Unmanned Aerial Vehicle Communication Research Articles

Measurement-Based Tapped Delay Line Channel Modeling for Fixed-Wing Unmanned Aerial Vehicle Air-to-Ground Communications at S-Band

Fixed-wing unmanned aerial vehicles (UAVs) are widely considered as a vital candidate of aerial base station in beyond Fifth Generation (B5G) systems. Accurate knowledge of air-to-ground (A2G) wireless propagation is important for A2G communication system development and testing where, however, there is still a lack of A2G wideband channel models for such a purpose. In this paper, we present a wideband fixed-wing UAV-based A2G channel measurement campaign at 2.7 GHz, and consider typical flight phases, based on which a wide-sense stationary uncorrelated scattering (WSSUS)-based tapped delay line (TDL) wideband channel model is proposed. Parameters of individual channel taps are analyzed in terms of gain, amplitude distribution, Rice factor and delay-Doppler spectrum. It is shown that UAV flight phases significantly influence the channel tap parameters. Particularly, the “Bell”-type spectrum is found to be the most suitable model for the delay-Doppler spectrum under various flight scenarios for A2G propagation. The proposed channel model can provide valuable assistance and guidance for UAV communication system evaluation and network planning.

Joint trajectory and beamforming optimization for UAV secure communications with passive coupled phase-shift/active STAR-RIS

Recently, the concept of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has been proposed to achieve a full-space (i.e., 360∘) reconfigurable wireless environment. By exploiting the capability of manipulating signal propagation in both transmission and reflection spaces, the STAR-RIS possesses great potential to boost the security in unmanned aerial vehicle (UAV) communications. In this paper, the trajectory and beamforming optimization are investigated for secure transmission in STAR-RIS-assisted UAV communication systems. Unlike existing studies on the STAR-RIS that mostly focus on the passive ideal model, we consider the more practical passive coupled phase-shift model and the active model of the STAR-RIS. Under the multi-user multi-eavesdropper scenario, we aim to maximize the minimum average secrecy rate by jointly optimizing the UAV-base station's (UAV-BS's) beamforming, UAV's trajectory, and the STAR-RIS's transmission and reflection (T&R) coefficients. To tackle the non-convex optimization problem, the original problem is decomposed into three subproblems, and efficient alternating algorithms are proposed by leveraging the semi-definite relaxation, the successive convex approximation, and penalty-based approaches. Simulation results show that: 1) the practical coupled phase-shift model of passive STAR-RIS suffers a security performance degradation compared with the ideal independent phase-shift model, but it still outperforms the conventional RIS; 2) the active STAR-RIS with sufficient power budget outperforms its passive counterpart, while behaves slightly worse under unreasonable power budget; 3) the optimization of T&R coefficients drives the UAV's trajectory closer to the STAR-RIS, and the amplitudes of T&R coefficients highly depend on the UAV's trajectory in order to ensure the secrecy rate fairness.

Hybrid deep learning with pelican optimization algorithm for M2M communication on UAV image classification

<p>Machine-to-machine (M2M) communication for unmanned aerial vehicle (UAVs) and image classification is essential to current remote sensing and data processing. UAVs and ground stations or other linked devices exchange information seamlessly using M2M communication. M2M connectivity helps UAVs with cameras and sensors communicate aerial pictures in real time or post-mission for image categorization and analysis. During flight, UAVs acquire massive volumes of picture data. Image classification, commonly using deep learning (DL) methods like convolutional neural network (CNN), automatically categorizes and annotates photos based on predetermined classes or attributes. This work uses UAV photos to produce hybrid deep learning with pelican optimization algorithm for M2M communication (HDLPOA-M2MC). HDLPOA-M2MC automates UAV picture class identification. GhostNet model is used to derive features in HDLPOA-M2MC. The HDLPOA-M2MC approach leverages pelican optimization algorithm (POA) for hyperparameter adjustment in this investigation. Finally, autoencoder-deep belief network (AE-DBN) model can classify. The HDLPOA-M2MC method’s enhanced outcomes were shown by several studies. The complete results showed that HDLPOA M2MC performed better across measures.</p>

Multi-Node Joint Jamming Scheme for Secure UAV-Aided NOMA-CDRT Systems: Performance Analysis and Optimization

Unmanned aerial vehicle (UAV) communication using non-orthogonal multiple access-based coordinated direct and relay transmission (NOMA-CDRT) supports both massive connectivity and wide-area coverage, becoming a key technology for future emergency rescue communications. However, relay forwarding and high-quality line-of-sight links may subject UAV-aided NOMA-CDRT to multiple eavesdropping attempts by saboteurs. Therefore, we propose a multi-node joint jamming scheme using artificial noise (AN) for the UAV-assisted NOMA-CDRT to improve the system’s physical layer security. In the proposed scheme, the base station directly serves a nearby user while using a UAV relay to serve a disaster-affected user, and both the users and the UAV relay utilize AN to jointly interfere with eavesdroppers around the users. To accurately characterize and maximize the ergodic secrecy sum rate (ESSR) of the proposed scheme, we derive the corresponding closed-form expressions and design a joint power allocation and interference control (JPAIC) algorithm using particle swarm optimization. Simulations verify the correctness of the theoretical analysis, the ESSR advantage of the proposed scheme compared with the conventional NOMA-CDRT, and the effectiveness of the proposed JPAIC.

Unlicensed Spectrum Access and Performance Analysis for NR-U/WiGig Coexistence in UAV Communication Systems

Unmanned aerial vehicles (UAVs) are extensively employed in pursuit, rescue missions, and agricultural applications. These operations necessitate substantial data and video transmission, requiring significant spectral resources. The unlicensed millimeter wave (mmWave) spectrum, especially in the 60 GHz frequency band, offers promising potential for UAV communications. However, WiGig users are the incumbent users of the 60 GHz unlicensed spectrum. Therefore, to ensure fair coexistence between UAV-based new radio-unlicensed (NR-U) users and WiGig users, unlicensed spectrum-sharing strategies need to be meticulously designed. Due to the beam directionality of the NR-U system, traditional listen-before-talk (LBT) spectrum sensing strategies are no longer effective in NR-U/WiGig systems. To address this, we propose a new cooperative unlicensed spectrum sensing strategy based on mmWave beamforming direction. In this strategy, UAV and WiGig users cooperatively sense the unlicensed spectrum and jointly decide on the access strategy. Our analysis shows that the proposed strategy effectively resolves the hidden and exposed node problems associated with traditional LBT strategies. Furthermore, we consider the sensitivity of mmWave to obstacles and analyze the effects of these obstacles on the spectrum-sharing sensing scheme. We examine the unlicensed spectrum access probability and network throughput under blockage scenarios. Simulation results indicate that although obstacles can attenuate the signal, they positively impact unlicensed spectrum sensing. The presence of obstacles can increase spectrum access probability by about 60% and improve system capacity by about 70%.

Low-profile, low sidelobe array antenna with ultrawide beam coverage for UAV communication

This paper presents a design method to implement an antenna array characterized by ultra-wide beam coverage, low profile, and low Sidelobe Level (SLL) for the application of Unmanned Aerial Vehicle (UAV) air-to-ground communication. The array consists of ten broadside-radiating, ultrawide-beamwidth elements that are cascaded by a central-symmetry series-fed network with tapered currents following Dolph-Chebyshev distribution to provide low SLL. First, an innovative design of end-fire Huygens source antenna that is compatible with metal ground is presented. A low-profile, half-mode Microstrip Patch Antenna (MPA) is utilized to serve as the magnetic dipole and a monopole is utilized to serves as the electric dipole, constructing the compact, end-fire, grounded Huygens source antenna. Then, two opposite-oriented end-fire Huygens source antennas are seamlessly integrated into a single antenna element in the form of monopole-loaded MPA to accomplish the ultrawide, broadside-radiating beam. Particular consideration has been applied into the design of series-fed network as well as antenna element to compensate the adverse coupling effects between elements on the radiation performance. Experiment indicates an ultrawide Half-Power Beamwidth (HPBW) of 161° and a low SLL of −25 dB with a high gain of 12 dBi under a single-layer configuration. The concurrent ultrawide beamwidth and low SLL make it particularly attractive for applications of UAV air-to-ground communication.

Exploiting Cascaded Channel Signature for PHY-Layer Authentication in RIS-Enabled UAV Communication Systems

Reconfigurable Intelligent Surface (RIS)-assisted Unmanned Aerial Vehicle (UAV) communications face a critical security threat from impersonation attacks, where adversaries impersonate legitimate entities to infiltrate networks to obtain private data or unauthorized access. To combat such security threats, this paper proposes a novel physical layer (PHY-layer) authentication scheme for validating UAV identity in RIS-enabled UAV wireless networks. Considering that most existing works focus on traditional communication systems such as IoT and millimeter wave multiple-input multiple-output (MIMO) systems, there is currently no mature PHY-layer authentication scheme to serve RIS-UAV communication systems. To this end, our scheme leverages the unique characteristics of cascaded channels related to RIS to verify the legitimacy of UAV transmitting signals to the base station (BS). To be more precise, we first use the least squares estimate method and coordinate a descent-based algorithm to extract the cascaded channel feature. Next, we explore a quantizer to quantize the fluctuations of the channel gain that are related to the extracted channel feature. The 1-bit quantizer’s output findings are exploited to generate the authentication decision criteria, which are then tested using a binary hypothesis. The statistical signal processing technique is utilized to obtain the analytical formulations for detection and false alarm probabilities. We also conduct a computational complexity analysis of the proposed scheme. Finally, the numerical results validate the effectiveness of the proposed performance metric models and show that our detection performance can reach over 90% accuracy at a low signal-to-noise ratio (e.g., −8 dB), with a 10% improvement in detection accuracy compared with existing schemes.

A Quantum-Resistant Identity Authentication and Key Agreement Scheme for UAV Networks Based on Kyber Algorithm

Unmanned aerial vehicles (UAVs) play a critical role in various fields, including logistics, agriculture, and rescue operations. Effective identity authentication and key agreement schemes are vital for UAV networks to combat threats. Current schemes often employ algorithms like elliptic curve cryptography (ECC) and Rivest–Shamir–Adleman (RSA), which are vulnerable to quantum attacks. To address this issue, we propose LIGKYX, a novel scheme combining the quantum-resistant Kyber algorithm with the hash-based message authentication code (HMAC) for enhanced security and efficiency. This scheme enables the mutual authentication between UAVs and ground stations and supports secure session key establishment protocols. Additionally, it facilitates robust authentication and key agreement among UAVs through control stations, addressing the critical challenge of quantum-resistant security in UAV networks. The proposed LIGKYX scheme operates based on the Kyber algorithm and elliptic curve Diffie–Hellman (ECDH) key exchange protocol, employing the HMAC and pre-computation techniques. Furthermore, a formal verification tool validated the security of LIGKYX under the Dolev–Yao threat model. Comparative analyses on security properties, communication overhead, and computational overhead indicate that LIGKYX not only matches or exceeds existing schemes but also uniquely counters quantum attacks effectively, ensuring the security of UAV communication networks with a lower time overhead for authentication and communication.

Joint 3D trajectory and phase shift optimization via deep reinforcement learning for RIS-assisted UAV communication systems

Unmanned aerial vehicle (UAV) can be deployed as aerial base station to provide communication services for the user equipments (UEs). However, in urban environments, the links between UAV and UEs might be frequently blocked by obstacles, leading to severely adverse effects on the quality of service (QoS) of UEs. Additionally, due to the limited energy of the UAV, it might not always be feasible to re-establish the line-of-sight (LoS) links by frequently adjusting the positions of the UAV. In this context, the reconfigurable intelligent surface (RIS) is utilized to enhance the transmission range of UAV-UE links by reflecting incident signals to UEs. In this paper, we investigate the RIS-assisted UAV communication systems with the goal of maximizing the energy efficiency of the UAV through a joint optimization of the UAV’s trajectory and the RIS’s phase shift. The formulated optimization problem is non-convex, and challenging to solve in a polynomial time. Therefore, an effective deep reinforcement learning (DRL)-based solution, named Dueling DQN-PER is proposed, which combines the Dueling DQN algorithm with the prioritized experience replay (PER) technique. To ensure the fairness among all UEs, we design a service fairness index, and integrate it into the reward function when designing the proposed algorithm. Numerical results demonstrate that: 1) the proposed Dueling DQN-PER algorithm is capable of improving the system energy efficiency and has a better training performance than benchmark schemes; 2) by devising the service fairness index, the fairness among all UEs is ensured while enhancing the system performance in energy efficiency; 3) the RIS-assisted UAV communication systems benefit from significant energy efficiency gain over the systems without RIS.

Enhancing unmanned aerial vehicle communication through distributed ledger and multi-agent deep reinforcement learning for fairness and scalability

Enhancing unmanned aerial vehicle communication through distributed ledger and multi-agent deep reinforcement learning for fairness and scalability

User Scheduling and Path Planning for Reconfigurable Intelligent Surface Assisted MISO UAV Communication

The high mobility of unmanned aerial vehicles (UAVs) enables them to improve system throughput by establishing line-of-sight (LoS) links. Nevertheless, in urban environments, these LoS links can be disrupted by complex urban structures, leading to potential interference issues. Reconfigurable intelligent surfaces (RIS) provide an innovative approach to enhance communication performance by intelligently reflecting incident signals. Recent studies suggest that utilizing multi-antenna transmission can increase system efficiency, while single-antenna transmission may be more prone to interference. To address these challenges, this article introduces a RIS-assisted multiple-input single-output (MISO) UAV communication system. Our objective is to optimize the minimum user rate, thereby guaranteeing equitable communication for all users. Nevertheless, the non-convexity inherent in this optimization problem complicates the pursuit of a direct solution. Hence, we decompose the problem into four subproblems: user scheduling optimization, RIS phase-shift optimization, UAV trajectory optimization, and UAV transmit beamforming optimization. To obtain suboptimal solutions, we have developed an alternating iterative optimization algorithm for addressing the four subproblems. Numerical results demonstrate that our algorithm effectively boosts the minimum user rate of the entire system.

A UAV communication strategy based on user distribution time series prediction

Abstract To maximize the role of unmanned aerial vehicles (UAVs) as mobile relays to assist communication, while meeting the requirements of service quality and energy constraints, it is necessary to fully consider the motion trajectories and spatiotemporal distribution of ground users in UAV wireless communication systems. Therefore, an algorithm that combines Long Short-Term Memory (LSTM) and Gaussian Mixture Model (GMM) is proposed to predict user distribution. The LSTM, with its capability of handling temporal data, enables the UAV to refer to the preceding information during flight decision-making and make action outputs based on predictions of the dynamic environment. Simulation results demonstrate that the proposed approach can accurately predict the position information of mobile users, achieve faster convergence, and obtain better performance in dynamic scenarios. The system performance has improved by 5.8 percent.

A High-gain omnidirectional conformal antenna on the aerial vehicle

Abstract A series-fed conformal microstrip antenna with the tail wing of a UAV(Unmanned Aerial Vehicle)is proposed and implemented in this paper. In the target frequency band, the antenna has a nice performance of omnidirectional radiation and high gain, which easily conforms with the tail wing structure, achieving good wind resistance characteristics and meeting the strict aerodynamic requirements of the UAV. The antenna radiator is etched onto a printed board with a dielectric constant of 2.2 and a size of 644mm×112mm×4mm. In addition, the radiator is fixed with PMI form and formed a tail shape with surrounding glass fiber cloth, which has strong rigidity as a whole and has passed the flight test. The test results show that the antenna achieves a gain of more than 4.2 dBi at 564-676 MHz, a gain of more than 5.6 dBi at 600-650 MHz, and a gain of less than 3dB at 564-676MHz. This proposed antenna has a reasonable structure and good performance, which is verified by engineering experiments and is suitable for harsh environment requirements. It is expected that this antenna has wide applications in high-speed UAV communication.

Network simulation tools for unmanned aerial vehicle communications: A survey

SummaryUnmanned aerial vehicle (UAV) communication has been proposed as an effective solution in both military and civilian scenarios, with low cost, high efficiency, high flexibility, and on‐demand deployment. Network simulation is an economically efficient method for validating new ideas in UAV communication. While some simulation tools have been proposed for UAV communications, there is a lack of a state‐of‐the‐art review to guide newcomers in this research field. In existing surveys, the discussion of network simulators is not comprehensive and the content discussed is outdated. There has also been no discussion of open‐source UAV simulation tools. To fill these research gaps, this survey presents an updated and comprehensive survey of UAV network simulation tools, including a unique collection of open‐source tools. Research challenges and opportunities have been proposed to inspire follow‐up studies.

Full-Duplex Unmanned Aerial Vehicle Communications for Cellular Spectral Efficiency Enhancement Utilizing Device-to-Device Underlaying Structure

Unmanned aerial vehicle (UAV) communications have gained recognition as a promising technology due to their unique characteristics of rapid deployment and flexible configuration. Meanwhile, device-to-device (D2D) and full-duplex (FD) technologies have emerged as promising methods for enhancing spectral efficiency and offloading traffic. One significant advantage of UAVs is their ability to partition suitable D2D pairs to increase cell capacity. In this paper, we present a novel network model in which UAVs are considered D2D pairs underlaying cellular networks, integrating FD into the communication links between UAVs to improve spectral efficiency. We then investigate a resource allocation problem for the proposed FD-UAV D2D underlaying structure model, with the objective of maximizing the system’s sum rate. Specifically, the UAVs in our model operate in full-duplex mode as D2D users (DUs), allowing the reuse of both the uplink and downlink subcarrier resources of cellular users (CUs). This optimization challenge is formulated as a mixed-integer nonlinear programming problem, known for its NP-hard and intractable nature. To address this issue, we propose a heuristic algorithm (HA) that decomposes the problem into two steps: power allocation and user pairing. The optimal power allocation is solved as a nonlinear programming problem by searching among a finite set, while the user pairing problem is addressed using the Kuhn–Munkres algorithm. The numerical results indicate that our proposed FD-MaxSumCell-HA (full-duplex UAVs maximizing the cell sum rate with a heuristic algorithm) scheme for FD-UAV D2D underlaying models outperforms HD-UAV underlaying cellular networks, with improved access rates for UAVs in FD-MaxSumCell-HA compared to HD-UAV networks.

Blocklength Allocation and Power Control in UAV-Assisted URLLC System via Multi-agent Deep Reinforcement Learning

Integration of unmanned aerial vehicles (UAVs) with ultra-reliable and low-latency communication (URLLC) systems can improve the real-time communication performance for various industrial internet of things (IIoT) applications. Designing an intelligent resource allocation system is one of the challenges posed by an energy-constrained UAV communication system. Therefore, we formulate a sum rate maximization problem, subject to the UAVs’ energy by optimizing the blocklength allocation and the power control jointly in the uplink UAV-assisted URLLC systems, in which the probabilistic channel model between UAV and users is adopted. The problem is difficult to solve due to the non-convex objective function and the energy constraints, and also challenging to make fast decision in the complex communication environment. Thus, we propose a deep reinforcement learning (DRL)-based scheme to optimize the blocklength allocation and power control jointly. First, transform the original problem into the multi-agent reinforcement learning process, where each subcarrier is regarded as the agent that optimizes its individual blocklength allocation and power control. Then, each agent makes the intelligent decision to obtain the maximum reward value depending on the weighted segmented reward function, which is related to the UAV energy consumption and user rates to improve the rate performance. Finally, the simulation results show that the proposed scheme outperforms the benchmark schemes and has the stable convergence in different settings, such as the learning rate, the error probability, the subcarrier spacing, and the number of users.

IRS Integrated UAV Communication to Enhance Physical Layer Security: A Deep Reinforcement Learning approach

The paper investigates the security of an intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) network, where a base station (BS) transmits confidential information to the ground user (GU) via IRS-assisted UAV. The study explores using UAVs with IRS to enhance the security and reliability of wireless communication systems, particularly in the presence of eavesdroppers and friendly jammers. The beamforming at IRS-assisted UAV and UAV trajectory are jointly formulated as a non-convex optimization problem, which is solved by the deep reinforcement learning (DRL) algorithm to maximize the sum secrecy rate of GU with the aid of jammer. We proposed a dual-DDPG (D3PG) algorithm that utilizes the deep deterministic policy gradient (DDPG) structure to effectively address these dual non-convex problems of the UAV-trajectory optimization and the UAV-IRS beamforming optimization. The proposed algorithm's effectiveness and robustness are demonstrated through simulation results, with the IRS significantly enhancing the sum secrecy rate. Extensive simulations show that the proposed DRL-based D3PG scheme outperforms the traditional optimization schemes and ordinary DQN schemes.

Technical Loopholes and Trends in Unmanned Aerial Vehicle Communication Systems

Technical Loopholes and Trends in Unmanned Aerial Vehicle Communication Systems

Deep Reinforcement Learning-Based 3D Trajectory Planning for Cellular Connected UAV

To address the issue of limited application scenarios associated with connectivity assurance based on two-dimensional (2D) trajectory planning, this paper proposes an improved deep reinforcement learning (DRL) -based three-dimensional (3D) trajectory planning method for cellular unmanned aerial vehicles (UAVs) communication. By considering the 3D space environment and integrating factors such as UAV mission completion time and connectivity, we develop an objective function for path optimization and utilize the advanced dueling double deep Q network (D3QN) to optimize it. Additionally, we introduce the prioritized experience replay (PER) mechanism to enhance learning efficiency and expedite convergence. In order to further aid in trajectory planning, our method incorporates a simultaneous navigation and radio mapping (SNARM) framework that generates simulated 3D radio maps and simulates flight processes by utilizing measurement signals from the UAV during flight, thereby reducing actual flight costs. The simulation results demonstrate that the proposed approach effectively enable UAVs to avoid weak coverage regions in space, thereby reducing the weighted sum of flight time and expected interruption time.

A Novel UAV Air-to-Air Channel Model Incorporating the Effect of UAV Vibrations and Diffuse Scattering

In this paper, we propose a geometric channel model for air-to-air (A2A) unmanned aerial vehicle (UAV) communication scenarios. The model is established by incorporating line-of-sight, specular reflection, and diffuse scattering components, and it can capture the impacts of UAV vibrations induced by the propeller’s rotation. Based on UAV heights and ground scatterer density, a closed-form expression is derived to jointly capture the zenith and azimuth angular distributions of diffuse rays. The power of diffuse rays is modeled according to the grazing angle of the rays and the electrical properties and roughness of the ground materials. Key statistics, including the temporal autocorrelation function, spatial cross-correlation function, Doppler power spectrum density, and coherence time are derived, providing an in-depth understanding of the time-variant characteristics of the channel. The results indicate that the presented model is capable of capturing certain A2A channel characteristics, which align with the corresponding theoretical analysis. The findings suggest that the scattering effect of the A2A channel is significantly influenced by the altitude of the UAV. Additionally, it is shown that UAV vibrations can introduce extra Doppler frequencies, notably decreasing the temporal correlation and coherence time of the channel. This effect is more prominent when the system operates at high-frequency bands. The effectiveness of the presented model is confirmed through a comparison of its statistics with those of an existing model and with available measurement data.