Unmanned Aerial Vehicle Communication Research Articles

Wideband Low-RCS Step-Type Cavity-Backed Stacked Dielectric Resonator Antenna for Unmanned Aerial Vehicle Communications

This letter explains the design of a cavity-backed stacked dielectric resonator antenna (SDRA) with wider impedance bandwidth (IBW) and improved gain for C-band and S-band applications. The antenna mainly comprises of four dielectric resonators stacked above one another of varying size and shape. Combining the SDRA with step-type cavity and conformal radome forms the final antenna. The antenna provides a wider IBW of 103% (2.07–6.55 GHz), dual-band axial ratio bandwidth of 27.90% (2.38–3.15 GHz) and 16% (5.01–5.88 GHz), respectively. The antenna provides a maximum gain of 9 dBi. The final cavity-backed antenna offers a good radar cross-section reduction ranging from 2–8 GHz for both normal and oblique incidence angles. Hence the proposed SDRA with the dual-layer cavity is applicable for unmanned aerial vehicle communications.

Sum Rate Maximization for Intelligent Reflecting Surface-Assisted UAV-Enabled NOMA Network

In the next-generation network, intelligent reflecting surface (IRS), non-orthogonal multiple access (NOMA), and simultaneous wireless information and power transfer (SWIPT) are promising wireless communication techniques to effectively improve system sum rates. In traditional unmanned aerial vehicles (UAV) communication systems, the sum rate and coverage are greatly affected when there is an occlusion on the direct transmission link. To solve this problem, the IRS technology is introduced to improve the poor channel conditions. However, most of the previous research on IRS-assisted UAV to optimize system sum rate only considers frameworks that utilize the partially joint-combining techniques of IRS, NOMA, and SWIPT. In this paper, in order to further improve the sum rate of the system, we simultaneously integrate IRS, NOMA, and SWIPT technologies and establish a sum rate maximization optimization problem when the direct link is blocked. Then, an alternative optimization (AO) algorithm based on the maximizing system sum rate is proposed to solve the non-convex optimization problem, in which the IRS location and phase, the reflecting amplitude coefficient, UAV forwarding altitude, and power splitting factor are considered. To let the non-convex and non-linear function be transformed into a convex one, we first use an iterative approach to optimize the position of the IRS. After that, an optimization problem is constructed to maximize the system sum rate with the constraints of the IRS phase shifts, successful successive interference cancellation (SIC), maximum transmit power of base station (BS), and UAV. Numerical results show that the proposed algorithm outperforms the traditional orthogonal multiple access (OMA) and algorithms without IRS-assisted links in terms of the system sum rate.

Multiple UAVs collaborative traffic monitoring with intention-based communication

This article proposes an innovative ground traffic management approach that utilizes the mobility, flexibility, and collaborative capabilities of multiple Unmanned Aerial Vehicles (UAVs). The objective is to enhance the navigation and driving experience of self-driving vehicles by employing UAVs to cover blind areas that cannot be observed by ground monitoring equipment, thereby facilitating the avoidance of congested routes. This study focuses on determining the optimal number of UAVs and enhancing the scheduling strategy and task assignment method by introducing novel UAV communication collaboration techniques. The research faces challenges such as a limited quantity of UAVs, rapid response and feedback, variations in traffic conditions and tasks, and system complexity. Furthermore, the difficulty of communication collaboration is exacerbated by the incomplete connectivity of UAV networks. To address these challenges, this article introduces a method that utilizes intention information as the message for communication between UAVs, thereby enhancing their collaborative capabilities. Using the Multi-Agent Reinforcement Learning (MARL) neural network, this method generates intention information and utilizes the intention information generated by other UAVs to optimize the flight control and traffic monitoring task allocation strategy. This method effectively addresses the challenge of UAV collaboration in efficiently managing a large volume of stochastic traffic monitoring tasks. Experimental results demonstrate that the proposed approach significantly improves vehicle navigation accuracy and achieves a more balanced distribution of workload among multiple UAVs. Moreover, by sharing road conditions detected in the cloud, it reduces operational costs and opens up possibilities for the practical implementation of UAVs in traffic management.

A Deep Learning-Based Multi-Signal Radio Spectrum Monitoring Method for UAV Communication

Unmanned aerial vehicles (UAVs), relying on wireless communication, are inevitably influenced by the complex electromagnetic environment, attributed to the development of wireless communication technology. The modulation information of signals can assist in identifying device information and interference in the environment, which is significant for UAV communication environment monitoring. Therefore, in scenarios involving the communication of UAVs, it is necessary to find out how to perform the spectrum monitoring method to obtain the modulation information. Most existing methods are unsuitable for scenarios where multiple signals appear in the same spectrum sequence or do not use an end-to-end structure. Firstly, we established a spectrum dataset to simulate the UAV communication environment and developed a label method. Then, detection networks were employed to extract the presence and location information of signals in the spectrum. Finally, decision-level fusion was used to combine the output results of multiple nodes. Five modulation types, including ASK, FSK, 16QAM, DSB-SC, and SSB, were used to simulate different signal sources in the communication environment. Accuracy, recall, and F1 score were used as evaluation metrics. The networks were tested at different signal-to-noise ratios (SNRs). Among the different modulation types, FSK exhibits the most stable recognition performance across different models. The proposed method is of great significance for wireless radio spectrum monitoring in complex electromagnetic environments and is adaptable to scenarios where multiple receivers are used in vast terrains, providing a deep learning-based approach to radio monitoring solutions for UAV communication.

Cross-Platform UAV Swarm Key Management in Denied Environments

As resources provided by single unmanned aerial vehicles (UAVs) are limited, we propose a cross-platform UAV swarm key management scheme for task scenarios in denied environments. In denied environments where the communication link is open and the UAV nodes may go invalid, secure communication is often at stake. To solve this problem, we propose a key management scheme which, based on the Chinese remainder theorem (CRT) and the Hash function, constructs a swarm key by combining the local key and the session key to reduce the overhead of individual UAV nodes in the swarm. Meanwhile, the swarm head node constructs broadcast messages according to the key update needs, which reduces the overhead of the member nodes, improves the efficiency of key updating, and fulfills the key establishment and updating of the UAV swarm. Experiments show that our proposed scheme has forward and backward security and can defend against collusion attacks and replay attacks; our method was compared with other methods on the MIRACL cryptographic library in Visual Studio 2019, and it was found that our method has a lower computing and communication overhead, provides a solution to cross-platform key management of UAV swarms in denied environments, and ensures safe communication of UAVs in the swarm.

A Novel Geometry-Based 3-D Wideband Channel Model and Capacity Analysis for IRS-Assisted UAV Communication Systems

Intelligent reflecting surface (IRS) composed of a large number of low-cost passive reflecting elements has attracted significant attention from communication communities because of its ability to substantially improve the communication performance. In this paper, the aperture area and radiation pattern of the IRS reflecting elements are considered, and a novel geometry-based three-dimensional (3-D) wideband channel model is proposed for IRS-assisted unmanned aerial vehicle (UAV) communication systems. In the proposed model, the reflection phase is designed by jointly considering the aperture area of the IRS reflecting element and the propagation phases among UAV, IRS, and receiver (Rx), each IRS reflecting element is modeled as an anomalous reflector instead of a specular reflector, and large-scale IRS reflecting elements can jointly beamform the signal in a desired direction. Based on the proposed model, the effects of arbitrary trajectory of UAV and the number and the size of passive reflecting elements on channel statistical characteristics are considered, and the average received signal power and the ergodic sum capacity, which consider the impacts of the number and the size of passive reflecting elements, are also investigated. Furthermore, the path loss of the IRS-assisted link, which is in inverse proportion to the square of aperture area of IRS reflecting elements, is derived, and it coincides with the measured results in real outdoor scenarios. Analysis shows that the communication performance can be enhanced by increasing the number and the size of IRS reflecting elements, and it is validated by numerical results and Monte-Carlo simulation results.

Energy Efficient Resource Allocation for UAV-Served Energy Harvesting-Supported Cognitive Industrial M2M Networks

In this letter, the resource allocation problem for Energy Harvesting-supported Cognitive Industrial Machine-to-Machine (EH-CI-M2M) network underlaying Unmanned Aerial Vehicles (UAVs) communication is investigated with the objective of maximizing the average energy efficiency by jointly considering the EH time slot assignment, transmit power control and bandwidth allocation under the constraints of Quality of Service (QoS) and the available energy status of the EH-CMNet devices. Nevertheless, the optimization problem is difficult to be tackled directly since it is non-convex and NP-hard. We first transform the primitive objective problem into a convex form equivalently by non-linear fractional programming and variable relaxation approach. Based on Dinkelbach and Lagrangian theory, an iterative algorithm is proposed to solve the optimization problem. Extensive simulation results demonstrate that the proposed scheme outperforms the benchmark schemes in terms of energy efficiency in different network settings.

Multi-Objective Optimization Approaches for Physical Layer Secure Communications Based on Collaborative Beamforming in UAV Networks

Unmanned aerial vehicle (UAV) communications and networks are promising technologies in the forthcoming 5G/6G wireless communications. However, they have challenges for realizing secure communications. In this paper, we consider to construct a virtual antenna array consists UAV elements and use collaborative beamforming (CB) to achieve the UAV secure communications with different base stations (BSs), subject to the known and unknown eavesdroppers on the ground. To achieve a better secure performance, the UAV elements can fly to optimal positions with optimal excitation current weights for performing CB transmissions. However, this leads to extra motion energy consumption. We formulate a physical layer secure communication multi-objective optimization problem (MOP) of UAV networks to simultaneously improve the total secrecy rates, total maximum sidelobe level (SLL) and total motion energy consumption of UAVs by jointly optimizing the positions and excitation current weights of UAVs, and the order of communicating with different BSs. Due to the complexity and NP-hardness of the formulated MOP, we propose an improved multi-objective dragonfly algorithm with chaotic solution initialization and hybrid solution update operators (IMODACH) and a parallel-IMODACH (P-IMODACH) to solve the problem. Simulation results verify that the proposed approaches can effectively solve the formulated MOP and it has better performance than some other benchmark algorithms and approaches. Moreover, some unexpected circumstances are considered and discussed.

Idle-Less Slotted ALOHA Protocol for Drone Swarm Identification

In this paper, we modify the slotted ALOHA protocol to quickly identify a swarm of drones in unmanned aerial vehicle (UAV) communication environments. Considering the unreliable UAV channel and the fundamental tradeoff between collision and idle slots in slotted ALOHA, we propose an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">idle-less slotted ALOHA</i> (ILS-ALOHA) protocol that detects and utilizes idle slots to transmit control messages to drones. In ILS-ALOHA, the ground control station recognizes the idle slot promptly at the beginning of each slot and then transmits acknowledgment (ACK) and beacon messages during the remaining period of the idle slot. Therefore, it can control the access of the drone swarm more adaptively and feed back the ACK to the drones more frequently in an error-prone UAV environment without allocating additional slots. The analysis and simulation results show that the proposed ILS-ALOHA protocol outperforms the conventional slotted ALOHA-based protocols in terms of identification time, channel throughput, and probability of successful identification.

A Novel 3D Beam Domain Channel Model for UAV Massive MIMO Communications

Due to the agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications in the next-generation wireless networks. Considering the massive multiple-input multiple-output (MIMO) configuration, this paper proposes a novel three-dimensional (3D) beam domain channel model (BDCM) for UAV communications. Through dividing the large antenna array into several sub-arrays and classifying multipath components as near-field and far-field components, the proposed BDCM takes the spherical wave front (SWF) and array non-stationarity into account. Channel statistical properties including spatial-temporal-frequency correlation function (STF-CF), root-mean-squared (RMS) Doppler spread, beam spread, channel matrix collinearity (CMC), and stationary time interval are derived and simulated for the proposed BDCM. Influences of SFW and non-stationary properties on the statistical properties and system performance are analyzed. Simulation results show that, compared with the equivalent geometry-based stochastic model (GBSM), the proposed BDCM has better temporal correlation, while BDCM and GBSM are equivalent in the system performance evaluation. Furthermore, the performance of the proposed BDCM is evaluated in terms of accuracy, complexity, and pervasiveness. The results show that the proposed BDCM can represent massive MIMO channel properties accurately with low complexity and good compatibility.

Capacity Analysis of UAV Communications Under the Non-Ideal Transceiver Effects

Unmanned aerial vehicles (UAVs) have emerged as promising solutions to overcome the challenges that face traditional terrestrial communication. Not only are they reliable and cost-effective, but they can also be considered green strategies with inherent benefits such as diversity, flexibility, and altitude adaptability. This work analyses the effects of hardware impairments (HWIs) on the UAVs and the ground station (GS) communication system where the UAV moves in a random three-dimensional trajectory. In this regard, the average ergodic capacity of the system is derived by considering the Rician fading channel conditions between the UAV and the GS. We consider the average over a random three-dimensional trajectory movement including the angle of arrival and the distance between the UAV and the GS. We also provide an asymptotic analysis when the transmit power of the UAV and the number of GS antennas become exceptionally large. Extensive MATLAB simulations are provided to validate the gained results.

THz-Enabled UAV Communications Using Non-Orthogonal Multiple Access

The fact that integrating the unmanned aerial vehicles (UAV) newfangled applications in the envisioned next generation (nextG) necessitates a considerable high throughput, makes terahertz (THz) band an emerging key enabler to support stringent requirements in the nextG wireless ecosystem. To fully utilize the potential of a synergy of UAV with THz frequency band, a THz-enabled UAV communication is scrutinized in this paper, where a single antenna UAV is serving the ground users by incorporating non-orthogonal multiple access (NOMA) scheme. Our objective is to maximize the total rate of all ground users by jointly optimizing bandwidth allocation, power allocation, and UAV deployment in THz communication systems. The formulated max-sum rate optimization problem is non-convex, which makes it hard to solve optimally. Therefore, to cope with the aforementioned puzzle, we adopt an inner convex approximation-based path-following approach that considers a Joint Optimization of Power, Bandwidth allocation, and UAV's Deployment (JOPBD) by utilizing NOMA scheme. Furthermore, a comparison of our proposed JOPBD algorithm, with the orthogonal multiple access, and other baseline schemes is conducted. Then, the numerical simulations are carried out to reveal the impact of our proposed approach, and also to confirm its significance for the UAV operating in THz band.

Study of Power and Trajectory Optimization in UAV Systems Regarding THz Band Communications with Different Fading Channels

Researchers are interested in unmanned aerial vehicles (UAVs) because they have many uses in current 5G and future 6G networks and are safer than human-operated vehicles. Terahertz (THz)-band communications are a possible option in 6G for fast communication. THz wireless communication between UAVs is taken into consideration in this work. Different fading channels, which are significant influencing factors in THz communication channel modeling, are used to analyze the performance of UAV communications in THz networks. Consideration must be made to the structure, wireless channel parameters, and transmission characteristics when evaluating the performance of wireless technology. Nakagami-m, Rician, Weibull, and Rayleigh fading channels are all taken into consideration, along with log-normal fading. Moreover, an optimization algorithm for the THz channel is presented, which is meant to minimize transmission power by optimizing the trajectory of uplink and downlink transmissions between the UAV and users. The equations of the UAV locations and the transmission power optimization of each user are derived. Analytical formulations regarding capacity, outage probability, and bit error rate (BER) are generated when performance-influencing factors are taken into account. The analytical analysis is supported by the numerical results that are provided.

Reliability analysis for NOMA‐based UAV assisted short packet communication

AbstractThis paper introduces the short packet transmission in non‐orthogonal multiple access (NOMA)‐based unmanned aerial vehicle (UAV) assisted relay communication system. Short packet transmission has considerable potential to decrease the transmission latency of UAV communication, and NOMA can effectively enhance the spectrum efficiency and fairness. To improve the reliability of the system, an effective packet error rate (PER) minimization problem within short packet transmission is proposed by jointly optimizing the packet length, UAV placement, and power allocation under the reliability requirement and total power constraints. To address the intricate PER minimization problem, the optimization problem is firstly decomposed into three sub‐problems, and the corresponding monotonicity and convexity are analyzed, respectively. Then, an overall iterative optimization algorithm for PER minimization based on alternating direction method of multipliers algorithm and optimal solution algorithm is formulated by solving the three sub‐problems in an iterative manner. Simulation results validate the effectiveness and convergence of the proposed NOMA scheme and overall iterative optimization algorithm, respectively.

Image Splicing Compression Algorithm Based on the Extended Kalman Filter for Unmanned Aerial Vehicles Communication

The technology of unmanned aerial vehicles (UAVs) is extensively utilized across various domains due to its exceptional mobility, cost-effectiveness, rapid deployment, and flexible configuration. However, its limited storage space and the contradiction between the large amount of image data returned and the limited bandwidth in emergency scenarios necessitate the development of a suitable UAVs image compression technology. In this research, we propose an image splicing compression algorithm based on the extended Kalman filter for UAV communication (SEKF-UC). Firstly, we introduce the concept of image splicing to enhance the compression ratio while ensuring image quality, leveraging the characteristics of deep learning-based compression, which are relatively unaffected by the size of the compressed data. Secondly, to address the issue of slow processing speed of deep neural networks (DNN) caused by the substantial volume of data involved in image splicing, we incorporate an extended Kalman filter module to expedite the process. Simulation experiments show that our proposed algorithm outperforms existing methods in multiple ways, achieving a significant compression ratio improvement, from 2:1 to 25:1, with a marginal reduction of 6.5% in structural similarity (SSIM) compared to the non-spliced approach. Moreover, for deep neural networks (DNN), our method incorporating the extended Kalman filter module achieves the same error level with only 30 iterations—a significant reduction compared to the traditional BP algorithm’s requirement of over 4000 iterations—while improving the average network operation speed by an impressive 89.76%. Additionally, our algorithm excels in image quality, with peak signal-to-noise ratio (PSNR) improving by 92.7% and SSIM by 42.1%, at most, compared to other algorithms. These results establish our algorithm as a highly efficient and effective solution, suitable for various image processing and data compression applications.

Computer Vision-Aided mmWave UAV Communication Systems

Unmanned aerial vehicle (UAV) communication systems usually operate in harsh scenarios, which require accurate information about the topology and wireless channel to achieve the desired transmission performance. Therefore, when millimeter wave (mmWave) communication with its intrinsic line-of-sight (LoS) condition is adopted, accurate target localization is essential to determine the spatial relationship between the UAV and the grounded receivers (Rxs). In this paper, a computer-vision (CV)-aided jointly optimization scheme of flight trajectory and power allocation is designed for mmWave UAV communication systems by utilizing the visual information captured via cameras equipped at the UAV. Compared with traditional schemes, the implementation cost and overhead can be greatly saved as no radio frequency transmissions are required in the proposed localization scheme. In addition, the transmit power at the UAV is jointly optimized with its flight trajectory in two different cases. Finally, simulation results are presented to demonstrate the efficiency of the proposed schemes.

A Novel UAV-Assisted Multi-Mobility Channel Model for Urban Transportation Emergency Communications

With the increasing requirements for unmanned aerial vehicle (UAV) communication in various application scenarios, the UAV-assisted emergency communication in urban transportation scenario has received great attention. In this paper, a novel UAV-assisted UAV-to-vehicle (U2V) geometry-based stochastic model (GBSM) for the urban traffic communication scenario is proposed. The three-dimensional (3D) multi-mobilities of the transmitter (Tx), receiver (Rx), and clusters are considered by introducing the time-variant acceleration and velocity correspondingly. The velocity variation of the clusters is used to simulate the motion of vehicles around the Rx. Moreover, to describe the vehicles’ moving states, Markov chain is adopted to analyze the changes in cluster motion states, including survival, death, dynamic, and static states. By adjusting the scenario-specific parameters, such as the vehicle density (ρ) and dynamic–static ratio (Ω), the model can support various urban traffic scenarios. Based on the proposed model, several key statistical properties, namely the root mean square (RMS) delay spread, temporal autocorrelation function (ACF), level-crossing rate (LCR), power delay profile (PDP), and stationary interval, under different clusters and antenna accelerations are obtained and analyzed. The accuracy of the proposed model is verified by the measured data. The results demonstrate the usability of our model, which can be provided as a reference for the design, evaluation, and optimization of future communication networks between UAV and vehicles in urban transportation emergency communications.

Intelligent deep learning‐aided future beam and proactive handoff prediction model in Unmanned Aerial Vehicle‐assisted anti‐jamming Terahertz communication system

SummaryWireless communications often suffer from legitimate transmissions regarding malicious jamming attacks launched through the smart jammer. The drone or unmanned aerial vehicle (UAV) communication networks derived with reconfigurable intelligent surfaces (RIS) increase the issues of beam selection and proactive handoff in terahertz (THz). Thus, a new heuristic strategy is designed for efficient and incorporated optimization of the beamforming vector and anti‐jamming transmit power allocation in undefined environments. Here, the transmit power allocation and beamforming matrix of UAV are optimized with the developed hybrid heuristic algorithm of the Hybrid Crow Black Widow Search Optimization (HCBWSO) algorithm for maximizing the system achievable rate. Here, the HCBWSO algorithm is implemented to integrate with the Crow search algorithm (CSO) and Black Widow Optimization (BWO). The second contribution is to adopt RIS into THz–UAV communications, a new Enhanced Deep Temporal Convolutional Network (EDTCN) for predicting the future beam and proactive handoff of UAVs based on their prior analysis of the UAV locations, where the HCBWSO algorithm is utilized for recommending EDTCN. Here, the training of the EDTCN needs to be done with the collection of UAV information from the DEEPMIMO dataset for predicting the future beams and, also, tracking the location of the UAV. EDTCN helps in increasing the possibility of expanding the UAV coverage and also increases the consistency of the THz communication system. Thus, the prediction of the future beam increases the coverage area of the UAV and also maximizes the system rate in the THz communication system.

Research on Identity Authentication Scheme for UAV Communication Network

Unmanned aerial vehicles (UAV) play a vital role in many fields, such as agricultural planting, security patrol, emergency rescue, and so on. The development and implementation of these devices have become vital in terms of reachability and usability. Unfortunately, as drones become more widely used in various fields, they become more and more vulnerable to attacks and security threats, including, but not limited to, eavesdropping, man-in-the-middle attacks, and known session key attacks. In order to deal with these attacks and security threats and meet the needs of lightweight UAV communication, a secure and efficient authentication scheme is essential. To meet the security and lightweight requirements of an identity authentication scheme in a UAV communication network, this paper proposes an identity authentication scheme sdronelig based on an elliptic curve cryptosystem. The scheme realizes the mutual authentication and session key agreement configuration between the UAV and the ground station, and the authentication and key agreement between the UAVs can be realized with the help of the control station. The sdronelig authentication scheme is based on the ECDH key exchange protocol in the elliptic curve cryptography algorithm and adopts the MAC message authentication code technology and the method of pre-calculating part of the process. Under the premise of ensuring the security of the UAV communication network, the authentication efficiency is improved, the communication overhead and communication times are reduced, and the lightweight requirement of the UAV authentication scheme is met. Additionally, a formal verification tool is used to verify the security of the sdronelig scheme under the Dolev-Yao threat model, which is suitable for UAV networks. Finally, a detailed comparative study was conducted on security features, communication overhead, the number of communications, and computational overhead. The results show that the proposed sdronelig authentication scheme not only provides superior security features but also has better or comparable overhead compared to other existing authentication schemes.

Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization

Compared to the distributed ground-based radar (DGBR), the distributed airborne radar (DAR) has been widely applied due to its stronger anti-damage ability, more degrees of freedom, and better detection view of targets. However, unlike DGBR, the premise for the normal operation of DAR is to maintain stable wireless communication between unmanned aerial vehicles (UAVs). This requires each UAV to make reasonable use of its electromagnetic domain resources. That is, to maximize radar detection performance while ensuring communication performance constraints. However, current research in the field of radar resource allocation has not taken this into account, which greatly limits the practical application of optimization algorithms. Moreover, the current research tends to adopt centralized optimization algorithms. When the baseline of the UAV swarm is long, applying multi-relay methods directly results in heavy communications overhead and long-time delay. Based on the above background, this article aimed to develop a fully distributed algorithm for the joint optimization of radar detection performance and communication transmission performance. This study first took the measurement angle of arrival (AOA) as an example to provide a system model with communication constraints. This model considers the impact of factors such as the UAV location error, UAV communication coverage, and dynamic communication topology of the UAV on joint optimization. A formal representation of the joint optimization is presented. Then, we proposed a joint radar-communication optimization (JRCO) algorithm to fully utilize the electromagnetic domain resources of each UAV. Finally, numerical simulations verified the effectiveness of the proposed JRCO algorithm to traditional radar resource allocation methods.