Unmanned Aerial Vehicle Wireless Research Articles

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.

Investigations on OFDM UAV-based free-space optical transmission system with scintillation mitigation for optical wireless communication-to-ground links in atmospheric turbulence

The potential integration of unmanned aerial vehicles (UAVs) with free space optical (FSO) communication systems stands as a promising innovation in the realm of wireless network infrastructures. This study presents a comprehensive investigation into the application of orthogonal frequency division multiplexing (OFDM) in conjunction with UAV-based FSO technology, with a specific focus on establishing robust wireless communication links to ground sites within the evolving landscape of 5G networks. The research introduces a pioneering 4-level quadrature amplitude modulation (4-QAM)-OFDM-FSO framework tailored for UAV-to-ground communication, revolutionizing the prospects for seamless and high-throughput data transmission within dynamic network environments. Through comprehensive simulations and theoretical analyses, we unveil the system's efficacy in mitigating atmospheric turbulence, achieving heightened signal integrity, and ensuring performance adaptability over varying link distances, thus significantly addressing present limitations in traditional wireless communication models. Anchored within the context of modern wireless network infrastructures, this work serves as a crucial stepping stone for the practical application of OFDM-UAV-FSO communication systems, representing a paradigm shift in fostering resilient and agile wireless connectivity in the era of 5G networks. The inception of cutting-edge wireless networks expected to outperform the capabilities of 5G necessitates an infrastructure that can handle vast amounts of data. This infrastructure must be not only cost-effective and simple to deploy but also readily scalable to accommodate the diverse demands of front-haul and backhaul applications. Motivated by the growing interest in harnessing UAVs to extend the reach and enhance the operational efficacy of conventional cellular networks, this work introduces a novel application of UAV-ground station connections. The concept employs FSO to facilitate network traffic within both the segments. To optimize throughput, resilience, and spectral efficiency, the application of OFDM is proposed. The research considers the transmission of a 20 Gbps 4-QAM data signal across various channel conditions. It thoroughly assesses the performance implications of factors such as transmission distance and beam divergence. The study explores the correlation between pointing error, scintillation, beam divergence angle, and average spectral efficiency. A slight increase in pointing error results in a rapid rise in the scintillation index, while a larger beam divergence angle can help minimize the impact of scintillation. Adapting the beam's divergence angle based on the pointing error between the optical transceivers can reduce the effects of scintillation and improve the average spectral efficiency and channel capacity. Additionally, the relationship between pointing error, scintillation, and the determination of the optical beam divergence angle in terms of beam divergence and average spectral efficiency and channel capacity is examined, and theoretical evaluations further confirm the method's effectiveness in reducing scintillation in the presence of pointing errors. Furthermore, the simultaneous use of OFDM adaptive beam divergence control and modulation could significantly enhance the data rate. This approach aims to reduce the impact of scintillation in UAV FSO links, which often experience significant losses due to unpredictable fluctuations in the atmosphere's refractive index. The results of the simulations indicate that the integrated 4-QAM-OFDM-FSO framework can realize high data transmission rates, efficiently serving front-haul and backhaul needs, thereby signifying a significant evolutionary leap for the next generation of wireless technology. The numerical findings demonstrate the significant impact of the coherent FSO OFDM optical wireless communication (OWC) setup in UAV wireless communications to ground links, particularly in mitigating the effects of strong turbulence and pointing errors (PEs). Through the integration of spatial coherence diversity and adaptive modulation OFDM in the coherent OWC, there has been a noticeable enhancement in the average spectral efficiency (ASE). Notably, our results indicate an ASE of 53 bits/s/Hz and 37 bits/s/Hz achieved at an average transmitted optical power of 10 dBm for an aperture diameter of 10 cm, without and with PEs for the coherent OWC-FSO OFDM UAV technique, respectively. The proposed method was validated through simulations, demonstrating both improved average spectral efficiency and effective reduction of the scintillation effect. This approach holds promise for mitigating scintillation effects in UAV-FSO links.

Joint User Association and Power Control in UAV Network: A Graph Theoretic Approach

Unmanned aerial vehicles (UAVs) have recently been widely employed as effective wireless platforms for aiding users in various situations, particularly in hard-to-reach scenarios like post-disaster relief efforts. This study employs multiple UAVs to cover users in overlapping locations, necessitating the optimization of UAV-user association to maximize the spectral and energy efficiency of the UAV network. Hence, a connected bipartite graph is formed between UAVs and users using graph theory to accomplish this goal. Then, a maximum weighted matching-based maximum flow (MwMaxFlow) optimization approach is proposed to achieve the maximum data rate given users’ demands and the UAVs’ maximum capacities. Additionally, power control is applied using the M-matrix theory to optimize users’ transmit powers and improve their energy efficiency. The proposed strategy is evaluated and compared with other benchmark schemes through numerical simulations. The simulation outcomes indicate that the proposed approach balances spectral efficiency and energy consumption, rendering it suitable for various UAV wireless applications, including emergency response, surveillance, and post-disaster management.

Enabling Metaheuristics with Deep Learning based Resource Allocation in Unmanned Aerial Vehicles Wireless Networks

Unmanned aerial vehicle (UAV) network offers a variety of applications in public safety, disaster management, advertising and broadcasting, overload situation, etc. Due to the dynamic characteristics of MU, it is challenging to provide robust transmission services to mobile users (MU). Resource allocation (RA), including sub-channel, serving user, and transmit power, is a crucial problem; also, it is critical to enhance the coverage and energy efficiency of UAV-enabled communication protocol. Furthermore, system resources are limited (for example, spectrum, and transmission power) and UAV transmission coverage and on-board energy are limited. In order to meet the QoE of any user with limited UAV energy and limited resource system, we jointly enhance UAV trajectory, user communication scheduling, and bandwidth allocation and transmit power to satisfy user QoE requirements and increase energy efficiency. Thus, the study proposes a new mud ring optimization with deep belief network-based resource allocation scheme (MRODBN-RAS) technique for UAV-enabled wireless networks. The proposed MRODBN-RAS approach focuses on the effectual accomplishment of the computational and energy-effective decision. Besides, the MRODBN-RAS technique assumed the UAV as a learning agent by forming RA decisions as actions. In addition, the MRODBN-RAS technique designed a reward function to reduce the weighted resource utilization. The MRODBN-RAS technique uses DBN model with hyperparameter tuning using MRO algorithm to allocate the resources. The design of the MRO algorithm helps in the optimal selection of the hyperparameter related to the DBN model. The simulation results of the MRODBN-RAS method are examined under various measures. The extensive comparison study highlighted the better performance of the MRODBN-RAS approach over existing techniques.

DDD coil design for wireless charging of unmanned aerial vehicles

AbstractThe utilization of unmanned aerial vehicles (UAV) has become prevalent nowadays. UAVs are autonomous devices, except for self‐charging. Wireless power transfer (WPT) technologies eliminate human intervention in UAV charging and enable independent charging. The factors making WPT systems popular are autonomizing charging, eliminating cable complexity, and avoiding difficulties caused by environmental conditions. WPT of the UAV in the near field is provided by inductive coupling. In this paper, two vertical rectangular coils and horizontal DDD (triple rectangle) coils were placed in the receiver and the transmitter for UAV wireless charging, respectively. The design of the coils was made in ANSYS Maxwell 3D. A wireless charger of the UAV was designed for 100 W output power at 140 kHz frequency. The misalignment tolerance of the proposed design concerning the x‐ and y‐axis was examined. In addition, the magnetic flux density and magnetic field distributions of the WPT system were investigated. The study found that the proposed design can transfer energy wirelessly with 94% efficiency up to 10.3 cm horizontal and 1.7 cm vertical misalignment.

Design of UAV wireless communication system

The increasing demand for communication has led to a surge in the development and deployment of wireless communication technology in unmanned aerial vehicles (UAVs). As a result, there has been extensive research conducted on UAV wireless communication systems over the past five years. This article provides an overview of the advancements made in three key areas of research: transmission of UAV wireless communication systems, wireless communication networks, and design optimization of UAV wireless communication systems. The article summarizes the existing literature on these topics and highlights the main findings and challenges faced by researchers. In addition, it explores the principles and implementation approaches of three types of UAV wireless communication systems, including a UAV wireless communication system, a UAV wireless transmission system based on the AD9361+ZYNQ7045 platform, and a UAV communication system based on wireless data transmission module. Overall, the research in UAV wireless communication systems has shown great potential for future applications in various fields such as aerial photography, disaster monitoring, and search and rescue operations. By improving the efficiency and reliability of wireless communication between UAVs, the technology can enhance their performance and increase their usefulness in a range of industries.

Design of Two Orthogonal Transmitters With Double L-Type Ferrite for the Wireless Charging System in Unmanned Aerial Vehicles

This article proposes a wireless charging system based on a novel magnetic coupler with high coupling capacity for recharging the unmanned aerial vehicles (UAVs). The novel magnetic coupler includes two transmitters and one receiver, in which the transmitters consist of double orthogonal rectangular coils and double orthogonal L-type cores and the receiver is an air-cored receiving coil perpendicular to one coil of the transmitter and parallel to the other coil of the transmitter. The magnetic flux is concentrated in the receiving coil as much as possible while guaranteeing the magnetic flux operation space away from a UAV body by coil structure itself. A prototype of the UAV wireless charging system based on the novel magnetic coupler is built and tested. The experimental wireless charging system designed for Phantom 4 RTK can deliver 102 W at a dc-to-battery efficiency of 91.02% at the maximum output power point, which is adapted to the wireless charging in UAVs.

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.

Developing an Intelligent Cellular Structure Design for a UAV Wireless Communication Topology

In the current digital era, where Unmanned Aerial Vehicles (UAVs), Artificial intelligence (AI), and Internet of Everything (IoE) can be well integrated, more global connectivity and automated solutions can be witnessed. This paper aims to develop an intelligent cellular structure design for a UAV wireless communication topology using an AI framework. The proposed AI framework includes Self Organizing Maps (SOMs) and an NN fitting tool that can be simulated using the Graphical User Interface (GUI) toolbox in MATLAB. The proposed framework is validated in a proof-of-concept scenario, where various parameters of link budget and cellular structure design have been tuned to achieve an efficient and optimized automatic design. The obtained results show high levels of adaptable wireless communication predictions without human intervention, which is a noticeable shift from existing work in the literature.

Pricing Based Scheme for UAV-Enabled Wireless Energy Transfer

Unmanned aerial vehicles (UAVs) are expected to become one of the key enablers of the promising 5G and beyond 5G (B5G) communications. However, the deployment of the UAVs is fundamentally constrained by low energy storage capacity and limited flying time capability. These limitations are further aggravated in static UAVs (SUAVs), used as aerial nodes in cellular-connected applications such as surveillance, natural disaster, and heterogeneous cellular systems, among other high-speed wireless communications. In this paper, the idea of UAV to UAV wireless charging is envisaged. A scenario of wireless charging of an SUAV by the most cost-effective master UAV (MUAV), capable of wireless energy transfer (ET) is considered, wherein MUAVs are associated with their fixed grounded platforms. A UAV network is considered in which the most economical MUAV is dispatched to the known location of the SUAV and charge without discontinuing the mission of SUAV. Motivated by the fact that an optimized and relevant pricing scheme for futuristic charging service providers will be the critical aspect in the promising competitive market scenarios, a suitable pricing scheme is proposed; and the equilibrium in the market sharing condition using a game-theoretic approach is investigated. The Nash equilibrium (NE) for the total profit for each platform in a non-cooperative competitive environment is derived by analyzing their corresponding best prices. In this regard, a closed-form expression of the decision boundary for the SUAVs, based on area division, is identified.

Efficiency Improvement of Magnetic Coupler with Nanocrystalline Alloy Film for UAV Wireless Charging System with a Carbon Fiber Fuselage

Existing research on the magnetic coupler of unmanned aerial vehicle (UAV) wireless charging systems usually ignores the UAV fuselage, but the fuselage causes eddy current loss and reduces a system’s efficiency. Therefore, aiming at the above problems, this paper proposes a design for a magnetic coupler using nanocrystalline cores to reduce the loss caused by the UAV fuselage. First, the parameters of the asymmetric circular coils were designed for higher mutual inductance. The losses caused by the windings and cores were also calculated. Second, for the loss effect of the carbon fiber fuselage, the fuselage was modeled as an additional coil coupled with both the transmitting and receiving coils. The fact that the eddy current induced by the fuselage leads to efficiency reduction is revealed, which has been generally ignored by previous research. Then, the effect of the nanocrystalline alloy was analyzed based on the magnetic circuit model. An optimized nanocrystalline alloy film was applied to reduce eddy current loss and improve coupler efficiency. Finally, an experimental prototype with a 500 W output, 90.3% efficiency, and a 300 mm air gap were fabricated. When compared to the design without UAV material considerations, the coupler efficiency was improved by 7.9%.

A New Magnetic Coupler With High Rotational Misalignment Tolerance for Unmanned Aerial Vehicles Wireless Charging

In this letter, a novel magnetic coupler with high rotational misalignment tolerance is proposed for unmanned aerial vehicles (UAVs) wireless charging. The transmitting coil consists of an inner coil and an outer circular coil connected in series, which generates a horizontal magnetic field from the center to all periphery directions within the charging range. Then, to receive the flux effectively and to adapt to UAV's structure, a vertical square air-core coil is designed and attached to the landing gear. The proposed magnetic coupler can reduce significantly the magnetic fluxes penetrating through the body of the UAV, therefore mitigating electromagnetic interference with onboard devices. Simulation based on ANSYS Maxwell and experiments based on a laboratory prototype are carried out to validate the proposal. The results show that, the proposed magnetic coupler can transmit power in all 360° of rotational misalignments, and the output voltage of the wireless charging system fluctuates only 2% around the 48 V reference.

Channel Estimation for UAV Communication Systems Using Deep Neural Networks

Channel modeling of unmanned aerial vehicles (UAVs) from wireless communications has gained great interest for rapid deployment in wireless communication. The UAV channel has its own distinctive characteristics compared to satellite and cellular networks. Many proposed techniques consider and formulate the channel modeling of UAVs as a classification problem, where the key is to extract the discriminative features of the UAV wireless signal. For this issue, we propose a framework of multiple Gaussian–Bernoulli restricted Boltzmann machines (GBRBM) for dimension reduction and pre-training utilization incorporated with an autoencoder-based deep neural network. The developed system used UAV measurements of a town’s already existing commercial cellular network for training and validation. To evaluate the proposed approach, we run ray-tracing simulations in the program Remcom Wireless InSite at a distinct frequency of 28 GHz and used them for training and validation. The results demonstrate that the proposed method is accurate in channel acquisition for various UAV flying scenarios and outperforms the conventional DNNs.

IRS Empowered UAV Wireless Communication With Resource Allocation, Reflecting Design and Trajectory Optimization

As revolutionary technologies that can actively change the communication link signal, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have emerged as reliable, economical and convenient wireless communication solutions for a variety of practical scenarios. Therefore, this paper focuses on an IRS empowered UAV downlink communication network, where the dynamic UAV establishes a cascade link via IRS to provide signal enhancement services for multiple users. Considering constraints of transmit power, flight speed and area at the UAV and the reflecting constraints at the IRS, the block coordinate descent (BCD) method based on resource allocation, reflecting design and trajectory optimization is adopted to maximize the sum-rate of all users. The proposed problem is converted by using quadratic transformation and Lagrangian dual transformation. Then applying for the approximate linear method and Iterative Rank Minimization (IRM) to optimize the transmit power of UAV and phase shift of IRS respectively. Since additional reflection propagation paths by IRS, the complexity of the channel model makes the trajectory design difficult. To tackle this problem, this paper proposes a UAV trajectory optimization method based on enhanced reinforcement learning with the fixed initial location and destination. In the end, the convergence of the proposed scheme is effectively verified by simulations. Moreover, abundant simulation comparisons between the proposed scheme and other benchmark schemes demonstrate the validity and high performance gains of the proposed algorithm.

A Free-Rotation Asymmetric Magnetic Coupling Structure of UAV Wireless Charging Platform With Conformal Pickup

This article proposes an asymmetric magnetic coupler using the horizontal magnetic field to realize the conformal and lightweight unmanned aerial vehicle (UAV) side pickup mechanism and solve the problems of rotation misalignment and horizontal offset of UAVs. The novel structure uses multiple groups of series circular coils as the transmitting coil to construct a uniform near harness horizontal magnetic field. The solenoid structure covered with flexible magnetic material is used as the receiver, which is attached to the UAV landing gear. A 200-W prototype is produced to validate the proposed structure. The system can maintain smooth mutual inductance with a dc–dc efficiency of 90% under a ±20 mm offset and free-rotation.

Outage Performance Analysis of RIS-Assisted UAV Wireless Systems Under Disorientation and Misalignment

In this paper, we analyze the performance of a reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) wireless system that is affected by mixture-gamma small-scale fading, stochastic disorientation and misalignment, as well as transceivers hardware imperfections. First, we statistically characterize the end-to-end channel for both cases, i.e., in the absence as well as in the presence of disorientation and misalignment, by extracting closed-form formulas for the probability density function (PDF) and the cumulative distribution function (CDF). Building on the aforementioned expressions, we extract novel closed-form expressions for the outage probability (OP) in the absence and the presence of disorientation and misalignment as well as hardware imperfections. In addition, high signal-to-noise ratio OP approximations are derived, leading to the extraction of the diversity order. Finally, an OP floor due to disorientation and misalignment is presented.

Survey on UAV Deployment and Trajectory in Wireless Communication Networks: Applications and Challenges

A new era of the fifth-generation (5G) networks is realized to satisfy user demands on higher data rate and massive connectivity for information sharing and utilization. The vertical applications such as vehicle-to-everything (V2X) communications, industrial automation, smart factory, smart farm and smart cities require ultra-fast communications and wide service range. Coverage extension is a key issue to support the required demands on higher performance, but requires an additional deployment of base or relay stations. Therefore, an efficient solution needs to be cost-effective and easy, in order to deploy more stations. An unmanned aerial vehicle (UAV) has been considered as a candidate to overcome these issues because it is much more cost-effective than the ground stations and does not require network or cell replanning, thereby enhancing the network coverage without additional excessive deployment procedures of the existing networks. UAVs will play important roles in 5G and beyond networks assisting as macro base stations, relay stations, small cells, or a moving aggregator. The performance of UAV wireless networks highly depends on the position or the trajectory of UAVs and the resource managements of entire networks. Recently, there have been extensive studies on performance analysis, UAV deployment, UAV trajectory and resource management of UAV wireless networks to achieve the required demands on performance. This paper surveys research conducted for the UAV deployment and trajectory to construct UAV wireless networks for the coverage extension, the throughput improvement and the resource management for different use cases and scenarios, so as to encourage further studies in this area.

Energy-Efficient and Secure Communication Toward UAV Networks

Wireless networks ensure the unmanned aerial vehicles (UAVs) communicate and cooperate with each other, which plays an indispensable role among UAVs. The two crucial challenges in UAV wireless networks are energy saving and security. The current lightweight communication approaches lead to insufficient robustness of the encrypted transmission that is insecure. To address this issue, we propose a secure transmission approach with energy efficiency toward UAVs networks. We design a lightweight symmetric encryption algorithm based on SM4 and the relevant key negotiation and update mechanism to protect the confidentiality of communication contents. Moreover, a modified aggregative BLS signature scheme, together with the Merkle Hash tree (MHT), is introduced to guarantee the integrity and authenticity of data packets in transmission. Furthermore, we propose an online/offline revocable identity-based group signature (OORIBGS) scheme and integrate it into our framework for UAV anonymity, traceability, as well as revocability with small key management cost and high efficiency. We give detailed security analysis and prove that our proposal has the properties of data confidentiality, integrity, and authenticity, as well as identity traceability and anonymity. Moreover, we apply our approach in the UAVs networks and evaluate the runtime and anti-attack performance. The experimental results show that the proposed method can be effectively used in UAVs secure communication.

Deep Learning-Aided Downlink Beamforming Design and Uplink Power Allocation for UAV Wireless Communications with LoRa

In this paper, we consider an unmanned aerial vehicle (UAV) wireless communication system where a base station (BS) equipped multi antennas communicates with multiple UAVs, each equipped with a single antenna, using the LoRa (Long Range) modulation. The traditional approaches for downlink beamforming design or uplink power allocation rely on the convex optimization technique, which is prohibitive in practice or even infeasible for the UAVs with limited computing capabilities, because the corresponding convex optimization problems (such as second-order cone programming (SOCP) and linear programming (LP)) requiring a non-negligible complexity need to be re-solved many times while the UAVs move. To address this issue, we propose novel schemes for beamforming design for downlink transmission from the BS to the UAVs and power allocation for uplink transmission from the UAVs to the BS, respectively, based on deep learning. Numerical results demonstrate a constructed deep neural network (DNN) can predict the optimal value of the downlink beamforming or the uplink power allocation with low complexity and high accuracy.

One-directional parsing function for information security in computer networks of unmanned aerial vehicles

The tools of the formal languages and grammars theory are proposed to be used in developing the ways to increase the information security of wireless data transmission systems in computer networks of unmanned aerial vehicles (UAV). Multiple use of production rules for generating sentences of a (regular) language in the processes of inter-level transformations and transmission of PDUs from a true primary information generator through UAV wireless computer network nodes allows to create the pseudo-sources of primary information, which hinders unauthorized access.