Unmanned Aerial Vehicle Communication Systems Research Articles

Performance Analysis of Spatial Modulation Aided UAV Communication Systems in Cooperative Relay Networks

Performance Analysis of Spatial Modulation Aided UAV Communication Systems in Cooperative Relay Networks

Performance Evaluation of mmWave Communication for Real-Time UAV-Base Station Links

Introduction: In the context of modern communication networks, the increasing demand for high data rates, minimal latency, and improved reliability—especially in scenarios involving Unmanned Aerial Vehicles (UAVs)—calls for innovative solutions. Millimeter-wave (mmWave) communication emerges as a fitting choice due to its capacity to offer ample bandwidth and high data rates. Methods: This research paper presents a comprehensive study that compares the performance of mmWave communication links across four different frequencies (6, 28, 60, and 120 GHz) for UAV-to-Base Station (BS) communication. The investigation, conducted through simulations, adheres to atmospheric conditions such as rain and fog, different environments (urban and rural) scenarios. It evaluates the suitability of mmWave frequencies using critical performance metrics: latency, received power, Bit Error Rate (BER) and Signal-to-Interference plus Noise Ratio (SINR). These metrics play a fundamental role in assessing communication link quality. Results: Our findings reveal that leveraging higher mmWave frequencies—specifically 60 and 120 GHz—significantly enhances UAV-to-BS communication performance. Compared to lower frequencies, these higher bands exhibit reduced latency, higher data rates, and improved throughput. Consequently, mmWave frequencies beyond 60 GHz prove well-suited for UAV communication, facilitating efficient data exchange with minimal delay. Conclusion: Furthermore, the potential for reduced interference and enhanced reliability at these elevated frequencies makes them particularly advantageous in applications requiring seamless connectivity. Examples include UAV-assisted emergency response, surveillance, and remote sensing. These insights hold substantial implications for the design and deployment of UAV communication systems, highlighting mmWave technology’s transformative potential in the emerging era of aerial communication.

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.

Dual-Band Monopole MIMO Antenna Array for UAV Communication Systems.

This study proposes a compact, low-profile, four-port dual-band monopole multiple-input-multiple-output (MIMO) antenna array for unmanned aerial vehicle (UAV) communication systems. Each monopole antenna of the array features a modified T-shaped radiator configuration and is printed on a Rogers RT5880 substrate with compact dimensions of 134.96 mm × 134.96 mm × 0.8 mm. A four-element square MIMO configuration with sequential 0°, 90°, 180°, and 270° rotations was integrated smoothly into the UAV body. A prototype of the MIMO array was fabricated and experimentally evaluated, with measured results showing a close correlation to simulated results. The proposed dual-band monopole antenna demonstrated one of the widest impedance bandwidths of 46.15% at 2.4 GHz (2.04 to 3.25 GHz) IEEE 802.11b and 31.85% at 5.8 GHz (5.37 to 7.38 GHz) IEEE 802.11a on a thin 0.0064 λo substrate while achieving high transmission efficiency. The isolation of the proposed four-port MIMO design was measured at 23 dB at 2.4 GHz and 19 dB at 5.8 GHz. The MIMO array's total efficiency of each monopole antenna was measured at 96% at 2.4 GHz and 89% at 5.8 GHz. The design has measured diversity parameters such as an ECC below 0.01 and a DG of approximately 10. Based on these results, the proposed design suits the UAV communication system.

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%.

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.

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.

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.

Investigating the Influence of Temperature on UAV Signal Quality

Advancements in drone technology make them important in many areas. military, industry, and disaster The efficacy of a drone's communication systems can be greatly impacted by temperature fluctuations, either from environmental conditions or mechanical problems in the drone's construction. This study gives an analysis and computational model of the impact of temperature on the performance of drone communication. Utilizing a one-dimensional convolutional neural network, we aim to forecast the signal-to-noise ratio (SNR) and received signal strength indicator (RSSI). Following the initial stage of dataset creation in the drone laboratory, proceed to reprocess the dataset and divide it into a 70% training set and a 30% testing set. Subsequently, a graphical user interface (GUI) was developed using MATLAB App Designer to enhance user friendliness. The outcome suggests that the efficiency of the drone communication system declines with rising temperatures. Using 1DCNN is our contribution to this work; other studies depend only on simulation to assess performance. One benefit of 1DCNN is that the impact may be evaluated by automatically extracting important features from the input dataset. Using 1DCNN is our special addition to this project; other research evaluate the UAV communication system's effectiveness only through simulation. We propose in this work to optimize system characteristics for improved performance, including power transfer, by adding a feedback loop between the CNN result and the communication system. Furthermore, we investigate how different weather conditions, such wind and rain, affect UAV communication systems.

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.

Technical Loopholes and Trends in Unmanned Aerial Vehicle Communication Systems

Technical Loopholes and Trends in Unmanned Aerial Vehicle Communication Systems

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.

Learning-Aided Blind Beam Adaptation for UAV Communication Systems With Jittering

Learning-Aided Blind Beam Adaptation for UAV Communication Systems With Jittering

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.

Analysis of Drone Wireless Communication System Performance Affected by Vibration based on 1DCNN

Developments in drone technology have made them crucial in various fields. Vibrations caused by external conditions or mechanical failures in a drone's design can significantly affect the efficiency of the drone's communication systems. The drone's antenna generates phase noise, which can degrade the performance of drone communications systems. This work presents an analysis and computational model of how drone vibration affects system performance. by using two steps. The first one uses the simulation Monte-Carlo in MATLAB when the iteration algorithm processes with various variable values as the frequency carriers and the order of the quadrature-amplitude-modulation (M-QAM) system and evaluates the performance of the communication system by measuring the symbol error rate. The second step uses the one-dimensional convolutional neural network to predict the symbol error rate. After creating the dataset in the first stage, reprocess it and split it into 70% training and 30% testing. Then, by MATLAB App Designer created a graphical user interface (GUI) for friendly use. The result appears to be that the performance of the drone communication system decreased when frequency carriers and modulation order for M-QAM increased due to the impact of a vibrating antenna. Our contribution to this work is using 1DCNN, unlike other works that only use simulation to evaluate the performance, because 1DCNN can automatically extract useful features from the input dataset to evaluate the effect. This study provides a valuable method to evaluate the efficiency of a communication system on the UAV, which is particularly important for drone wireless system planning. In our next work, we propose investigating other factors affecting UAV communication systems, including humidity and temperature.

Three-dimensional deep reinforcement learning for trajectory and resource optimization in UAV communication systems

In this paper, we focus on trajectory optimization for multiple three-dimensional unmanned aerial vehicles (UAVs) serving as aerial base stations (BS) to provide wireless coverage for Internet of Things (IoT) devices. These IoT devices are randomly distributed in a three-dimensional region with unknown locations and channel parameters. By optimizing the UAV trajectory and power, we aim to achieve the minimum communication time for all IoT devices. However, the unknown flight time of the UAVs and the IoT device locations make it difficult to formulate an optimization problem between the objective and variables by using traditional convex optimization. To deal with this intractable problem, We propose a solution based on Hierarchical Reinforcement Learning (HRL), specifically modeling it as a Markov Decision Process (MDP) and we divide the training process into two parts, each utilizing the Proximal Policy Optimization (PPO) algorithm for training. Our simulation results show that the proposed HRL offers promising performance for both training and testing phases.

Secure Transmission Scheme for Blocks in Blockchain-Based Unmanned Aerial Vehicle Communication Systems

Secure Transmission Scheme for Blocks in Blockchain-Based Unmanned Aerial Vehicle Communication Systems

Analysis of Propagation Characteristics in Unmanned Aerial Vehicle (UAV) System

Unmanned Aerial Vehicle (UAV) has gained great attention to the spread of communication in civilian and military applications. UAV communication channel has its own characteristics compared to cellular and satellite systems, which are widely used. Thus, an accurate channel characterization is very important to optimize the performance and design of an efficient UAV communication system. However, several challenges exist in UAV channel modeling. For example, channel propagation characteristics of UAVs are still less explored. Therefore, this research discusses the propagation characteristics of UAV communication systems. Due to the limitation of the measurement tools, the propagation characteristics identified in this research was the pathloss coefficient value and optimum height based on the value of Received Signal Strength Indicator (RSSI) measurement results at different distance and heights. The link communication used 433 MHz telemetry. The results of pathloss coefficient at heights of 10 m, 20 m, and 30 m are 1.56 m, 1.77 m, and 1.99 m. While the results of the optimum height of 10 m, 20 m, and 30 m are 1.39 m, 1.32 m, and 1.47 m.