Unmanned Aerial Vehicle Communication Research Articles

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.

Channel Knowledge Map Construction Based on a UAV-Assisted Channel Measurement System

With the fast development of unmanned aerial vehicles (UAVs), reliable UAV communication is becoming increasingly vital. The channel knowledge map (CKM) is a crucial bridge connecting the environment and the propagation channel that may visually depict channel characteristics. This paper presents a comprehensive scheme based on a UAV-assisted channel measurement system for constructing the CKM in real-world scenarios. Firstly, a three-dimensional (3D) CKM construction scheme for real-world scenarios is provided, which involves channel knowledge extraction, mapping, and completion. Secondly, an algorithm of channel knowledge extraction and completion is proposed. The sparse channel knowledge is extracted based on the sliding correlation and constant false alarm rate (CFAR) approaches. The 3D Kriging interpolation is used to complete the sparse channel knowledge. Finally, a UAV-assisted channel measurement system is developed and CKM measurement campaigns are conducted in campus and farmland scenarios. The path loss (PL) and root mean square delay spread (RMS-DS) are measured at different heights to determine CKMs. The measured and analyzed results show that the proposed construction scheme can effectively and accurately construct the CKMs in real-world scenarios.

ASMTP: Anonymous secure messaging token‐based protocol assisted data security in swarm of unmanned aerial vehicles

AbstractSwarm of Unmanned Aerial Vehicles (UAVs) broaden the field of application in various fields like military surveillance, crop monitoring in agriculture, combat operations, etc. Unfortunately, they are becoming increasingly susceptible to security attacks, such as jamming, information leakage and spoofing, as they become more common and in more demand. So, there is a wider need for UAVs, which requires the design of strong security procedures to fend off such attacks and security dangers. Even though several studies focused on security aspects, many questions remain unanswered, particularly in the areas of secure UAV‐to‐UAV communication, support for perfect forward secrecy and non‐repudiation. In a battle situation, it is extremely important to close these gaps. The security requirements for the UAV communication protocol in a military setting were the focus of this study. In this paper, we present the issues faced by the UAV swarm, especially during military surveillance operations. To secure the communication link in UAV, a new protocol for UAV Swarm communication is proposed with anonymous secure messaging token‐based protocol (ASMTP). The proposed protocol secures UAV‐to‐base station communication and safeguards the metadata of the sender and receiver nodes. The proposed model maintains the confidentiality, integrity and availability of data in the UAV Swarm and achieves robustness. In addition, it provides a different strategy for the cybersecurity gaps in the swarm of UAVs during military surveillance and combat operations.

Optimization of Energy Efficiency for Federated Learning over Unmanned Aerial Vehicle Communication Networks

Federated learning (FL) is considered a promising machine learning technique that has attracted increasing attention in recent years. Instead of centralizing data in one location for training a global model, FL allows the model training to occur on user devices, such as smartphones, IoT devices, or local servers, thereby respecting data privacy and security. However, implementing FL in wireless communication faces a significant challenge due to the inherent unpredictability and constant fluctuations in channel characteristics. A key challenge in implementing FL over wireless communication lies in optimizing energy efficiency. This holds significant importance, especially considering user devices with restricted power resources. On the other hand, unmanned aerial vehicle (UAV) technologies present a cost-effective solution owing to flexibility and mobility compared to terrestrial base stations. Consequently, the deployment of UAV communication in FL is viewed as a potential approach to deal with the energy efficiency challenge. In this paper, we address the problem of minimizing the total energy consumption of all user equipment (UE) during the training phase of FL over a UAV communication network. Our proposed system facilitates UE to operate concurrently at the same time and frequency, thereby improving bandwidth utilization efficiently. In this paper, we address the problem of minimizing the total energy consumption during the training phase of FL over a UAV communication network. To deal with the proposed nonconvex problem, we propose a novel alternating optimization approach by dividing the problem into two suboptimal problems. We then develop iterative algorithms based on the inner approximation method, yielding at least one locally optimal solution. The numerical results demonstrate the superiority of the proposed algorithm in solving the proposed problem compared to other benchmark algorithms, particularly in determining the optimal trajectory of the UAVs. In addition, we conduct extensive experiments to evaluate how different parameter settings affect performance after implementing the proposed optimization approaches for deploying FL within the UAV communication system. These analyses yield valuable insights into the comparative effectiveness of the proposed optimization algorithms concerning overall energy consumption reduction.

Low-Resolution Optimization for an Unmanned Aerial Vehicle Communication Network under a Passive Reconfigurable Intelligent Surface and Active Reconfigurable Intelligent Surface

This paper investigates the optimization of an unmanned aerial vehicle (UAV) network serving multiple downlink users equipped with single antennas. The network is enhanced by the deployment of either a passive reconfigurable intelligent surface (RIS) or an active RIS. The objective is to jointly design the UAV’s trajectory and the low-bit, quantized, RIS-programmable coefficients to maximize the minimum user rate in a multi-user scenario. To address this optimization challenge, an alternating optimization framework is employed, leveraging the successive convex approximation (SCA) method. Specifically, for the UAV trajectory design, the original non-convex optimization problem is reformulated into an equivalent convex problem through the introduction of slack variables and appropriate approximations. On the other hand, for the RIS-programmable coefficient design, an efficient algorithm is developed using a penalty-based approximation approach. To solve the problems with the proposed optimization, high-performance optimization tools such as CVX are utilized, despite their associated high time complexity. To mitigate this complexity, a low-complexity algorithm is specifically tailored for the optimization of passive RIS-programmable reflecting elements. This algorithm relies solely on closed-form expressions to generate improved feasible points, thereby reducing the computational burden while maintaining reasonable performance. Extensive simulations are created to validate the performance of the proposed algorithms. The results demonstrate that the active RIS-based approach outperforms the passive RIS-based approach. Additionally, for the passive RIS-based algorithms, the low-complexity variant achieves a reduced time complexity with a moderate loss in performance.

Analysis of unmanned aerial vehicle communication performance with optic sensor integration

This paper presents a novel algorithmic framework to enhance unmanned aerial vehicle (UAV) communication performance through the adaptive integration of optical sensor data. The framework features a dynamic sensor integration algorithm (DSIA) that optimizes sensor bitrates based on real-time bandwidth availability and mission-driven priorities. An adaptive compression scheme works alongside the DSIA to maintain high data fidelity and resolution at lower bitrates. Together, these algorithms provide an efficient and scalable solution for managing high-volume UAV sensor data under varying conditions. The efficiency gains are demonstrated through simulations highlighting the framework’s ability to reduce packet loss and latency while maximizing image quality for a given communication bandwidth constraint. The scalable architecture ensures robustness across diverse UAV platforms, sensor types, and operational scenarios. Simulation results validate up to a 75 % reduction in latency and a 45 % improvement in image resolution compared to conventional fixed bitrate approaches. By optimizing sensor data relevancy and transmission fidelity, this framework enables more effective UAV communication and enhances mission performance.

Traffic-Aware Energy-Efficient Resource Allocation for RSMA Based UAV Communications

Traffic measurement will play an essential role in future networks to reveal the traffic requirements of the users, which will support network operations like resource allocation. In this paper, we study the traffic-aware resource allocation problem for downlink rate-splitting multiple access (RSMA) based unmanned aerial vehicle (UAV) communications. Specifically, with the help of traffic measurement, user requirements on achievable rate are known as prior knowledge to the UAV. Considering user requirements, we maximize the energy efficiency of the UAV by jointly optimizing the UAV deployment, beamforming, rate allocation, and subcarrier allocation. To overcome the non-convexity of the above problem, a joint optimization is developed to solve it iteratively. First, a heuristic approach is proposed to find the three-dimensional location of the UAV. Then, successive convex approximation approach is utilized to optimize RSMA parameters. Moreover, we formulate the subcarrier allocation problem as a many-to-one two-sided matching game, which is tackled by the swap matching algorithm. Numerical results suggest that the proposed subcarrier allocation scheme outperforms benchmark schemes, and RSMA based schemes perform close to that based on non-orthogonal multiple access, which all outperform orthogonal frequency division multiple access based scheme.

Pilot and data power optimization problems in multiple Aerial Relay Stations cell‐free communication systems

SummaryA cell‐free (CF) technology and unmanned aerial vehicles (UAVs) acting as aerial relay stations (ARSs) are gradually viewed as viable technologies for usage in sixth‐generation (6G) wireless networks owing to their outstanding advantages, such as large coverage, uniform service quality, huge connections, flexible deployment in all geographical areas, and high line‐of‐sight (LoS) probability. As a result, the combination of the CF model and UAVs is suitable for densely populated urban areas, shopping malls, festivals, and locations with complex geography. Furthermore, the integration of the CF model and UAV communication can improve system quality and is considered an entirely new research direction as there is no comprehensive investigation of this combined model. In this study, we investigate the quality of the multi‐ARS CF communication system, where ARSs are equipped with multiple antennas and stochastic distribution within a specific region to simultaneously serve numerous ground users. We establish a closed‐form formulation for the uplink and downlink throughput of individual users by using the matched filtering method and conjugate beamforming technology, respectively. Moreover, we propose a novel method to optimize the pilot and data transmission power coefficients for improving channel estimation and increasing throughput per user, thereby ensuring a high quality of service. The power optimization method is executed via the successive convex approximation (SCA) method, second‐order cone program (SOCP), and linear program. We evaluate system performance according to the cumulative distribution function (CDF) of user throughput based on various parameters, such as the number of users, the number of ARSs, and the length of pilot sequences. The analytical findings also reveal that the system with the proposed power optimization method is always superior to the system without the proposed method in both uplink and downlink.

Energy-efficient UAV communication: A NOMA scheme with resource allocation and trajectory optimization.

This work investigates a downlink nonorthogonal multiple access (NOMA) scheme with unmanned aerial vehicle (UAV) aided wireless communication, where a single UAV was regarded as an air base station (ABS) to communicate with multiple ground users. Considering the constraints of velocity and maneuverability, a UAV energy efficiency (EE) model was proposed via collaborative design resource allocation and trajectory optimization. Based on this, an EE maximization problem was formulated to jointly optimize the transmit power of ground users and the trajectory of the UAV. To obtain the optimal solutions, this nonconvex problem was transformed into an equivalent convex optimization problem on the basis of three user clustering algorithms. After several alternating iterations, our proposed algorithms converged quickly. The simulation results show an enhancement in EE with NOMA because our proposed algorithm is nearly 99.6% superior to other OMA schemes.

Spectrally Efficient UAV Communications using Non-Orthogonal Multiple Access (NOMA)

Non-orthogonal multiple access (NOMA) scheme with its share of benefits such as spectral efficiency and simultaneous support of massive connectivity, is one of the keys enabling technologies for upcoming 5G. However, in the context of UAV-assisted communication, existing literature mainly focuses on conventional orthogonal multiple access (OMA) which limits the spectral efficiency and number of simultaneous connections. Existing technologies are using LTE that only can serve 1 user per resource block. The user capacity nowadays is increased, so the existing technologies should be improved to higher user capacity such as 4 users per resource block. This research project focuses on the design of NOMA scheme for multiple users aiming to maximize the system capacity and spectral efficiency of Unmanned Aerial Vehicle (UAV) hotspot with baseline model and to simultaneously support mobile users. The proposed NOMA multi-users model is then compared with baseline model of 2 users per resource block in NOMA and OMA. The proposed NOMA model increases spectral efficiency, higher throughput and improved user fairness. The project only focuses on downlink communication with stationary users. The parameters measured are Bit Error Rate, Sum Rate, and Spectral Efficiency that depend on power allocation, altitude of UAV, and distance between UAV and users. Based on the simulation results, NOMA which employs Fractional Transmit Power Allocation (FTPA) algorithm has better performance in terms of sum-rate and spectral efficiency compared to those of OMA by as much as 650%. Results are also presented for various NOMA power allocation algorithms.

Reinforcement learning-based energy efficiency optimization for RIS-Assisted UAV hybrid uplink and downlink system

As Reconfigurable Intelligent Surface (RIS) technology can intelligently reflect the incident signal, it can be deployed to maintain a line-of-sight connection and improve the signal strength between unmanned Aerial Vehicles (UAV) and users. Current researches on RIS-assisted UAV communication typically consider optimizing data rates for single-line communications and do not focus on the energy consumption of the UAV. Furthermore, it is frequently challenging to apply conventional algorithms to RIS-assisted UAV optimization problems in a timely way, and the two-dimensional trajectory design cannot fully utilize the 3D mobility of the UAV. In order to increase the data rates for all ground users while improving the energy efficiency of the system, we consider a RIS-assisted full-duplex UAV communication system, in which a DDQN-based algorithm is proposed to jointly optimize the phase shift of the RIS and the 3D trajectory of the UAV. Simulation results demonstrate that the proposed algorithm can achieve a significant data rate gain and energy efficiency compared to time-division duplex systems and other deep reinforcement learning algorithms.

A systematic approach for threat and vulnerability analysis of unmanned aerial vehicles

The proliferation of Unmanned Aerial Vehicles (UAVs) is expected to experience a substantial rise in the next years, driven by their ever-increasing application across various domains. However, ensuring secure communication between UAVs and their ground stations is crucial to prevent the unauthorized disclosure of sensitive information that could jeopardize the mission’s integrity when exploited by malicious actors. Despite this importance, several UAV systems currently operate based on open-source command and control technologies, which have overlooked several security considerations while focusing on availability and safety. To address this concern, this study conducts a comprehensive security assessment of UAV-based systems starting with a systematic literature review whose main purpose is building a comprehensive catalog of threats associated with this technology. Particular attention has been paid to the MAVlink protocol, an open-source protocol commonly utilized for telemetry and command and control for multiple UAVs. Therefore, drawing upon the built catalog, a threat modeling and penetration testing technique has been employed to examine the MAVlink implementation on a real UAV. A threat model developed for a specific case study is also presented, leading to the discovery of four new vulnerabilities, some of which were successfully exploited through attacks. By shedding light on these vulnerabilities, this work seeks to encourage further investigation and research to develop robust security mechanisms for UAV communication systems. It is imperative to address these vulnerabilities proactively to enhance the overall security posture and safeguard against potential threats in the UAV ecosystem.

Deep reinforcement learning aided secure UAV communications in the presence of moving eavesdroppers

Integrating unmanned aerial vehicles (UAVs) with wireless systems is widely anticipated to bring enormous benefits to network users, and is considered as a key enabling technology for next generation wireless networks. However deployment of UAVs raises security concerns, as they are prone to eavesdropping attacks. In this paper we investigate secure data transmissions in a UAV-aided communication system from the physical layer security (PLS) perspective. The UAV is employed as a flying base station and transmit confidential information to a ground user. Specifically, an eavesdropper is moving in the vicinity of the ground user, attempting to intercept legitimate data transmission. We aim to optimize UAV trajectory to maximize secrecy throughput and due to the fast changing environment with unpredictable movements of the eave, this nonconvex problem is difficult to solve. Instead, we propose a deep reinforcement learning framework by reformulating this problem as a Markov Decision Process (MDP). By carefully designing state spaces, state-dependent action sets and time-dependant reward signals, we aim to help the agent learn an optimized trajectory and complete the flight task within time limit. We adapt the deep Q network (DQN) algorithm and extend it with prioritized experience replay (PER) and importance sampling method to ensure a stable and effective learning outcome. Simulation results validate the effectiveness of the learning algorithm and confirm that the agent is able to learn an effective policy with desirable and robust learning outcomes. Compared with benchmark schemes, our proposed scheme is shown to achieve considerable performance gain in secrecy throughput.

UAV-Enabled Integrated Sensing and Communication: Opportunities and Challenges

Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) has attracted growing research interests in the context of sixth-generation (6G) wireless networks, in which UAVs will be exploited as aerial wireless platforms to provide better coverage and enhanced sensing and communication (S&C) services. However, due to the size, weight, and power (SWAP) constraints of UAVs, their controllable mobility, and the line-of-sight (LoS) air-ground channels, UAVenabled ISAC introduces new opportunities and challenges. This article provides an overview of UAV-enabled ISAC and proposes various solutions for optimizing the S&C performance. In particular, we first introduce UAV-enabled joint S&C and discuss UAV motion control, wireless resource allocation, and interference management for ISAC systems employing single and multiple UAVs. Then, we present two application scenarios for exploiting the synergy between S&C, namely sensing-assisted UAV communication and communication-assisted UAV sensing. Finally, we highlight several interesting research directions to guide and motivate future work.

A Quad-Polarization Reconfigurable Conformal Array of Wide Coverage Range and High Gain for Unmanned Aerial Vehicle Communications

A Quad-Polarization Reconfigurable Conformal Array of Wide Coverage Range and High Gain for Unmanned Aerial Vehicle Communications

Near-field beamforming method based on motion model analysis for UAVs communication

This study presents a near-field beamforming method for the moving swarm composed of unmanned aerial vehicles (UAVs) to address the challenges of position errors that hinder effective beamforming. Firstly, a model with positional errors in moving UAVs was developed. The sources and characteristics of UAVs’ position errors were analyzed and classified into low-frequency motion errors and high-frequency vibration errors, and their effects on UAVs array beamforming were analyzed using coordinate transformation and decomposition. Secondly, a parameter estimation algorithm was proposed based on the Fast Fourier Transformation (FFT) and the Least Square (LS) method to estimate the unknown parameters of the UAV's low-frequency motion errors. Lastly, a near-field beamforming algorithm for UAVs was proposed based on the Interpolation Kalman filter (IKF). The effectiveness of the proposed model and algorithms was verified through simulations, which provided a theoretical basis for the near-field beamforming under the existence of the UAVs position errors.

Joint optimization of UAV communication connectivity and obstacle avoidance in urban environments using a double-map approach

AbstractCellular-connected unmanned aerial vehicles (UAVs), which have the potential to extend cellular services from the ground into the airspace, represent a promising technological advancement. However, the presence of communication coverage black holes among base stations and various obstacles within the aerial domain pose significant challenges to ensuring the safe operation of UAVs. This paper introduces a novel trajectory planning scheme, namely the double-map assisted UAV approach, which leverages deep reinforcement learning to address these challenges. The mission execution time, wireless connectivity, and obstacle avoidance are comprehensively modeled and analyzed in this approach, leading to the derivation of a novel joint optimization function. By utilizing an advanced technique known as dueling double deep Q network (D3QN), the objective function is optimized, while employing a mechanism of prioritized experience replay strengthens the training of effective samples. Furthermore, the connectivity and obstacle information collected by the UAV during flight are utilized to generate a map of radio and environmental data for simulating the flying process, thereby significantly reducing operational costs. The numerical results demonstrate that the proposed method effectively circumvents obstacles and areas with weak connections during flight, while also considering mission completion time.

Industrial blockchain threshold signatures in federated learning for unified space-air-ground-sea model training

The space-air-ground-sea three-dimensional (3D) network is a comprehensive communication network system. This 3D network combines extensive coverage of satellite communications, adaptability of unmanned aerial vehicle (UAV) communications, reliability of terrestrial communications, and the necessity for maritime communications. These networks generate enormous amounts of data, and training machine learning (ML) models on this data will have a significant impact on industry. At the same time, the availability of such data poses numerous security threats, which can be overcome using Federated Learning (FL). Decentralized training in FL can provide a universal model from local data generated by 3D networks. However, most existing FL frameworks have a centralized server, which questions credibility, single-point failure, and global confidence. To solve these problems, industrial blockchain technology has received a lot of attention by replacing centralized servers in traditional FL, which offers a promising approach to address key issues like data privacy and security. In a blockchain-based system, digital signatures are a core component for ensuring data integrity and system security, however, private key disclosure can pose significant risks. Security can be enhanced by using threshold signatures which provide a more reliable foundation for FL by storing keys in multiple nodes and requiring multiple nodes to collaborate to generate signatures. In this paper, we propose a Threshold signing scheme for ISO/IEC Digital Signature Standards (TDSS) in an industrial blockchain. The TDSS scheme helps FL to achieve truly distributed decentralization for unified space-air-ground-sea model training. The TDSS scheme exploits the SM-2 digital signature algorithm in the ISO/IEC standard when t out of n nodes in an industrial blockchain interact with each other to calculate the signature. The experimental results and analysis show that the TDSS scheme has provable security and efficient against security attacks, which can be applied to large-scale threshold signing scenarios.