Unmanned Aerial Vehicle Communication Systems Research Articles

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.

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.

Energy Efficient Resource Allocation for Wireless Powered UAV Wireless Communication System With Short Packet

The unmanned aerial vehicle (UAV), which is prominent in its flexibility and low cost, is considered to be fully utilized in the future wireless communication system to provide flexible services and improve connectivities. In this paper, we investigate the resource allocation problem in a wireless powered UAV communication system. In this considered system, The UAV acts as hybrid access point (HAP), which can first perform wireless power transfer in the downlink and charge the Internet of Thing (IoT) user devices (UDs). The UDs can use the harvested energy to deliver the data to the UAV. In the uplink, we explicitly consider short packet communication (SPC) as the transmission feature, which adopts finite block-length codewords and suffers from rate degradation. With the objective to maximize system energy efficiency, we jointly optimize the position and transmit power of the UAV, and transmission time of each UD. To address the formulated non-convex problem, we develop efficient algorithms to find sub-optimal solutions. Extensive simulations are conducted to verify the effectiveness of the proposed scheme.

Digital Self-Interference Cancellation for Full-Duplex UAV Communication System over Time-Varying Channels

Full-duplex unmanned aerial vehicle (UAV) communication systems are characterized by mobility, so the self-interference (SI) channel characteristics change over time constantly. In full-duplex UAV communication systems, the difficulty is to eliminate SI in time-varying channels. In this paper, we propose a pilot-aid digital self-interference cancellation (SIC) method. First, the pilot is inserted into the data sequence uniformly, and the time-varying SI is modeled as a linear non-causal function. Then, the time-varying SI channel is estimated by the discrete prolate spheroidal basis expansion model (BEM). The error of block edge channel estimation is reduced by cross-block interpolation. The result of channel estimation is convolved with the transmitted data to obtain the reconstructed SI, which is subtracted from the received signal to achieve SIC. The simulation results show that the SIC performance of the proposed method outperforms the dichotomous coordinate descent recursive least square (DCD-RLS) and normalized least mean square (NLMS) algorithms. When the interference to noise ratio (INR) is 25 dB, the performance index normalized least mean square (NMSE) is reduced by 5.5 dB and 4 dB compared with DCD-RLS and NLMS algorithms, which can eliminate SI to the noise floor, and the advantage becomes more obvious as the INR increases.

A dual‐frequency inverted‐F antenna for UAVs transceiver application

AbstractA dual‐frequency inverted‐F antenna operating in the ultrahigh frequency band is presented. The proposed antenna includes an inverted F‐shaped radiator and a dual‐frequency loading circuit. The dual‐frequency loading circuit is located between the short pin and the ground. The dual‐frequency loading circuit presents a short‐circuit state at the low‐frequency band and an open‐circuit state at the high‐frequency band. By introducing the dual‐frequency loading circuit, the proposed antenna possesses dual‐frequency performance compared with the traditional inverted‐F antenna. The resonant frequencies of the proposed antenna can be controlled by adjusting the component values of the dual‐frequency loading circuit. A prototype was fabricated and measured to validate the method. The height of the fabricated antenna is 40 mm at 713 MHz. The bandwidths of the fabricated prototype are 713–779 MHz and 858–920 MHz. The proposed antenna possesses vertical polarization, which shows that the proposed antenna has broad application prospects in unmanned aerial vehicles communication systems.

Trajectory planning for OTFS-based UAV communications

Unmanned aerial vehicles (UAVs) have attracted growing research interests in recent years, which can be used as cost-effective aerial platforms to transmit collected data packets to ground access points (APs). Thus, it is crucial to investigate robust air-tos-ground (A2G) wireless links for high-speed UAVs. However, the A2G wireless link is unstable as it suffers from large path-loss and severe Doppler effect due to the high mobility of UAVs. In order to meet these challenges, we propose an orthogonal time frequency space (OTFS)-based UAV communication system to relief the Doppler effect. Besides, considering that the energy of UAV is limited, we optimize the trajectory planning of UAV to minimize the energy consumption under the constraints of bit error rate (BER) and transmission rate, where the Doppler compensation is taken into account. Simulation results show that the performance of OTFS-based UAV system is superior to orthogonal frequency division multiplexing (OFDM)-based UAV systems, which can accomplish transmission tasks over shorter distances with lower energy consumption.

A Double-Beam Soft Handover Scheme and Its Performance Analysis for mmWave UAV Communications in Windy Scenarios

Unmanned aerial vehicles (UAVs) acting as users to access the cellular network over the millimeter wave (mmWave) spectrum form a promising solution to guarantee high data rate transmission. Due to the narrow beams in the mmWave systems and the high mobility of the UAVs, a key challenge of mmWave UAV communications is to design efficient and seamless handover schemes, especially in windy scenarios. In this paper, we first investigate the signaling interaction procedure of the hard handover strategy in 3GPP 5G new radio (NR) standard. For providing better connectivity and lower outage probability, a soft handover scheme named double-beam soft handover (DB-SH) is designed for the mmWave UAV system. Considering the dynamic wind effects, we formulate a random walk (RW) model for quantifying the angle offsets of the beams at the UAVs, as the orientations of the UAVs suffer from the fluctuations over time due to the dynamic wind load. Considering the RW model and the beam training, we further analyze the outage probabilities of the 3GPP 5G NR hard handover scheme and the DB-SH scheme for the mmWave UAV communication systems. Finally, our simulation results demonstrate the accuracy of the presented theoretical analysis which provides insights for mmWave UAV system designs.

Trajectory Design and Communication Resources Allocation for Wireless Powered Secure UAV Communication Systems

Benefiting from the intrinsic convenience, flexibility, low cost, and unmanned aerial vehicles (UAVs) are envisioned to play an important role in future wireless communication systems. However, the limited on-board battery capacities and the broadcast characteristic of air-to-ground wireless channels bring some nonnegligible challenges on achieving secure UAV communications. To tackle these challenges, we propose to apply both the wireless power transfer and physical layer security techniques in a UAV communication system. We investigate the secrecy rate maximization problem by jointly optimizing the wireless charging duration, the trajectory, and transmit power of the UAV, subject to limited battery capacity, maximum flying speed, and energy-harvesting causal constraints. To solve this nonconvex problem, we first transform this problem into an equivalent problem with a smooth objective function, and then propose a low-complexity trajectory design and communication resources allocation algorithm for settling the reformulated problem via employing alternating optimization and SCP. Specifically, the reformulated problem is decomposed into three independent subproblems: the wireless charging duration optimization, the transmit power control, and the trajectory design of the UAV. Subsequently, we iteratively solve one of these three subproblems, while the others being fixed until the proposed algorithm converges. Extensive simulation results are provided to demonstrate that the proposed algorithm can significantly enhance the secrecy rate relative to the benchmark algorithms in absence of rechargeable battery or with fixed trajectory and transmit power.

A Non-Stationary 6G UAV Channel Model With 3D Continuously Arbitrary Trajectory and Self-Rotation

In this paper, based on the geometric stochastic modeling method, a space-time-frequency (S-T-F) non-stationary model with three-dimensional (3D) continuously arbitrary trajectory and self-rotation is proposed for sixth generation (6G) massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) unmanned aerial vehicle (UAV) channels. It is the first 6G massive MIMO mmWave UAV channel model that considers the 3D continuously arbitrary trajectory of UAV in practice and models S-T-F non-stationarity of 6G UAV channels. In the proposed model, the calculation of channel impulse response (CIR) is developed, which considers the 3D time-varying accelerations and self-rotations of transceivers and clusters. To further model the S-T-F non-stationarity of UAV channels, a novel UAV-related birth-death (BD) algorithm based on correlated clusters is developed. In the developed algorithm, the impact of typical UAV-related parameters, e.g., the UAV’s moving direction, altitude, and time-varying velocity, on the setting of correlated clusters and the BD process is sufficiently considered. Important channel statistical properties are derived and investigated. Some numerical results and interesting observations are given, which can provide some assistance for the design of 6G massive MIMO mmWave UAV communication systems. Finally, the utility of the proposed model is verified by the close agreement between simulation results and ray-tracing-based results.

Energy-Efficiency Joint Trajectory and Resource Allocation Optimization in Cognitive UAV Systems

In this article, an effective energy-efficiency optimization problem is investigated in the cognitive unmanned aerial vehicle (UAV) communication system, where the moving following UAV (FUAV) transmits collected data to the leading UAV (LUAV) by reusing the spectrum of the ground primary user. For this scenario, a novel joint UAV trajectory and resource allocation optimization algorithm is proposed. We aim to maximize the energy efficiency of the cognitive UAV communication systems under interference, shortest step size, collision prevention, and speed constraints. However, this optimization problem is difficult to be solved, as it is nonconvex and involves strong relations among many variables. To address this issue, we first decompose the original optimization problem into four subproblems: 1) spectrum sensing duration subproblem; 2) spectrum sensing threshold subproblem; 3) transmitted power subproblem; and 4) FUAV trajectory optimization subproblem. For spectrum sensing duration subproblem and spectrum sensing threshold subproblem, their optimal solutions can be efficiently solved via a golden section search method. For the transmitted power subproblem, the closed-form expression of the optimal transmitted power is deduced. For the nonconvex FUAV trajectory optimization subproblem, we consider the shortest step size design under known starting and destination positions and then obtain an approximate solution of FUAV trajectory via the successive convex optimization (SCO) technique. Finally, an alternate iterative framework with fast convergence is designed to solve the original optimization problem. The simulation results show that the proposed algorithm has 18 times improvement in energy efficiency compared with the straight flight scheme with lower energy consumption.

AG channel measurements and characteristics analysis in hilly scenarios for 6G UAV communications

As an important part of sixth generation (6G) integrated space-air-ground-sea networks, unmanned aerial vehicle (UAV) communications have aroused great attention and one of its typical application scenarios is the hilly environments. The related UAV air-ground (AG) channel characteristics analysis is crucial for system design and network evaluation of future UAV communications in hilly scenarios. In this paper, a recently conducted channel measurements campaign in a hilly scenario is presented, which is conducted at the center frequencies of 2.585 GHz and 3.5 GHz for different flight trajectories. Based on the measurement data, some key channel characteristics are analyzed, including path loss (PL), shadow fading (SF), Rician K-factor, root mean square (RMS) delay spread (DS), and temporal auto-correlation function (ACF). Finally, the comparison of typical channel characteristics under circular and straight trajectories is given. The related results can provide a theoretical reference for constructing future UAV communication system in hilly scenarios.

Joint Optimization of STAR-RIS Assisted UAV Communication Systems

In this letter, we study the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted unmanned aerial vehicle (UAV) communications. Our goal is to maximize the sum rate of all users by jointly optimizing the STAR-RIS's beamforming vectors, the UAV's trajectory and power allocation. We decompose the formulated non-convex problem into three subproblems and solve them alternately to obtain the solution. Simulations show that: 1) the STAR-RIS achieves a higher sum rate than traditional RIS; 2) to exploit the benefits of STAR-RIS, the UAV's trajectory is closer to STAR-RIS than that of RIS; 3) the energy splitting for reflection and transmission highly depends on the real-time trajectory of UAV.

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.