Unmanned Aerial Vehicle Relay Research Articles

Outage performance analysis of antenna selection schemes in UAV-assisted networks

In this paper, we investigate the performance of various antenna selection (AS) techniques in an unmanned aerial vehicle (UAV)-assisted relay communication system, wherein a UAV is deployed as a flying relay to establish and sustain a communication link between source and destination nodes. The AS schemes under study encompass transmit antenna selection/maximal ratio combining (TAS/MRC), joint transmit and receive antenna selection (JTRAS), and maximum ratio transmission/receive antenna selection (MRT/RAS). We present a unified outage probability (OP) performance analysis of AS techniques in UAV relay networks. We initially derive a closed-form expression for the OP under Nakagami-m fading channels. Subsequently, an asymptotic expression elucidating the array and diversity gain is derived to offer deeper insights into the performance of the studied system. Finally, Monte Carlo simulations are conducted to validate the theoretical results.

On performance analysis of non-orthogonal multiple access downlink for cellular-connected unmanned aerial vehicle relaying assisted vehicle-to-everything system

This paper presents the unmanned aerial vehicle (UAV) relays’ assisted vehicle-to-everything (V2X) network to implement the internet of things (IoT) systems with improvement in the coverage area. Such a network benefits from many advantages of the non-orthogonal multiple access (NOMA) scheme. We have implemented a decode-and-forward (DF) scheme for these UAVs. Then, we characterize the channels as Nakagami-m fading to evaluate the performance of the system. We derive closed-form expressions of outage probability (OP), ergodic capacity (EC), and throughput. The results show that the performance of the system depends on the transmitted signal-to-noise ratio (SNR) at the base station and the heights of the UAV relays. Target rate and power allocation factors are two main parameters that can be adjusted to achieve better performance. The results also compare to the system without UAV and OMA technique that shows the advantages of deploying UAV-assisted NOMA. Therefore, the design of NOMA for UAV relay-assisted V2X systems provides sufficient demand. The simulation results verified the effectiveness of the proposed UAV network and the precision of the theoretical analysis.

An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments

The use of unmanned aerial vehicles (UAVs) is increasing in transportation applications due to their high versatility and maneuverability in complex environments. Search and rescue is one of the most challenging applications of UAVs due to the non-homogeneous nature of the environmental and communication landscapes. In particular, mountainous areas pose difficulties due to the loss of connectivity caused by large valleys and the volumes of hazardous weather. In this paper, the connectivity issue in mountainous areas is addressed using a path planning algorithm for UAV relay. The approach is based on two main phases: (1) the detection of areas of interest where the connectivity signal is poor, and (2) an energy-aware and resilient path planning algorithm that maximizes the coverage links. The approach uses a viewshed analysis to identify areas of visibility between the areas of interest and the cell-towers. This allows the construction of a blockage map that prevents the UAV from passing through areas with no coverage, whilst maximizing the coverage area under energy constraints and hazardous weather. The proposed approach is validated under open-access datasets of mountainous zones, and the obtained results confirm the benefits of the proposed approach for communication networks in remote and challenging environments.

On energy conservation for fixed-wing UAV assisted practical full-duplex relaying with service requirement and bank angle limit

This paper studies a full-duplex (FD) amplify-and-forward relaying, where a fixed-wing unmanned aerial vehicle (UAV) is dispatched as a mobile relay to serve a source-destination communication pair so as to satisfy their service requirement. Here, the UAV relay flies in a circular path and hence it must continuously change its heading of flight which leads to a bank angle of the aircraft. In order to ensure flight safety, therefore, a bank angle limit is imposed on the UAV relay. By considering a practical FD relaying, namely, there exists both residual loop interference (RLI) and processing time-delay at the relay side, novel signal to interference plus noise ratio (SINR) received at the destination is analyzed, giving a closed-form expression in addition to a concise lower-bound. Armed with the SINR analysis, the size of data received by the destination during a period of flight time of the UAV is calculated, leading to a closed-form lower-bound of the size of data. Taking bank angle limit into consideration and using the calculated lower-bound of the size of data, an optimization problem with the purpose of energy conservation is solved, leading to a novel method for joint adjustment of the UAV's flight parameters. Computer simulation experiments are conducted, and the results demonstrated that the proposed optimization method performs better in terms of energy conservation compared to the benchmark techniques regardless of the value of RLI and/or the service requirement of the source-destination communication pair.

UAV-assisted fair communications for multi-pair users: A multi-agent deep reinforcement learning method

Unmanned aerial vehicle (UAV) plays an important role in scenarios like search and rescue, remote communication relay, battlefield mobile networks, etc. In this paper, we investigate multiple UAV relays providing real-time transmission for multi-pair ground users (GUs) in the absence of ground-based stations (GBSs). Due to the limited load capacities and energy resources on-board, fairness between multi-pair users, throughput maximization, as well as the connectivity maintenance between UAVs are jointly considered. We formulate the energy-efficient fair throughput objective function, which turns to be non-convex with hybrid variables. To solve this intractable problem, we utilize the power of neural networks in function approximation and propose a multi-agent deep reinforcement learning (MADRL)-based algorithm. Different from traditional MADRL algorithms, we utilize the method, named independent proximal policy optimization (IPPO), which allows agents to update according to their own observations memory and encourages more explorations to some extend, as the base of our solution. Simulation results demonstrate that our algorithm outperforms some baselines in terms of fairness, throughput as well as energy consumption.

Outage and throughput performance analysis of smart UAV‐assisted full‐duplex NOMA‐based energy harvesting cognitive radio networks

SummaryIn this paper, the throughput and outage performance for an unmanned aerial vehicle (UAV)‐assisted non‐orthogonal multiple access (NOMA)‐based hybrid cognitive radio (CR) network are examined under m‐Nakagami fading channel. To be more specific, a secondary source transmitter ( ) broadcasts a NOMA‐enabled mixed signal to secondary destinations via UAV relay, which operates in full‐duplex (FD) mode and employs decode‐and‐forward protocol. In our approach, an energy harvesting (EH) circuit is employed at both and UAV to harvest energy from the PU radio frequency (RF) signals. These secondary transmitters are also incorporated with energy detector (ED) circuit as well, which detects the activity of the primary user (PU). The status of PU to be busy or idle is confirmed on the basis of the energy detector's results. In turn, this drives and UAV to switch between underlay and overlay protocols in an adaptive manner to increase spectrum efficiency (SE). The closed form mathematical expressions of the secondary outage probability for NOMA‐based UAV equipped CR networks under imperfect successive interference cancellation (i‐SIC) conditions have been evaluated. The administration of the power allocation policy at the and UAV is assessed while maintaining the PU quality of service (QoS). Finally, MATLAB simulation testbed has been used to verify all the analytical closed form expressions.

UAV Relay Energy Consumption Minimization in an MEC-Assisted Marine Data Collection System

Recently, unmanned aerial vehicle (UAV)-assisted maritime communication systems have drawn considerable attention due to their potential for broadband maritime communication applications. However, their limited energy resources remains a critical issue in providing long-term data transmission support for maritime applications. In this study, an integrated sea–air–terrestrial communication system was constructed for marine data collection, where several unmanned surface vessels (USVs) are deployed to collect marine data from underwater sensors (UWSs), and a UAV hovers above these USVs as a relay node, transmitting marine data from USVs to an onshore base station (BS). To prolong the lifetime of the UAV relay, mobile edge computing technology is applied in USVs for partial data computing, which reduces the to-be-relayed data volume from UAVs to USVs to onshore BS as well as the relay energy consumption of UAV. A parallel data computing and transmission scheme was developed for simultaneous local data computing and relaying in the proposed system. Accordingly, a UAV energy consumption minimization problem was formulated with constraints on the USV’s computational ability, the USV’s transmission power budget, the UAV transmission power budget, and the maximum system latency. To effectively solve this nonconvex optimal problem, an energy optimal partial data computing and relaying strategy was constructed by successively optimizing the data partial computational offloading ratio, USV transmit power allocation, and UAV transmit power. Numerical simulations were used to verify the effectiveness of the proposed strategy.

Energy Minimization for In-Band Full-Duplex Relaying Using an Amplify-and-Forward UAV Relay

This paper investigates an in-band full-duplex (IBFD) relaying, where a fixed-wing unmanned aerial vehicle (UAV) is dispatched as an amplify-and-forward relay to forward data from a source to a destination. By considering ideal and practical IBFD cases, new received signal-to-noise ratio (SNR) expressions at the destination are derived. Armed with the newly derived SNR expressions and the existing one in literature, three different lower-bounds of the size of data received by the destination are calculated. Using the three lower-bounds of the size of data, an optimization problem corresponding to energy minimization of the IBFD relaying system subject to a total data-bit constraint in addition to a mission completion time (MCT) constraint is solved, leading to three sets of solutions. Computer simulation experiments are conducted and the accuracy of the derived SNR expressions and the three sets of solutions of the optimization problem are compared. The results demonstrated that the newly derived SNR expressions are more accurate than the existing one in literature and are more suitable to be utilized for energy minimization, and the proposed optimization method can complete the relaying task with the minimum energy and MCT. When the residual loop-interference channel power is equal to -100dB, the scheme of ideal IBFD only consumes up to 2.5% of the energy of the compared schemes regardless of the value of the total data-bit constraint.

Development and Analysis of Outage Probability and Achievable Rate for UAV Relay Implementation in Disaster Areas

Unmanned aerial vehicles (UAV) coupled with wireless networks have offered the potential for forthcoming mobile communications networks. The UAV-assisted wireless communication technology approach is an option for maintaining communication services, one of which is for disaster areas. This study aims to investigate the deployment of UAV-assisted communications in disaster areas. The simulation in this work is focused on investigating the relationship between the UAV’s height or radius and the outage probability and achievable rate. We identifies a UAV that behaves as a relay in a disaster area connected to two users, identified several critical parameters, such as UAV height, radius, and SNR value, and investigated the impact of changing each of them on outage probability and achievable rate values. Finally, we simulates the effect of these critical parameters on outage probability and achievable rate to maximize the use of UAV-assisted communication as a disaster management solution. According to the simulation results, as the signal to noise (SNR) increases, the probability of network communication interference decreases, and the achievable level increases. Moreover, increasing the range of UAVs can improve communication performance by reducing the probability of outages and increasing the achievable rate.

Toward Optimal Deployment of UAV Relays in UAV-Assisted Internet of Vehicles

The past decade has witnessed an explosive growth of unmanned aerial vehicles (UAVs), which have attracted great attention in assisting Internet of Vehicles (IoV) communication systems thanks to the advantages of cost-effectiveness, high maneuverability, and flexible deployment. Yet several issues, such as UAVs' transmit power and energy resources as well as the high-speed mobility of IoV user equipment (VUE), pose challenges to the development of UAVs in UAV-assisted IoV networks. To provide reliable and high-throughput wireless links for VUEs in a given ground area, this paper considers a scenario in which a ground VUE is facing difficulties in receiving service from a ground roadside unit (RSU) owing to possible sudden RSU malfunction or poor signal coverage. In this case, UAVs are generally deployed in the air, serving as relays to establish communication links between uncovered VUE and the neighboring RSU. To maximize the system capacity, we investigate the deployment problem of UAV relays under single-UAV relaying and multi-UAV relaying cases upon considering VUE mobility, and exploit the optimal deployment strategy of UAV relays under fixed total power consumption. Comprehensive simulation results verify that the proposed algorithm can outperform benchmark methods on significant critical indicators such as the average achievable user rate, and the outage probability. Also, evaluations and analyses have highlighted the effects of key system parameters (e.g., altitude, number, and transmit power of UAV relays) on optimal UAV relay deployment position and system performance.

A Distributed Cooperative Continuous Coverage Strategy of UAVs for MEC Services of Smart Grids Considering Energy Consumption

<p>Unmanned aerial vehicles (UAVs) play an essential role in scenarios such as smart grid data collection and power line inspection for their maneuverability and low cost. However, the most obvious defect of UAVs is still the energy problem. The battery life of UAVs is too short of completing long-term tasks. Therefore, this paper proposes a distributed cooperative and continuous coverage strategy for UAVs. When the UAV is short of power, we select the path through the relay algorithm and send backhaul information such as the remaining power to the macro base station (mBS). mBS will send auxiliary UAVs to replace UAVs with weak power to complete the task of UAV replacement. We jointly optimize the relay information transmission path and UAV trajectory to achieve the goal of minimizing the total energy consumption of the system. The simulation results show that the optimization algorithm can significantly reduce the energy consumption of UAVs.</p> <p> </p>

Resource Allocation of Multiple Base Stations for Throughput Enhancement in UAV Relay Networks

An unmanned aerial vehicle (UAV), with the advantages of mobility and easy deployment, serves as a relay node in wireless networks, which are known as UAV relay networks (URNs), to support user equipment that is out of service range (Uo) or does not have a direct communication link from/to the base station (BS) due to severe blockage. Furthermore, URNs have become crucial for delivering temporary communication services in emergency states or in disaster areas where the infrastructure is destroyed. The literature has explored single transmissions from one BS to a UAV to establish a wireless backhaul link in the URN; however, there exists a possibility of Uo outages due to severe interference from an adjacent BS, causing an overall throughput degradation of user equipment (UE) in the URN. In this paper, to improve the signal-to-interference-plus-noise ratio (SINR) of a wireless backhaul link, avoid an outage of Uo, and guarantee a reliable relay transmission, simultaneous transmissions from multiple BSs (e.g., macrocell BSs (mBSs) and small cell BSs (sBSs)) is considered. An outage probability is analyzed, and an optimal transmit time allocation algorithm is proposed to maximize the throughput of the UE and guarantee a reliable relay transmission. Simulation results demonstrate that simultaneous transmissions from multiple BSs in the URN leads to higher throughput and reliable transmission without an Uo outage compared to a single transmission in the URN from a single BS (e.g., mBS or sBS), and the optimization of transmit time allocation is essential in the URN.

Energy efficiency maximization for UAV-enabled amplify-and-forward relaying via joint power and trajectory optimization

This paper investigates an optimization problem corresponding to energy efficiency maximization of an unmanned aerial vehicle (UAV)-enabled relaying system, where a fixed-wing UAV acts as an amplify-and-forward mobile relay to assist data transmission between a source node and a destination node. On the premise of satisfying the speed and acceleration constraints of the UAV, the energy efficiency (EE) of the relaying system is maximized by jointly optimizing the UAV’s trajectory and the individual transmit power levels of the source and the UAV relay. The initial joint optimization problem is non-convex and cannot be solved directly. Therefore, the joint optimization problem is decomposed into two sub-problems which are solved by applying the successive convex approximation technique and the Dinkelbach’s algorithm. On this basis, an efficient iterative algorithm is proposed to tackle the joint optimization problem through the block coordinate descent technique. Simulation results demonstrated that by conducting the proposed algorithm, the flight trajectory of the UAV and the individual transmit power levels of the nodes can be flexibly adjusted according to the system conditions, and the proposed algorithm contributes to the higher EE compared with the benchmark schemes.

Performance study of an enhanced fully optical generalized spatial modulation free-space opticalsystem based on serial unmanned aerial vehiclerelay.

This paper proposes an enhanced fully optical generalized spatial modulation (EFOGSM) free-space optical (FSO) system based on serial unmanned aerial vehicle (UAV) relaying under M distribution. The relay transmission mode of the system is decode-and-forward (DF), and two spatial diversity receiving schemes were considered at the receiver of the system. In the atmospheric channel modeling, the angle of arrival (AoA) jitter was introduced to represent the effect of UAV random jitter in the air on the channel, together with three important factors: path loss, pointing error, and atmospheric turbulence. Under the joint influence of the four fading factors, the probability density function expression of the joint fading channel under the new M distribution was derived, and the upper bound of the average bit error rate of the serial UAV-relaying EFOGSM FSO system was derived. The influence of key factors, such as the number of UAV relay nodes, the position distance of UAV, the field of view parameters of the receiver, the direction deviation, and the data transmission rate on the system performance, was analyzed by simulation under weak and strong turbulence. Monte Carlo simulation verified the correctness of the numerical calculation and simulation. This study provides a good theoretical basis for the engineering implementation of a serial UAV relay EFOGSM FSO system.

Two-way UAV-aided systems with short packet communications

In this paper, we investigate a two-way unmanned aerial vehicle (UAV)-aided system, where two sources communicate with each other with the aid of an UAV relay. Two downlink transmission schemes consisting of non-orthogonal multiple access (NOMA) and network coding (NC) have been studied under finite blocklength regime. Considering imperfect successive interference cancellation (SIC), we have derived the exact and approximate expressions of the systems’ average block-error rates (BLERs). In particular, we propose a solution to maximize system throughput by optimizing packet length. In addition, we have also quantified the effects of the system parameters on the BLER performance, including the transmit power, the power allocation coefficients, and the UAV’s altitude. Numerical results demonstrate that the performance gap between the NOMA-based and NC-based systems is negligible.

Secrecy Rate Maximization by Cooperative Jamming for UAV-Enabled Relay System With Mobile Nodes

In recent years, Unmanned Aerial Vehicles (UAVs) have been widely used in wireless communications due to their low cost, small size, flexible deployment and mobile controllability. However, because of the line-of-sight (LoS) communication links, the security threat is always a challenging problem to deal with. In particular, information stolen and leakage may happen in the presence of eavesdroppers. This paper proposes a UAV-enabled system with a relay UAV and a jammer UAV, and certain mobile source and destination nodes in the presence of an eavesdropper to solve the secrecy rate maximization problem. In this system, the relay UAV transmits information between pairs of moving source nodes and moving destination nodes with interrupted communication channels due to blockage or long distance, and the jammer UAV interferes with eavesdropper to reduce the milked information through sending jamming signals. We establish an average secrecy rate maximization problem with trajectory and transmit power optimization under certain constraints for this system. Since this problem is non-convex and reformulated as the Markov decision process (MDP), we use deep reinforcement learning (DRL) method to solve it. In this article, we adopt a proximal policy optimization (PPO) algorithm to find an optimal solution because it can deal with the model of continuous action space. According to our defined states, rewards and actions in this specified MDP, this algorithm can autonomously learn to optimize the trajectory and power allocation of the UAVs to realize our goal. Simulation results demonstrate that the proposed PPO-based average secrecy rate maximization algorithm is valid, effective and scalable.

Trajectory Optimization for FSO Based U-IoT Backhaul Networks

Considering many advancements in aerial platform technology, aerial backhaul networks (ABNs) are expected to play a big role in 6th generation (6G) mobile systems. Considering the significant demand in capacity of Internet of Things (IoT) networks, ABNs using free space optical (FSO) signals are receiving attention as an alternative to using traditional radio frequency (RF) signals. However, FSO based ABNs face the challenge of needing to avoid obstacles (e.g., clouds) in the vertical channel to maintain line-of-sight (LoS) between the ground terminals (GTs) and the unmanned aerial vehicle (UAV) based high-altitude platform stations (HAPSs). Therefore, in this paper, a trajectory optimized FSO based UAV-IoT (U-IoT) backhaul network (TOFU) scheme that minimizes the end-to-end outage probability is proposed. Considering moving clouds and obstacles, the TOFU scheme identifies the changing LoS area and applies gradient descent based on optimized control of the HAP and relay UAVs to minimize the end-to-end outage probability. Simulation experiments were conducted to include various cloud properties and orientations, where the results show that the TOFU scheme can provide significant improvements in the end-to-end outage probability and throughput compared to other benchmarked schemes.

UAV relay network deployment through the area with barriers

Flying ad hoc networks composed of Unmanned Aerial Vehicles (UAVs) are flexible, inexpensive, and fast to deploy. This makes them a very attractive technology for many civilian and military applications. Due to the high mobility of the nodes, maintaining a multi-hop communication link between the UAVs is a challenging task. The existing network deployment method designed for relay network may fail to guarantee the connectivity due to the limited wireless transmission and dynamic topology. Designing and implementing a cost-effective UAV relay network is still a problem when there are barriers between the source node and the ground control station. In this paper, we first build a general UAV relay network model; then we propose a UAV deployment algorithm based on Q-learning to construct a UAV relay network with the minimum number of nodes, and we use an Equidistant Node Control algorithm to construct a dynamic relay path. We conduct a series of experiments on the OMNeT++ simulation platform to evaluate the performance of our proposed algorithm. The experimental results show that our UAV relay network has the lower cost and reliable communication performance.

Mode Adaptive Secure UAV Relay Transmissions

Unmanned aerial vehicles (UAV) can enhance wireless transmission security by improving the secrecy rate through relaying the legitimate transmission and injecting artificial noise to the eavesdroppers. However, due to the inherent mechanical energy consumption and the long-range signal propagation pathloss, the traditional cruising and hovering UAV relay modes are deficient in energy utilization and secrecy rate, respectively. Modeling of the secrecy energy efficiency with respect to UAV mode adaptation is limited in the literature and deserves further investigation. Therefore, in this paper, we propose a secrecy rate-energy efficient UAV relay mode adaptation scheme for secure wireless communications scenarios where the eavesdropping locations are known, i.e., the eavesdropper is a hijacked internal legitimate node. In this secure system, the UAV cooperatively assists the legitimate transmissions and injects artificial interference to the eavesdropper. We investigate optimum UAV placement and relay period planning for the hovering and the cruising relay modes, respectively. By modeling the UAV modes transfer and maximizing the secrecy energy efficiency, the optimum mode adaptation ratios are obtained under both the secrecy rate-prioritized and the energy-saving criteria. In addition, the proposed UAV relay mode adaptation mechanism is further studied for each UAV task division, to adapt to the dynamic channel variations and enhance the security. Our theoretical analysis shows that the mode adaptation ratio is determined by the secrecy rate-energy gain between the cruising and the hovering modes, and by the energy consumption threshold of the UAV relays. Our numerical results demonstrate that the proposed scheme achieves higher secrecy energy efficiency than the traditional schemes.

Energy-Efficient Fixed-Wing UAV Relay With Considerations of Airframe Shadowing

Owing to their high energy efficiency, fixed-wing unmanned aerial vehicles (UAVs) can operate as aerial relays to provide long periods of uninterrupted connectivity. In such systems, however, airframe shadowing may occur (i.e. the blockage of a UAV communication link by the UAV’s own airframe) and can significantly degrade the communications performance. In this work, we propose a novel approach to optimize the trajectory of a fixed-wing UAV operating as a relay between a ground user (GU) and a ground base station (BS) to maximize the relay efficiency (defined as the ratio of the amount of relayed data to the consumed energy). We use the aerodynamic equations of the fixed-wing UAV to derive a semi-deterministic airframe shadowing model for loitering trajectories. Analytical expressions and simulation results illustrate the dependence of the airframe shadowing on the UAV’s attitude and speed.