Unmanned Aerial Vehicle Communication Research Articles

Energy aware solution for IRS-aided UAV communication in 6G wireless networks

The rapid evolution of unmanned aerial vehicles (UAVs) is emerging as a promising technology for wireless network applications such as remote sensing, scalable network coverage, security surveillance, real-time monitoring, rescue, and emergency communications. Firstly, this article investigates the integration of intelligent reflecting surface (IRS) technology with UAVs for next generation intelligent networks. Furthermore, a framework is proposed for the integration of IRS technology with UAV-aided communication to meet the requirements of sustainable wireless networks. The proposed system aims to achieve maximum energy efficiency for a given power level and phase shift using a power consumption model. Taking into consideration the transmit power and the power consumed by the circuit components at the base station (BS), user equipment (UE), IRS and UAV, the energy efficiency of the proposed system is evaluated for different IRS reflecting elements and different UAV locations. It is shown that a UAV equipped with 150 reflecting elements improves energy efficiency by 7.47% with 100 co-located antennas at the BS. Also, the achieved energy efficiency is maximum when the UAV is close to the BS or the communicating user equipment. The paper concludes with a use case scenario for the IRS-assisted UAVs in the context of intelligent transportation systems (ITS) in smart cities.

Predicting spectrum status duration using non-linear homotopy estimation based HMM for UAV communications

As the spectrum resources are limited, unmanned aerial vehicles (UAVs) may communicate by flexible spectrum sharing. To avoid interference to primary users, UAVs require spectrum state prediction. Furthermore, prediction of spectrum status duration is also needed since the primary users may access just after the UAVs access. In other words, a UAV should predict the spectrum status duration and choose one with enough idle duration. Unfortunately, reported methods for duration prediction, such as the non-stationary hidden Markov model (NSHMM), may not work for UAV communications. These methods predict the duration of the next moment at the current location, while the UAV is probably to have moved to the next location when obtaining the prediction result. Therefore, the UAV should predict the spectrum status duration of the next moment at the next location. The main challenge is that it is difficult to obtain the historical data of the next location in advance during the UAV flight. In this paper, we propose a new prediction approach that firstly estimates the historical data of the next location by using a non-linear homotopy relationship, then implements the NSHMM to predict the duration, referring to the non-linear homotopy estimation based HMM (NLH-HMM). Experimental results show that the NLH-HMM based method can efficiently predict the spectrum status duration of the next location.

Pathloss and Airframe Shadowing Loss of Air-to-Ground UAV Channel in the Airport Area at UHF- and L-Band

In air-to-ground unmanned aerial vehicle (UAV) communications, an accurate channel propagation model is very important for the evaluation and design of communication systems. For this purpose, we have conducted several channel measurement campaigns in the airport area at UHF- and L-band (around 433 MHz and 1.518 GHz). In the line-of-sight (LoS) case, we give the distance-dependent pathloss models for both frequenciy bands. Meanwhile, it is found that the measured pathloss in the LoS scenario has a good agreement with the two-ray propagation model at short link distances. This is because of the strong ground reflection path. In the non-LoS (NLoS) case, we propose the elevation-dependent pathloss model at UHF-band based on the field measurement results. On the other hand, considering that the airframe of the UAV can block the wave propagation during the 3-D rotation in the air and the taking off and landing procedures, we specifically analyze the statistical characteristics of the airframe shadowing loss for the two cases and provide the loss distribution models for the fixed-wing, small-sized UAVs made of wood structure and carbon fiber skins.

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.

Smart Patrolling using Drones, Artificial Intelligence and 6G Wireless Communication

In today’s epoch, where law breaking have become an element of human society, with an annual increase of 1.6% in registered crime cases. This increases police departments’ work every year as well. Now days, Robots have proved themselves to accomplish the tasks that even humans find risky and tiresome. Unmanned Aerial Vehicles (UAV) are autonomous vehicles like aircrafts or drones which execute preprogrammed missions. In this project, we present a smart patrolling system with minimum latency possible using UAV, fog computing and 6G wireless communication. UAV will be auto detecting any kind of doubtful activity using lightweight convolution neural network (CNN), Kerman hybrid tracking algorithm. UAVs will be further trained to transmit only the suspicious data to the fog nodes to reduce energy and bandwidth consumption. Fog nodes will process the received data using fuzzy decision making and will further transmit the important information like alerts with live video streaming and location of crime to police station after.

Cooperative hybrid beamforming design in Multi‐UAV MmWave MIMO systems

AbstractDue to the limitation of mobility and cost, it is inappropriate to utilize ground base station (GBS) to provide service in emergency scenarios or harsh environments. Unmanned aerial vehicle (UAV) is considered as a feasible way to solve the problem. Millimetre wave communication and UAV can be used together to offer wireless communications flexibly and rapidly for target area. However, fuselage jittering and energy supply would affect the quality and reliability of UAV communication seriously. To overcome the weakness of UAV, the authors prefer to use multiple UAVs to provide wireless communication service simultaneously. This paper investigates the design of hybrid beamforming in the scenarios including multiple UAVs and one end user. Cooperation strategy and alternating optimization method are applied in this design. The distributed UAV base stations are regarded as an entirety and the reference beamforming scheme obtained. Then, the beamforming problem for each UAV is formulated. Moreover, residual error minimization (REM) algorithm is proposed to find the solution of hybrid beamformers at UAV side. Finally, the beamformer of the receiver is optimized with the assist of effective channel, which includes the gain of both channel and beamformers of the transmitters. Simulation results show that the proposed algorithm can achieve near‐optimal spectral efficiency in different scenarios.

A Method to Reduce Route Discovery Cost of UAV Ad Hoc Network

The unmanned aerial vehicle communication networks (UAVCNs) are composed of unmanned aerial vehicles (UAVs) connected in ad hoc mode to facilitate vertical communication in 5G and beyond networks. UAVs operating in an ad hoc mode of operation mostly use reactive routing protocols to establish routes in the network to reduce the energy consumption of the nodes. In this article, a route discovery method is presented to reduce the overhead faced by reactive routing protocols during the route discovery phase. The expanding ring search (ERS) method is mostly used by reactive routing protocols in the destination discovery phase of the routing algorithm. Although the performance of the ERS method is better than simple flooding, the presented method further reduces energy consumption and routing overhead as compared to the conventional ERS method. To achieve the task, the time to live (TTL) is modified to accommodate a large number of nodes in a search attempt. We proposed variants of the proposed techniques for diverse application requirements and compared the performance with the state-of-the-art ERS technique. It has been demonstrated with the help of simulations that the presented algorithm outperforms the ERS method in terms of reduced routing overhead and reduced energy consumption.

Federated learning via over-the-air computation in IRS-assisted UAV communications

Intelligent reflective surface (IRS) and unmanned aerial vehicle (UAV) communication are two key technologies in the sixth generation of mobile communication (6G). In this paper, IRS is equipped on UAV to form aerial IRS, which can achieve 360° panoramic full-angle reflection and flexible deployment of IRS. In order to achieve high-quality and ubiquitous network coverage under data privacy and low latency requirements, we propose an Federated learning (FL) network via Over-the-Air computation (AirComp) in IRS-assisted UAV communications. Our goal is to minimize the worst-case mean square error (MSE) by jointly optimizing the IRS phase shift, denoising factor for noise suppression, the user’s transmission power, and UAV trajectory. Optimizing and quickly adjusting the UAV position and IRS phase shift, it flexibly assists the signal transmission between users and base stations (BS). In order to solve this complex non-convex problem, we propose a low-complexity iterative algorithm, which divides the original problem into four sub-problems, respectively using the semi-definite programming (SDP) method, slack variable introduction method, successive convex approximation (SCA) method to solve each sub-problem. Through the analysis of simulation results, our proposed design scheme is obviously better than other benchmark schemes.

Intelligent flying-beamformer for hybrid mmWave systems: A deep reinforcement learning approach

Millimeter wave (mmWave) enabled unmanned aerial vehicle (UAV) communications with excellent flexibility and high data transmission capabilities are widely regarded as an essential element of the next generation non-terrestrial networks. In this paper, we investigate the 3D trajectory design, beamwidth and power allocation problems for mmWave UAV communication systems. We first formulate a non-convex optimization problem to maximize the total normalized spectral efficiency (NSE) of all ground terminals (GTs). Then, we propose a deep reinforcement learning (DRL) based framework, termed as intelligent flying-beamformer, to solve the formulated non-convex optimization, which contains of two sequential phases: 3D UAV trajectory design, and joint optimization of beamwidth and power allocation. In particular, the off-policy deep deterministic policy gradient (DDPG) and the on-policy proximal policy optimization (PPO) algorithms are used to process the complex data in the two phases, respectively. Simulation results verify the effectiveness of the proposed intelligent flying-beamformer in improving system capacity. Specifically, numerical results show that the optimization of the beamwidth can indeed significantly improve the spectral efficiency of mmWave UAV network.

Hybrid Offline-Online Design for Reconfigurable Intelligent Surface Aided UAV Communication

This letter considers the reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) communication systems in urban areas under the general Rician fading channel. A hybrid offline-online design is proposed to improve the system performance by leveraging both the statistical channel state information (S-CSI) and instantaneous channel state information (I-CSI). For the offline phase, we aim to maximize the expected average achievable rate based on the S-CSI by jointly optimizing the RIS’s phase-shift and UAV trajectory. The formulated stochastic optimization problem is difficult to solve due to its non-convexity. To tackle this problem, we propose an efficient algorithm by leveraging the stochastic successive convex approximation (SSCA) techniques. For the online phase, the UAV adaptively adjusts the transmit beamforming and user scheduling according to the effective I-CSI. Numerical results verify that the proposed hybrid design performs better than various benchmark schemes, and also demonstrate a favorable trade-off between system performance and CSI overhead.

Secrecy Capacity Enhancement in Active IRS-Assisted UAV Communication System.

As a new technology for reconstructing communication environments, intelligent reflecting surfaces (IRSs) can be applied to UAV communication systems. However, some challenges exist in IRS-assisted UAV communication system design, such as physical layer security issues, IRS design, and power consumption issues owing to the limitation of the hardware. Therefore, a secrecy capacity optimization scheme for an active IRS-assisted unmanned aerial vehicle (UAV) communication system is proposed to solve multi-user security issues. In particular, controllable power amplifiers are integrated into reflecting units to solve the problem of blocked links, and the UAV can dynamically select the served user according to the channel quality. In order to maximize the system average achievable secrecy capacity and ensure the power constraints of the UAV and active IRS, user scheduling, UAV trajectory, beamforming vector, and reflection matrix are jointly optimized, and the block coordinate descent (BCD) algorithm is applied to solve this non-convex problem. Simulation results show that the active IRS-assisted UAV communication scheme can significantly weaken the "multiplicative fading" effect and enhance the system secrecy capacity by 55.4% and 11.9% compared with the schemes with passive IRS and without optimal trajectory, respectively.

A non-stationary channel prediction method for UAV communication network with error compensation

In an unmanned aerial vehicle (UAV) communication network, especially for mission-critical applications, ultra-reliable and low-latency communication (URLLC) of the control links has essential implications for realizing collision avoidance and real-time control of UAVs. However, the UAVs’ mobility and the channel environment’s variability may make the wireless channels highly non-stationary. Real-time and accurate Channel Status Information (CSI) acquisition is a critical challenge to implementing URLLC for UAV control links. Predicting the characteristics of non-stationary channels is profitable for formulating communication strategies to mitigate the effects of future channel fading. Nevertheless, the difficulty lies in extracting accurate prediction models for non-stationary channels to obtain accurate CSI. This paper proposes a more precise prediction by utilizing the CSI obtained as feedback from the receiver and introducing error correction. The method contains three stages, CSI collection and processing, channel tracking, and error correction. First, the transmitter collects the CSI feedback from the receiver and converts it to a stationary series. Secondly, the recent historical CSIs are used to track the time evolution of the channel based on an autoregressive integrated moving average (ARIMA) model. Next, the Gaussian process regression (GPR) model is used to establish the prediction error regression model and obtain a more accurate prediction. Finally, the effectiveness of the proposed method is verified based on a UAV wireless communication network application scenario in an urban environment. Simulation results show that the proposed method outperforms other methods regarding RMSE and reliability in high-dynamic UAV communication scenarios. Significantly when the channel changes rapidly, this method can respond to the changes faster and predict the CSI more accurately.

Characterization of Low-Power Wireless Links in UAV-Assisted Wireless-Sensor Network

The introduction of unmanned aerial vehicle (UAV) makes low-power links face many new challenges in UAV-assisted wireless-sensor network (WSN). In this article, the spatial characteristics of these low-power links are analyzed comprehensively, and the applicability of traditional link quality metrics and models is evaluated. In particular, the differences between link characteristics of UAV-assisted WSN and those of traditional WSN are discussed, and possible reasons for these differences are analyzed. To achieve this goal, two UAVs working on different bands are used and three different flight modes are designed. Experimental results show that link characteristics of UAV-assisted WSN become more complicated due to the 3-D movement of UAV. Link quality fluctuates significantly when the altitude or horizontal distance changes, and it is difficult to demarcate a clear bound between the connected and transitional region. Traditional models for spatial characteristic are only applicable when there is no UAV communication interference and the direction between the air node and ground node remains unchanged. Meanwhile, existing models between physical-layer metrics and packet reception ratio are only applicable when there is no UAV communication interference. For links with the same quality, stability of UAV-assisted WSN is not significantly different from that of traditional WSN. Finally, there is more obvious overall asymmetry between uplinks and downlinks in UAV-assisted WSN, and the quality of downlinks is significantly better than that of uplinks. The discovery of these new features would have great significance on the design of UAV-assisted WSN.

Joint Resource Allocation and Trajectory Optimization for Completion Time Minimization for Energy-Constrained UAV Communications

An unmanned-aerial-vehicle (UAV)-aided communication scenario is studied in this research, where a UAV with constrained on-board battery capacity communicates with multiple ground users (GUs) during the flying process. We investigate the UAV trajectory as well as the resource allocation to minimize the time consumption of the specific task. The formulated problem is non-convex with innumerable variables. To make this problem tractable, a collection of line segments are utilized to represent the real trajectory through discretization. The reformulated problem is solved by applying the block coordinate descent (BCD) algorithm. To be specific, the reformulated problem is decoupled into two subproblems, i.e., resource allocation and trajectory optimization subproblems. The subproblems are both non-convex, which are efficiently resolved with the successive convex approximation (SCA) method. Then the result is obtained with an iterative algorithm. The simulation results reveal that, in comparison to benchmark schemes, the system performance has a significant improvement.

Two-Path Conventional Neural Network Based Signal-to-Noise Ratio Evaluation for UAV Communication Link

With the development of information technology, unmanned aerial vehicles (UAVs) have become an indispensable and important part of daily life and they have brought great convenience to life. Evaluating the signal-to-noise ratio (SNR) of UAV communication link is vital to improve the communication performance between UAV and the user. The classical SNR evaluation schemes of UAV communication link are limited in terms of performance, while deep learning (DL) based schemes are always at the expense of computation complexity. To solve the issues mentioned above, a two-path convolution neural network (TP-CNN) is proposed therein to evaluate the SNR of UAV communication link. Firstly, a two-dimensional dataset of UAV control signal is built and expanded thereafter. Then the TP-CNN model is designed and modified by feature fusion of input samples. Finally, the simulations are conducted, and the simulation results indicate that the performance of our proposed model is superior to that of the baseline model in terms of mean absolute error (MAE) and mean relative error (MRE).

Optimal Position and Target Rate for Covert Communication in UAV-Assisted Uplink RSMA Systems

With the explosive increase in demand for wireless communication, the issue of wireless communication security has also become a growing concern. In this paper, we investigate a novel covert communication for unmanned aerial vehicle (UAV)-assisted uplink rate-splitting multiple access (RSMA) systems, where a UAV adopts the rate-splitting (RS) strategy to increase the total transmission rate while avoiding deteriorating the covert transmission of a ground user. In the proposed system, a ground user and a UAV adopt the RSMA scheme to simultaneously communicate with a base station surveilled by an evil monitor. The UAV acts as both the transmitter and the friendly jammer to cover the ground user’s transmission with random power. To maximize the expected sum rate (ESR), we first study the RS strategy and obtain the optimal power allocation factor. Then, the closed-form of minimum detection error probability (DEP), ESR, and optimal target rate of the UAV are derived. Constrained by the minimum DEP and expected covert rate (ECR), we maximize the ESR by optimizing the position and target rate of the UAV. Numerical results show that the proposed scheme outperforms the traditional NOMA systems in terms of ESR with the same DEP and ECR.

A new connectivity model for unmanned aerial vehicle communications and flying height optimization

AbstractThis paper presents a new connectivity model for unmanned aerial vehicle (UAV) communications. We present closed‐form formulas for the probability of line‐of‐sight as a function of UAV height and distance to a ground based receiver, based on a two‐dimensional model of building and UAV locations. We show that our formulas exactly match Monte‐Carlo simulated values, whereas the existing approach over‐predicts by a factor of 2 to 3. We use the model to optimize the flying height for a UAV deployed as a mobile base station, where the goal is to maximize the number of possible connected users. We observe that the optimal height has a log‐linear characteristic. We use this to present a closed‐form formula for optimal height. We show that our techniques predict the height within 4% of realistic Monte‐Carlo simulations, and significantly outperform existing approaches which are shown to over‐predict by 40%–50%.

Joint resource scheduling and trajectory design for multi-UAV-assisted uplink NOMA networks

The combination of unmanned aerial vehicle (UAV) communication and non-orthogonal multiple access (NOMA) is of great significance to support massive connections in the Internet of things (IoT). This paper studies a multi-UAV-assisted communication network, where the UAVs are employed as aerial access points to receive data from IoT nodes with NOMA adopted for uplink transmission. The aim is to maximize the average sum rate of the network, with the requirements of the individual upload of each node. To handle the node clustering problem, the spectral clustering and maximum weight matching algorithm are first integrated to divide the IoT nodes. Then, the time-varying NOMA user grouping scheme is implemented accordingly. Furthermore, by considering the trajectory decomposition and successive hover-and-fly structure, the complex UAV trajectory problem is transformed into a linear programming and the optimal time-continuous trajectory is achieved. Extensive simulations validate the effectiveness of the proposed algorithms for the UAV-assisted NOMA network.

Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning

Aiming at the unmanned aerial vehicle (UAV)-assisted federated learning wireless-network scenario, and considering the influence of the UAV altitude on the coverage area, we propose a joint optimization algorithm of UAV placement, computation and communication resources. Considering the energy efficiency and federated learning performance, we defined the cost function of the system. Under the constraint of the total delay of federated learning completion, we formulated an optimization problem of minimizing the cost function to achieve the balance between the total energy consumption of users and the federated learning performance. Since the formulated optimization problem is a non-convex problem, in order to solve this problem, we decomposed it into three optimization subproblems: UAV horizontal placement, local accuracy and computation and communication resources. We used successive convex approximation (SCA), the Dinkelbach Method, the Bisection method and KKT condition, respectively, to solve the three subproblems, and finally obtain the optimal solutions through iteration of the three subproblems. Simulation results show that compared with the federated learning scenario under fixed UAV altitude, our proposed algorithm not only guarantees the learning performance, but also reduces more users’ total energy consumption.

Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey

Advancements in electronics and software have enabled the rapid development of unmanned aerial vehicles (UAVs) and UAV-assisted applications. Although the mobility of UAVs allows for flexible deployment of networks, it introduces challenges regarding throughput, delay, cost, and energy. Therefore, path planning is an important aspect of UAV communications. Bio-inspired algorithms rely on the inspiration and principles of the biological evolution of nature to achieve robust survival techniques. However, the issues have many nonlinear constraints, which pose a number of problems such as time restrictions and high dimensionality. Recent trends tend to employ bio-inspired optimization algorithms, which are a potential method for handling difficult optimization problems, to address the issues associated with standard optimization algorithms. Focusing on these points, we investigate various bio-inspired algorithms for UAV path planning over the past decade. To the best of our knowledge, no survey on existing bio-inspired algorithms for UAV path planning has been reported in the literature. In this study, we investigate the prevailing bio-inspired algorithms extensively from the perspective of key features, working principles, advantages, and limitations. Subsequently, path planning algorithms are compared with each other in terms of their major features, characteristics, and performance factors. Furthermore, the challenges and future research trends in UAV path planning are summarized and discussed.