Unmanned Aerial Vehicle Relay Research Articles

UAV-Assisted Free Space Optical Communication System With Amplify-and-Forward Relaying

In this paper, the performance of an unmanned aerial vehicle (UAV)-based free-space optical relay system with amplify-and-forward relaying is analyzed. In particular, the statistical probability of end-to-end signal-to-noise ratio at the destination is obtained under a general condition that takes into account the effects of nonzero boresight pointing errors along with the effects of orientation and position fluctuations of the UAV relay, the UAV position with respect to the source and destination nodes, the optical beamwidth, turbulence strength of both source-to-relay and the relay-to-destination links. Subsequently, the closed-form expressions are derived for the bit-error rate and outage probability of the considered system. The accuracy of the analytical expressions is verified by performing extensive simulations. Analytical results are then utilized to study of the relationship between the optimal system parameters design and UAV's position in the sky. The results show that the optimal UAV's position that achieves a minimum outage probability highly depends on the optical beamwidth at the destination. Moreover, to achieve a minimum outage probability, the relay-to-destination link length must be shorter than source-to-destination (SR) link and by deceasing beamwidth at the destination, the optimal value for SR link length increases.

Outage Performance of 3D Mobile UAV Caching for Hybrid Satellite-Terrestrial Networks

In this paper, we consider a hybrid satellite-terrestrial network (HSTN) where a multiantenna satellite communicates with a ground user equipment (UE) with the help of multiple cache-enabled amplify-and-forward (AF) three-dimensional ($3$D) mobile unmanned aerial vehicle (UAV) relays. Herein, we employ the two fundamental most popular content (MPC) and uniform content (UC) caching schemes for two types of mobile UAV relays, namely fully $3$D and fixed height. Taking into account the multiantenna satellite links and the random $3$D distances between UAV relays and UE, we analyze the outage probability (OP) of considered system with MPC and UC caching schemes. We further carry out the corresponding asymptotic OP analysis to present the insights on achievable performance gains of two schemes for both types of $3$D mobile UAV relaying. Specifically, we show the following: (a) MPC caching dominates the UC and no caching schemes; (b) fully $3$D mobile UAV relaying outperforms its fixed height counterpart. We finally corroborate the theoretic analysis by simulations.

On Throughput for UAV Relay Assisted for Use in Disaster Communications


 
 
 In this paper, the system performance of an energy harvesting (EH) unmanned aerial vehicle (UAV) system for use in disasters was investigated. The communication protocol was divided into two phases. In the first phase, a UAV relay (UR) harvested energy from a power beacon (PB). In the second phase, a base station (BS) transmitted the signal to the UR using non-orthogonal multiple access (NOMA); then, the UR used its harvested energy from the first phase to transfer the signal to two sensor clusters, i.e., low-priority and high-priority clusters, via the decode-and-forward (DF) technique. A closed-form expression for the throughput of the cluster heads of these clusters was derived to analyze the system performance. Monte Carlo simulations were employed to verify our approach.
 
 

Establishment of UAV Path Planning Model Based on Ant Colony and Simulated Annealing Algorithm

This paper is about the optimal allocation, optimal path planning and optmization of two kinds of UAV (SSA UAV and Radio Relay UAV). Describes the planning and allocation of unmanned aerial vehicles (UAVs) for wildfire rescue and reconnaissance operations in Australia. First of all, we build model using analytic hierarchy process (AHP) and simulated annealing algorithm to determine the constraint conditions, considering safety, capacity, economy, Victoria region of the terrain and the size of the fire, frequency and other factors, through the branch and bound method of integer to get optimal allocation of unmanned aerial vehicle (UAV) is out of the optimal route. Secondly, we use ant colony algorithm to optimize the three-dimensional path of the UAV and plan it. Finally, we use MATLAB to consistency check, to get a comparison matrix, and then the weights for each survey and plan the best path, so as to determine the optimal number of drones and combination, and analyzes the error of the model, to ensure the accuracy of the models.

Joint 3D trajectory and resource optimization for a UAV relay-assisted cognitive radio network

In this paper, we consider a new spectrum sharing scenario for a cognitive relay network, where a secondary unmanned aerial vehicle (UAV) relay receives information from the ground secondary base station (SBS) and transmits information to the ground secondary user (SU), coexisting with the primary users (PUs) at the same wireless frequency band. We investigate the optimization of the UAV relay's three-dimensional (3D) trajectory to improve the communication throughput performance of the secondary network subject to the interference constraints of the PUs. The information throughput maximization problem is studied by jointly optimizing the UAV relay's 3D trajectory and the transmit power of the SBS and the UAV, subject to the constraints on the velocity and elevation of the UAV relay, the maximum and average transmit power, and the information causality, as well as a set of interference temperature (IT) constraints. An efficient algorithm is proposed to solve the admittedly challenging non-convex problem by using the path discretization technique, the successive convex approximation technique and the alternating optimization method. Finally, simulation results are provided to show that our proposed design outperforms other benchmark schemes in terms of the throughput.

Distributed reinforcement learning based framework for energy-efficient UAV relay against jamming

Unmanned aerial vehicle (UAV) network is vulnerable to jamming attacks, which may cause severe damage like communication outages. Due to the energy constraint, the source UAV cannot blindly enlarge the transmit power, along with the complex network topology with high mobility, which makes the destination UAV unable to evade the jammer by flying at will. To maintain communication with a limited battery capacity in the UAV networks in the presence of a greedy jammer, in this paper, we propose a distributed reinforcement learning (RL) based energy-efficient framework for the UAV networks with constrained energy under jamming attacks to improve the communication quality while minimizing the total energy consumption of the network. This framework enables each relay UAV to independently select its transmit power based on historical state-related information without knowing the moving trajectory of other UAVs as well as the jammer. The location and battery level of each UAV need not be shared with other UAVs. We also propose a deep RL based anti-jamming relay approach for UAVs with portable computation equipment like Raspberry Pi to achieve higher and faster performance. We study the Nash equilibrium (NE) and the performance bounds based on the formulated power control game. Simulation results show that the proposed schemes can reduce the bit error rate (BER) and reduce energy consumption of the UAV network compared with the benchmark method.

Trajectory Optimization and Resource Allocation for OFDMA UAV Relay Networks

In this paper, we consider a single-cell multi-user orthogonal frequency division multiple access (OFDMA) network with one unmanned aerial vehicle (UAV), which works as an amplify-and-forward relay to improve the quality-of-service (QoS) of the user equipments (UEs) in the cell edge. Aiming to improve the throughput while guaranteeing the user fairness, we jointly optimize the communication mode, subchannel allocation, power allocation, and UAV trajectory, which is an NP-hard problem. To design the UAV trajectory and resource allocation efficiently, we first decompose the problem into three subproblems, i.e., mode selection and subchannel allocation, trajectory optimization, and power allocation, and then solve these subproblems iteratively. Simulation results show that the proposed algorithm outperforms the random algorithm and the cellular scheme.

MIMO Relaying UAVs Operating in Public Safety Scenarios

Methods to implement communication in natural and humanmade disasters have been widely discussed in the scientific community. Scientists believe that unmanned aerial vehicles (UAVs) relays will play a critical role in 5G public safety communications (PSC) due to their technical superiority. They have several significant advantages: a high degree of mobility, flexibility, exceptional line of sight, and real-time adaptative planning. For instance, cell edge coverage could be extended using relay UAVs. This paper summarizes the sidelink evolution in the 3GPP standardization associated with the usage of the device to device (D2D) techniques that use long term evolution (LTE) communication systems, potential extensions for 5G, and a study on the impact of circular mobility on relay UAVs using the software network simulator 3 (NS3). In this simulation, the transmitted packet percentage was evaluated where the speed of the UAV for users was changed. This paper also examines the multi-input multi-output (MIMO) communication applied to drones and proposes a new trajectory to assist users experiencing unfortunate circumstances. The overall communication is highly dependent on the drone speed and the use of MIMO and suitable antennas may influence overall transmission between users and the UAVs relay. When the UAVs relaying speed was configured at 108 km/h the total transmission rate was reduced to 55% in the group with 6 users allocated to each drone.

Joint trajectory and power optimization for mobile jammer-aided secure UAV relay network

This paper investigates the achievable average secrecy rate for a mobile jammer-aided secure unmanned aerial vehicle (UAV) relaying system. Specifically, one UAV is deployed as relay to support the communications between a source node and a destination node. An additional cooperative UAV is dispatched to enhance security by transmitting an the interference signal to interfere with any suspicious eavesdropping. With the objective of enhancing secrecy against eavesdropping, an optimization problem for jointly optimizing the time scheduling, transmit power, and UAV's trajectory is formulated while considering the energy-constrained condition of rotary-wing UAV. As the corresponding optimization problem is mixed-integer non-convex, we propose an iterative algorithm to efficiently solve this optimization problem by applying successive convex optimization and block coordinate descent techniques to derive a locally optimal solution. Simulation results demonstrate the effectiveness of the proposed algorithm compared with benchmark algorithms.

3D Urban UAV Relay Placement: Linear Complexity Algorithm and Analysis

Optimal unmanned aerial vehicle (UAV) placement in a 3-dimensional (3D) space to build a connection between a base station (BS) and a ground user is studied herein. A key challenge is to avoid signal propagation blockage due to obstacles. Much prior work uses probabilistic terrain models with model parameters learned from the statistics over a large area, and therefore, the optimization for a specific user in a small local area is poor. In contrast, this paper seeks the optimal UAV position over actual and fine-grained terrain, and develops efficient UAV positioning strategy adaptive to the degree of location-dependent line-of-sight (LOS) condition measured on the fly. It is proven that the globally optimal UAV position in 3D can be determined from the proposed search trajectory which has merely linear length in the diameter of the target area. Therefore, the proposed strategy can be practically implemented. Numerical experiments are performed over a real-world urban topology and demonstrate superior performance gain over existing strategies based on probabilistic models.

The role of unmanned aerial vehicles and mmWave in 5G: Recent advances and challenges

AbstractNext‐generation wireless communication networks, in particular, the densified 5G will bring many developments to the existing telecommunications industry. The key benefits will be the higher throughput and very low latency. In this context, the usage of unmanned aerial vehicle (UAV) is becoming a feasible option for deploying 5G services on demand. At the same time, the immense bandwidth potential of mmWave has strengthened its performance in radio communication. In this article, we provide a consolidated synthesis on the role of UAVs and mmWave in 5G, emphasis on recent developments and challenges. The review focuses on UAV relay architectures, identifies the relevant problems and limitations in the deployment of UAVs using mmWave in both access and backhaul links simultaneously. There is a critical analysis of the optimum placement of the UAVs as a relay with a focus on the mmWave band. The distinctive rich characteristics of the mmWave propagation and scattering are presented. We also synthesis mmWave path loss models. Then, the scope of artificial intelligence and machine learning techniques as an efficient solution for combating the dynamic and complex nature of UAV‐based cellular communication networks are discussed. In the end, security and privacy issues in UAV‐based cellular network are spotlighted. It is believed that the literature discussed, and the findings reached in this article are of significant importance to researchers, application engineers and decision‐makers in the designing and deployment of UAV‐supported 5G network.

Employing non-orthogonal multiple access scheme in UAV-based wireless networks

This paper studies the two-hop transmission relying unmanned aerial vehicle (UAV) relays which is suitable to implement in the internet of things (IoT) systems. To enhance system performance in order to overcome the large scale fading between the base station (BS) and destination as well as achieve the higher spectrum efficiency, where non-orthogonal multiple access (NOMA) strategies were typically applied for UAV relays to implement massive connections transmission. In particular, outage probability is evaluated via signal to noise ratio (SNR) criterion so that the terminal node can obtain reasonable performance. The derivations and analysis results showed that the considered fixed power allocation scheme provides performance gap among two signals at destination.The numerical simulation confirmed the exactness of derived expressions in the UAV assisted system.

Spectrum-Sharing UAV-Assisted Mission-Critical Communication: Learning-Aided Real-Time Optimisation

We propose an unmanned aerial vehicle (UAV) communications scheme with spectrum-sharing mechanism to provide mission-critical services such as disaster recovery and public safety. Specifically, the UAVs can serve as flying base stations to provide extended network coverage for the affected area under spectrum-sharing cognitive radio networks (CRNs). To cope with the effects of network destruction in a disaster, we propose a real-time optimisation framework for resource allocation (e.g., power and number of UAVs) for CRNs assisted by UAV relays. The proposed optimisation scheme aims at optimising the network throughput of primary and secondary networks under the stringent constraint of maximum tolerable interference impinged on the primary users. We also propose a deep neural network (DNN) model to significantly reduce the execution time under real-time solution of mixed-integer UAV deployment problems. For both primary and secondary networks, our real-time optimisation algorithms impose low computational complexity, hence, have a low execution time in solving throughput optimisation problems, which demonstrates the benefit of our approached proposed for spectrum-sharing UAV-assisted mission-critical services.

UAV‐Relaying Cooperation for Internet of Everything with CRT‐Based NOMA

Due to the great potential of the combination of machine learning technology and unmanned aerial vehicle (UAV) enabled wireless communications, various optimization algorithms on resource allocation have been proposed for the Internet of Things. UAVs not only can perform the missions under the extreme conditions but also enhance the overall performance of the system as an aerial relay assisting transmission in the public and civil domains, which have been received extensive attentions. However, with the limited capacity and power constraints, they are difficult to support the transmission for the big data information users. In addition, the lack of spectrum resource poses challenges to satisfy the quality of service (QoS) of mobile users in wireless networks. To contribute to these urgent problems, this article first studies the potential and effective applications of UAVs, by introducing the Chinese remainder theorem (CRT) and nonorthogonal multiple access (NOMA) technologies into UAV relay networks. Two scenarios with/without direct transmissions between the source and destination nodes are investigated, following the decomposition and reconstruction mechanisms to satisfy the big data information transmission. Considering the user fairness, we further discuss the effect of the UAV numbers to the overall system capacity. To maximize the system capacity, the designs of transmission protocol and receiver are also discussed, in various channel conditions. Finally, a low complexity and efficient two‐stage power allocation scheme is established for the perspective of users and UAV relays.

Wireless-Powered Full-Duplex UAV Relay Networks Over FTR Channels

A thorough understanding of fundamental limits of wireless-powered unmanned aerial vehicle (UAV) relay networks in millimeter waves is still missing. We narrow this gap by investigating the outage performance of a UAV-assisted wireless network over fluctuating two-ray (FTR) channels. The FTR fading model is particularly appealing since well characterizes the wireless propagation in a wide range of frequencies, including those in millimeter waves. The proposed setup consists of a source-destination pair communicating with the assistance of a UAV, which is a wireless-powered relay station operating in full-duplex mode under the amplify-and-forward protocol. For the wireless energy harvesting at the UAV, wireless power transfer (WPT), simultaneous wireless information and power transfer (SWIPT), and self-recycling energy techniques are employed together. To characterize the system outage probability, we obtain an integral-form expression derived from an approximate analysis and a simple closed-form expression derived from an asymptotic analysis at the high signal-to-noise ratio (SNR) regime. Monte Carlo simulations are provided to validate the correctness of our theoretical results and provide insights on the network performance in terms of key system parameters. Interestingly, obtained results show that the FTR fading parameters corresponding to the first hop and second hop play no role on the system outage performance at high SNR. Instead, it is mainly governed by the effect of the residual self-interference at the UAV, leading to outage floors.

3D Trajectory and Transmit Power Optimization for UAV-Enabled Multi-Link Relaying Systems

Unmanned aerial vehicles (UAVs) have the advantages of high mobility, remarkable versatility, and on-demand deployment, which lead to their wide applications in various industries. In disasters or other emergency events, UAV-enabled relaying can provide a temporary wireless connection for the affected area. Thus, a UAV-enabled multi-link relaying system is considered in this article, where multiple sources communicate with their destinations via multiple UAV relays at the same time sharing the same spectrum. Considering the interference between any two source-UAV relay-destination links, we propose to maximize the minimum throughput of all links by jointly optimizing the UAV relays’ three-dimension (3D) trajectories and the sources and UAV relays’ transmit power levels. As the considered problem is non-convex, we propose an iterative algorithm to solve it by applying the block coordinate ascent technique, which optimizes the UAV trajectories and the transmit power alternately until reaching convergence. Numerical results show that the proposed algorithm significantly outperforms the benchmark algorithms in terms of all links’ minimum throughput, and demonstrate the necessity of jointly optimizing the 3D UAV trajectories and transmit power for interference mitigation in a UAV-enabled multi-link relaying system.

UAV-Aided Two-Way Multi-User Relaying

Unmanned aerial vehicle (UAV)-aided two-way relaying networks are designed, where a UAV is deployed to assist multiple pairs of users in their information exchange. There are two basic approaches for the user pairs’ information exchange within a single time slot via the UAV relay. The first approach is based on full-duplex, where all participants operate in the full-duplex mode to transmit and receive signals simultaneously. However, all transceivers have to operate in the face of severe self-interference, which cannot be completely suppressed. The second approach is based on conventional half-duplex, where the users send their information to the UAV within a certain fraction of the time slot, and the UAV relays them within the remaining fraction to avoid the self-interference. In either approach, the joint bandwidth and power allocation maximizing the sum information exchange throughput under realistic resource and user throughput constraints poses a complex nonconvex problem. New inner approximations are proposed for developing path-following algorithms for their computation. Our numerical results show that the time-fraction-based half-duplex approach clearly outperforms the high-complexity full-duplex approach.

Throughput Maximization of Mixed FSO/RF UAV-Aided Mobile Relaying With a Buffer

In this paper, we investigate a mobile relaying system assisted by an unmanned aerial vehicle (UAV) with a finite size of the buffer. Under the buffer size limit and delay constraints at the UAV relay, we consider a dual-hop mixed free-space optical/radio frequency (FSO/RF) relaying system (i.e., the source-to-relay and relay-to-destination links employ FSO and RF links, respectively). Taking an imbalance in the transmission rate between RF and FSO links into consideration, we address the trajectory design of the UAV relay node to obtain the maximum data throughput at the ground user terminal. Specifically, we classify two relaying transmission schemes according to the delay requirements, i.e., i) delay-limited transmission and ii) delay-tolerant transmission. Accordingly, we propose an iterative algorithm to effectively obtain the locally optimal solution to our throughput optimization problems and further present the complexity analysis of this algorithm. Through this algorithm, we present the resulting trajectories over the atmospheric condition, the buffer size, and the delay requirement. In addition, we show the optimum buffer size and the throughput-delay tradeoff for a given system. The numerical results validate that the proposed buffer-aided and delay-considered mobile relaying scheme obtains 223.33% throughput gain compared to the conventional static relaying scheme.

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.