locate UAV relay nodes and optimise the network topology with the minimum number of UAVs required. Finally, the implementation process and results are validated by simulation.

Placement of unmanned aerial vehicles as communication relays in two-tiered multi-agent system: Clustering based methods

The network performance and the unmanned aerial vehicle (UAV) number are important objectives when UAVs are placed as communication relays to enhance the multi-agent information exchange. The problem is a non-deterministic polynomial hard (NP-hard) multi-objective optimization problem, instead of generating a Pareto solution, this work focuses on considering both objectives at the same level so as to achieve a balanced solution between them. Based on the property that agents connected to the same UAV are a cluster, two clustering-based algorithms, M-K-means (MKM) and modified fast search and find density of peaks (MFSFDP) methods, are first proposed. Since the former algorithm requires too much computational time and the latter one requires too many relays, an algorithm for the balanced network performance and relay number (BPN) is proposed by discretizing the area to avoid missing the optimal relay positions and defining a new local density function to reflect the network performance metric. Simulation results demonstrate that the proposed algorithms are feasible and effective. Comparisons between these algorithms show that the BPN algorithm uses fewer relay UAVs than the MFSFDP and classic set-covering based algorithm, and its computational time is far less than the MKM algorithm.

Efficiency Maximization for UAV-Enabled Mobile Relaying Systems With Laser Charging

This work studies the joint problem of power and trajectory optimization in an unmanned aerial vehicle (UAV)-enabled mobile relaying system. In the considered system, in order to provide convenient and sustainable energy supply to the UAV relay, we consider the deployment of a power beacon (PB) which can wirelessly charge the UAV and it is realized by a properly designed laser charging system. To this end, we propose an efficiency (the weighted sum of the energy efficiency during information transmission and wireless power transmission efficiency) maximization problem by optimizing the source/UAV/PB transmit powers along with the UAV's trajectory. This optimization problem is also subject to practical mobility constraints, as well as the information-causality constraint and energy-causality constraint at the UAV. Different from the commonly used alternating optimization (AO) algorithm, two joint design algorithms, namely: the concave-convex procedure (CCCP) and penalty dual decomposition (PDD)-based algorithms, are presented to address the resulting non-convex problem, which features complex objective function with multiple-ratio terms and coupling constraints. These two very different algorithms are both able to achieve a stationary solution of the original efficiency maximization problem. Simulation results validate the effectiveness of the proposed algorithms.

Secure 3D Mobile UAV Relaying for Hybrid Satellite-Terrestrial Networks

In this paper, we propose a novel decode-and-forward (DF)-based secure 3D mobile unmanned aerial vehicle (UAV) relaying for hybrid satellite-terrestrial networks (HSTNs) in the presence of an aerial eavesdropper lying around a serving UAV relay in a circular plane. Herein, we adopt a stochastic mixed mobility (MM) model for mobilizing the UAV relays in a 3D cylindrical cell with a ground user equipment (UE). We consider the deployment of eavesdropper under the cases: (a) the eavesdropper is located at certain fixed distance around a serving UAV relay; (b) the eavesdropper is located uniformly random around the relay. By considering the opportunistic closest-, uniform-, and maximum-signal-to-noise ratio (maximum-SNR) UAV relay selection (URS) strategies, we analyze the secrecy performance of the considered HSTN in terms of probability of non-zero secrecy capacity (PNZSC) and secrecy outage probability (SOP). In particular, we derive exact PNZSC and SOP for the closest-URS (CURS) and uniform-URS (UURS). While, we derive analytical lower and upper bounds on PNZSC and SOP for maximum-SNR URS (MURS). We further carry out the corresponding asymptotic SOP analysis to assess the secrecy diversity order and coding gain of aforementioned URS strategies. Simulations are performed to corroborate the analysis.

Power Consumption Minimization of UAV Relay in NOMA Networks

In this letter, we consider employing a non-orthogonal multiple access (NOMA) unmanned aerial vehicle (UAV) as relay to ferry data from a remote base station to multiple ground users. Our objective is to minimize the power consumption of UAV by jointly optimizing the location and transmission power of UAV. The formulated problem is non-convex with highly coupled binary discrete constraints, which is intractable to solve. Firstly, it is transformed into a more tractable form, where the logarithmic constraints are transformed into polynomial form, and the discrete constraints are transformed into continuous form. Then the non-convex equality constraints are handled by penalty method, while the non-convex inequality constraints are handled by successive convex approximation technique. Based on these measures, we propose a double-loop iterative algorithm to obtain the solution approximately. Numerical results show the effectiveness of our proposed algorithm.