Unmanned Aerial Vehicle Communication Systems Research Articles

A simple characterization of UAV communication systems

A simple characterization of UAV communication systems

Energy minimization for UAV communication systems assisted with multiple IRSs

Unmanned aerial vehicles (UAVs) have steadily gained attention to overcome the harsh propagation loss and blockage issue of millimeter-wave communication. However, UAV communication systems suffer from energy consumption, which limits the flying time of UAVs. In this paper, we propose several UAV energy consumption minimization techniques through the aid of multiple intelligent reflecting surfaces (IRSs). In specific, we introduce a tractable model to effectively capture the characteristics of multiple IRSs and multiple user equipments (UEs). Then, we derive a closed form expression for the UE achievable rate, resulting in tractable optimization problems. Accordingly, we effectively solve the optimization problems by adopting the successive convex approximation technique. To compensate for the high complexity of the optimization problems, we propose a low complexity algorithm that has marginal performance loss. In the numerical results, we show that the proposed algorithms can save UAV energy consumption significantly compared to the benchmark with no IRSs, justifying that exploiting the IRSs is indeed favorable to UAV energy consumption minimization.

Offense-Defense Distributed Decision Making for Swarm vs. Swarm Confrontation While Attacking the Aircraft Carriers

The effectiveness of multiple UAVs at attacking the enemy target and defending against the attacking UAVs simultaneously gained researchers’ attention due to their cost efficiency and high mission success rates. This paper explores the UAV swarm vs. swarm offense/defense confrontation decision-making while attacking the aircraft carriers on open seas. The system is developed as a multi-agent system. Precisely, every UAV is modeled as an independent agent, which is free to make decisions based on the behavioral rules of the swarm, detection radius, and enemy location. A distributed auction-based algorithm with a limited data-sharing rate ability is formulated among swarm UAVs. A critical feature of the proposed strategy is that it empowers each UAV to make decisions in real-time during the mission in the presence of relatively limited communication hardware in terms of the update rate within the UAV communication system. The algorithm parameters are optimized to obtain an improved target allocation and elimination. Simulation results are presented to show the effectiveness of the proposed Offence-Defense UAV-based swarm Strategy.

Timeliness optimization of unmanned aerial vehicle lossy communications for internet-of-things

Information freshness is a key factor for Internet-of-Things (IoT) to make appropriate decisions and operations. This paper proposes an analytical framework for evaluating the timeliness performance of the IoT system based on Unmanned Aerial Vehicle (UAV) lossy communications. The performance analysis consists of the outage probability analysis and the Age-of-Information (AoI) analysis with outages. To begin with, we solve a lossy coding problem formulated from the UAV communication system, and derive a closed-form expression of the outage probability based on Shannon’s lossy source-channel separation theorem. Then, we characterize the Peak AoI (PAoI) for the considered system, and further minimize the PAoI by deriving the optimal rate for generating information. Moreover, we analyze the system performance through theoretical calculations and simulations. The results indicate that the optimal server utilization ratio is always no larger than 0.5. In practical applications, we can utilize the proposed analytical framework to determine the system parameters which guarantee the timeliness performance of UAV lossy communications.

Deep Learning for Channel Tracking in IRS-Assisted UAV Communication Systems

To boost the performance of wireless communication networks, unmanned aerial vehicles (UAVs) aided communications have drawn dramatically attention due to their flexibility in establishing the line of sight (LoS) communications. However, with the blockage in the complex urban environment, and due to the movement of UAVs and mobile users, the directional paths can be occasionally blocked by trees and high-rise buildings. Intelligent reflection surfaces (IRSs) that can reflect signals to generate virtual LoS paths are capable of providing stable communications and serving wider coverage. This is the first paper that exploits a three-dimensional geometry dynamic channel model in IRS- assisted UAV-enabled communication system. Moreover, we develop a novel deep learning based channel tracking algorithm consisting of two modules: channel pre-estimation and channel tracking. A deep neural network with off-line training is designed for denoising in the pre-estimation module. Moreover, for channel tracking, a stacked bi-directional long short term memory (Stacked Bi-LSTM) is developed based on a framework that can trace back historical time sequence together with bidirectional structure over multiple stacked layers. Simulations have shown that the proposed channel tracking algorithm requires fewer epochs to convergence compared to benchmark algorithms. It also demonstrates that the proposed algorithm is superior to different benchmarks with small pilot overheads and comparable computation complexity.

Optimization of Energy Efficiency in UAV-Enabled Cognitive IoT With Short Packet Communication

The unmanned aerial vehicle (UAV) assisted communication can significantly improve the coverage and spectrum efficiency of the Internet of Things (IoT). One main feature of IoT is short packet communication (SPC), in which the data transmission uses finite block-length codewords. The transmission rate and the packet error rate will affect the effective throughput of the IoT. In this paper, we investigate the impact of SPC on the performance of UAV-enabled spectrum sharing network. Specifically, the optimization of energy efficiency (EE) in the UAV communication system is considered since the battery of the UAV is usually limited. We design the packet error rate, the sensing duration, the normalized sensing threshold and the UAV’s transmit power to maximize the EE under the constraint that the primary user is sufficiently protected. Since the system parameters are intertwined with each other, we employ a successive optimization algorithm to solve the optimization problem by dividing it into three subproblems. Then, an efficient iterative algorithm is proposed to obtain the optimal parameters. Simulation results reveal that the proposed algorithm can converge to global optimal value and has better EE performance than other benchmark schemes. And there is a fundamental tradeoff between the throughput and the EE.

A Non-Stationary 3D Model for 6G Massive MIMO mmWave UAV Channels

This paper proposes a non-stationary three-dimensional (3D) irregular-shaped geometry-based stochastic model (IS-GBSM) for fifth generation (5G) and beyond massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) unmanned aerial vehicle (UAV) channels. This is the first sixth generation (6G) massive MIMO mmWave UAV IS-GBSM that can model the UAV channel space-time non-stationarity, and can describe the impact of some unique UAV-related parameters, e.g., the UAV’s moving direction, height, and speed, on channel statistical properties. To better represent the space-time non-stationarity in UAV scenarios, a novel UAV-related space-time cluster evolution algorithm is developed. The developed algorithm considers the characteristics of UAV communications on the modeling of space-time non-stationarity. Based on the proposed model, some channel statistical properties are derived and thoroughly investigated, including the space-time-frequency correlation function, Doppler power spectral density, envelope level crossing rate, and average fade duration. Some numerical results and interesting observations are given, and the impact of UAV-related parameters on channel statistical properties is explored, which can provide assistance for the design of 6G massive MIMO mmWave UAV communication systems. Finally, the applicability of the proposed model is verified by the close agreement between simulation results and measurement.

A low‐profile dual‐mode antenna for unmanned aerial vehicles applications

A low-profile dual-mode antenna is proposed in this paper. The antenna is composed of a semi-rectangular-annular (SRA) coaxial line with sleeve loading structures vertically mounted on the ground plane. The proposed antenna has two radiation modes, mode 1 of the vertically polarised (VP) radiation characteristics at 500 MHz and mode 2 of the horizontally polarised (HP) radiation characteristics at 588 MHz. The two vertical components of the SRA coaxial line contribute to the VP radiation, which is achieved with the in-phase electric currents on the two vertical components by adjusting the length of the in-body inductive loading structures. The horizontal component of the SRA coaxial line is mainly contributed to HP radiation when the electric currents on two vertical components are reversed. And the antenna is well matched with the additional impedance matching network, which consists of two inductors and a capacitor. The height of the fabricated prototype is only 25.9 mm (about 0.04λ) at 500 MHz. The fabricated prototype possesses monopole-like radiation pattern at 500 MHz and loop-antenna-like radiation pattern at 588 MHz. The measured maximum gain of the proposed antenna at 500 MHz is −6.8 dBi, and the measured maximum gain of the proposed antenna at 588 MHz is 2.5 dBi. The antenna is suitable for unmanned aerial vehicles communication systems due to its advantages of low-profile and dual-mode radiation.

Application of C1DAE-ANIL in End-to-End Communication of IRS-Assisted UAV System

Due to the uninterrupted flight of the unmanned aerial vehicles (UAV) and the varying phase of the intelligent reflecting surface (IRS) in the IRS-assisted UAV communication system, it is very difficult to estimate the channel information of the system accurately. The traditional solutions try to model the whole communication system as an autoencoder including encoder, channel, and decoder. However, the autoencoder needs to be retrained when the environment changes and the generalization ability is lousy. To solve above problems, a novel C1DAE-ANIL algorithm is proposed to estimate the channel information of the IRS-assisted UAV communication system. On one hand, one-dimensional convolution is introduced to the traditional multi-perceptron autoencoder (MLPAE) for enhancing the encoding and decoding performances of the one-dimensional convolutional autoencoder (C1DAE). On the other hand, the meta-learning is applied to the C1DAE, and the almost no inner loop (ANIL) algorithm is used to find an optimal initial parameter vector of the C1DAE to realize the rapid training in dynamic environments. Compared with the existing contenders, the simulation results show that the end-to-end communication model using the C1DAE-ANIL algorithm not only improves the accuracy of channel estimation but also greatly accelerates the training process in dynamic environments.

Real-time ray-based channel generation and emulation for UAV communications

A real-time ray-based hardware emulator for Unmanned Aerial Vehicle (UAV) communication channels which suits for the Three-Dimensional (3D) dynamic scenarios and considers the movements of both terminals is developed in this paper. The time-variant channel parameters, i.e., ray delay, ray gain, and ray Doppler frequency are precalculated in the host by using the Ray Tracing (RT) method. Meanwhile, RT simulation dramatically increases the number of valid rays. To address the problem of resource limitation and huge computational burden in the implementation, an efficient ray coefficients generation method based on iteration is proposed and implemented. With the advantages of low cost and high flexibility, a Software Defined Radio (SDR) hardware platform is used to emulate the ray-based UAV channels by utilizing the compact architecture including the Time-Division (TD) scheme and Tapped-Delay Line (TDL) for channel convolution. Finally, hardware measurement results demonstrate that the properties of emulated channel, i.e., Power Delay Profile (PDP) and Doppler Power Spectrum Density (DPSD) consist with the simulated ones, which verifies the correctness of hardware implementation. The proposed channel emulator provides an efficient way for optimization, verification, and evaluation of UAV communication systems.

UAV-Assisted Multi-Access Edge Computing: Technologies and Challenges

As the number of end users served by wireless networks continues to grow, modern wireless communication systems have put forward higher requirements for the diversity of wireless network forms. The unmanned aerial vehicle (UAV) wireless network communication system will occupy an important position in the future wireless communication system. With the explosive growth of 5G data traffic and Internet of Things (IoT) devices, computing-intensive, communication-intensive, and delay-sensitive applications have put tremendous pressure on UAV communication systems. Unlike terrestrial communication networks, the inherent characteristics of UAVs make it more troublesome to deal with the above-mentioned difficulties. Multi-access edge computing (MEC) can help mobile devices improve computing and communication capabilities, and its combination with UAVs is also an open subject. This article mainly summarizes the related knowledge and research of MEC and UAV wireless communication systems. We combine the UAV system with MEC technology and try to propose a UAV-assisted MEC wireless communication system. We analyze the key methods to realize this technology and point out a series of possible challenges. This article aims to provide new ideas for the proposed new architecture of aerial computing.

Robust trajectory planning for UAV communication systems in the presence of jammers

Unmanned Aerial Vehicle (UAV) has emerged as a promising novel application for the Sixth-Generation (6G) wireless communication by leveraging more favorable Line-of-Sight (LoS) propagation. However, the jamming resistance by exploiting UAV’s mobility is a new challenge in the UAV-ground communication. This paper investigates the trajectory planning problem in an UAV communication system, where the UAV is operated by a Ground Control Unit (GCU) to perform certain tasks in the presence of multiple jammers with imperfect power and location information. To ensure the reliability of the GCU-to-UAV link, we formulate the problem as a non-convex semi-infinite optimization, aiming to maximize the average worst-case Signal-to-Interference-plus-Noise Ratio (SINR) over a given flight duration by designing the robust trajectory of the UAV under stringent energy availability constraints. To handle this problem efficiently, we develop an iterative algorithm for the solution with the aid of S-procedure and Successive Convex Approximation (SCA) method. Numerous results demonstrate the efficacy of our proposed algorithm and offer some useful designinsights to practical system.

3D Non-Stationary Wideband UAV-to-Ground MIMO Channel Models Based on Aeronautic Random Mobility Model

A commonly used assumption in the existing unmanned aerial vehicle (UAV) channel models is that the UAV flies along linear trajectories. However, this assumption does not always hold in realistic UAV communication scenarios. In this paper, we relax the linear trajectory restriction and present a temporally non-stationary channel model for UAV-to-ground communication scenario. The temporal non-stationarity of the channel stemming from time-varying heading directions of the UAV is properly modeled. This is achieved by combining a geometry-based stochastic model (GBSM) and an aeronautic random mobility model (RMM), which is introduced to characterize the movement pattern of UAVs. Based on UAV and ground station (GS) trajectories, time-varying model parameters and statistics including space-time-frequency correlation function (STF-CF), Doppler power spectrum density (PSD), delay PSD, and stationary interval are derived. The influences of the RMM-related parameters on the statistics are presented and analyzed. Some of those statistics are verified by measurements, showing the practicability of the model. The work presented in this paper is helpful in designing UAV communication system considering realistic UAV trajectories.

UAV Trajectory and Communication Co-Design: Flexible Path Discretization and Path Compression

The performance optimization of UAV communication systems requires the joint design of UAV trajectory and communication efficiently. To tackle the challenge of infinite design variables arising from the continuous-time UAV trajectory optimization, a commonly adopted approach in the existing literature is by approximating the UAV trajectory with piecewise-linear path segments connected via a finite number of waypoints in three-dimensional (3D) space. However, this approach may still incur prohibitive computational complexity in practice when the UAV flight period/distance becomes long, as the distance between consecutive waypoints needs to be kept sufficiently small to retain high approximation accuracy. To resolve this fundamental issue, we propose in this paper a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">new</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">general</i> framework for UAV trajectory and communication co-design with flexible number of waypoint optimization variables (called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">designable</i> waypoints) or their <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sub-path</i> representations. First, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">flexible path discretization</i> scheme that optimizes only a number of selected waypoints (designable waypoints) along the UAV path for complexity reduction, while all the designable and non-designable waypoints are used in calculating the approximated communication utility along the UAV trajectory for ensuring high trajectory discretization accuracy. Next, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">path compression</i> scheme, which treats the UAV trajectory as a signal and compresses its path representation based on the basis decomposition. Specifically, the UAV 3D path is first decomposed into three one-dimensional (1D) sub-paths and each sub-path is then approximated by superimposing a number of selected basis paths (which are generally less than the number of designable waypoints) weighted by their corresponding path coefficients, thus further reducing the path design complexity. Finally, we provide a case study on UAV trajectory design for aerial data harvesting from distributed sensors, and numerically show that the proposed flexible path discretization and path compression schemes can significantly reduce the UAV trajectory design complexity yet achieve favorable rate performance as compared to conventional path/time discretization schemes.

A Novel Non-Stationary 6G UAV Channel Model for Maritime Communications

To achieve space-air-ground-sea integrated communication networks for future sixth generation (6G) communications, unmanned aerial vehicle (UAV) communications applying to maritime scenarios serving as mobile base stations have recently attracted more attentions. The UAV-to-ship channel modeling is the fundamental for the system design, testing, and performance evaluation of UAV communication systems in maritime scenarios. In this paper, a novel non-stationary multi-mobility UAV-to-ship channel model is proposed, consisting of three kinds of components, i.e., the line-of-sight (LoS) component, the single-bounce (SB) components resulting from the fluctuation of sea water, and multi-bounce (MB) components introduced by the waveguide effect over the sea surface. In the proposed model, the UAV as the transmitter (Tx), the ship as the receiver (Rx), and the clusters between the Tx and Rx, can be seen as moving with arbitrary velocities and arbitrary directions. Then, some typical statistical properties of the proposed UAV-to-ship channel model, including the temporal autocorrelation function (ACF), spatial cross-correlation function (CCF), Doppler power spectrum density (PSD), delay PSD, angular PSD, stationary interval, and root mean square (RMS) delay spread, are derived and investigated. Finally, by comparing with the available measurement data, the accuracy of proposed channel model is validated.

Capacity Analysis of UAV Communication System Based on FD-NOMA

In order to improve the communication quality between unmanned aerial vehicle (UAV) and ground users. An UAV communication system model based on full-duplex and non-orthogonal multiple access (NOMA) technology is proposed, and the capacity of the system model in urban scenarios is analyzed. First, the accurate capacity expression of the system model is given; then, the calculation problem of the exponential integral function in the formula is solved by introducing the Q function, and the approximate closed-form expression of the exponential integral function is obtained, and then the approximate closed-form expression of the capacity is obtained; second, the coefficient factor is used to obtain a more accurate approximate closed-form expression; finally, simulation and numerical results show that increasing the number of UAV or NOMA power vector can achieve better capacity performance.

A Novel Link Failure Detection and Switching Algorithm for Dissimilar Redundant UAV Communication

Unmanned Aerial Vehicles (UAVs) used for humanitarian applications require simple, accessible and reliable components. For example, a communication system between UAV and the Ground Control Station (GCS) is essential in order to monitor UAV status; various communication protocols are available in the industry. Such systems must be simple for non-technical personnel (e.g., healthcare workers) to operate. In this study, a novel link failure detection and switching algorithm was proposed for a dissimilar redundant UAV communication system designed for long-range vaccine delivery in rural areas. The algorithm would ease the workload of the operators and address a research gap in the design of such algorithms. A two-layer design is proposed: A baseline layer using the heartbeat method, and optimisations to speed up local failure detection. To dynamically tune the heartbeat timeout for the algorithm’s baseline without intervention from ground operators, the modified Jacobson’s algorithm was used. Lab simulations found that the algorithm was generally accurate in converging to an optimal value, but has less satisfactory performance at poor or unpredictable connectivity, or when link switches get triggered frequently. Improvements have been suggested for the algorithm. This study contributes to ongoing research on ensuring reliable UAV communication for humanitarian purposes.

Performance analysis of SSK modulation for UAVs communication

In this paper, we investigate the performance of Space Shift Keying (SSK) modulation on a Multiple-Input-Multiple-Output (MIMO) wireless communication system for Unmanned Aerial Vehicles (UAVs) over Rician fading channels. SSK modulation can be a key technique for energy-efficient and reliable communication scenarios for UAV applications. The first part of the performance analysis consists of theoretical derivations for the Bit Error Rate (BER) performance of UAVs' SSK communication. The second part consists of theoretical derivations for the outage probability. Error rate performance comparison between SSK modulation for UAV communication systems and conventional systems is given. Then, the proposed system's diversity order is derived and presented as a number of receive antenna Nr. The BER performance decrease against non-line-of-sight channel scenarios is shown via simulation results due to the line-of-sight effect on the SSK modulated system. Monte Carlo simulations validate theoretical derivation results for various transmit - receive antennas and Rician K factor values.

Intelligent Reflecting Surfaces Assisted UAV Communications for IoT Networks: Performance Analysis

The increasing demand for wireless connectivity and the emergence of the notion of the Internet of Everything require new communication paradigms that will ultimately enable a plethora of new applications and new disruptive technologies. In this context, the present contribution investigates the use of the recently introduced intelligent reflecting surface (IRS) concept in unmanned aerial vehicles (UAV) enabled communications aiming to extend the network coverage and improve the communication reliability as well as spectral efficiency of Internet of Things (IoT) networks. In particular, we first derive tractable analytic expressions for the achievable symbol error rate (SER), ergodic capacity, and outage probability of the considered set up. Following this, we also derive tight upper and lower bounds on the average signal-to-noise ratio (SNR). Our derivations are then compared with the corresponding asymptotic performance, based on the central limit theorem (CLT) assumption, which reveals that the asymptotic SNR falls within the area between derived bounds, and approaches either bound depending on the number of reflective elements (REs). We further show that the asymptotic SER becomes in a tight agreement with the corresponding exact simulation SER for N ≥ 16. In addition, the offered results demonstrate that the use of the IRS is significantly effective as they assist in improving the achievable SER by five orders of magnitude. We further demonstrate that, in terms of achievable ergodic capacity, IRS-assisted UAV communication systems can exhibit ten times higher capacity compared to conventional UAV communications. Based on the above, these results and related insights are anticipated to be useful in the design and deployment of IRS-assisted UAV systems in the context of beyond 5G communications, such as 6G communications.

Wireless secure communication involving UAV: an overview of physical layer security

Unmanned aerial vehicle (UAV) is a flight device with power and energy based on computer program control, which has the characteristics of small size, light weight, high maneuverability and low cost. With its characteristics, UAV can play an important role in military and civil fields. However, due to the broadcast nature of wireless communication and inherent air-to-ground line-of-sight channel, UAV wireless communication system is more vulnerable to security threats. On the basis of traditional encryption technology, the secrecy capacity of the UAV communication system can be improved by introducing physical layer security. This article aims to study the security of the physical layer in the UAV communication system, and summarizes the latest research results on the safety communication involving UAVs on the physical layer, such as trajectory optimization, power allocation, user scheduling and cooperative UAVs. Further, some potential research directions and challenges in physical layer security of UAV system are discussed.