Search Results (32)

In recent research on path planning for cellular-connected Unmanned Aerial Vehicles (UAVs), leveraging navigation models based on complex networks and applying the A* algorithm has emerged as a promising alternative to more computationally intensive methods, such as deep reinforcement learning (DRL). These approaches offer performance that approaches that of DRL, while addressing key challenges like long training times and poor generalization. However, conventional A* algorithms fail to consider critical UAV flight characteristics and lack effective obstacle avoidance mechanisms. To address these limitations, this paper presents a novel solution for path planning of cellular-connected UAVs, utilizing a 3D radio map for enhanced situational awareness. We proposed an innovative path planning algorithm, UAVRM-A*, which builds upon the complex network navigation model and incorporates key improvements over traditional A*. Our experimental results demonstrate that the UAVRM-A* algorithm not only effectively avoids obstacles but also generates flight paths more consistent with UAV dynamics. Additionally, the proposed approach achieves performance comparable to DRL-based methods while significantly reducing radio outage duration and the computational time required for model training. This research contributes to the development of more efficient, reliable, and practical path planning solutions for UAVs, with potential applications in various fields, including autonomous delivery, surveillance, and emergency response operations. Full article

In wireless communications, traditional base stations act as the backbone for providing network connectivity to users. These base stations, however, require significant resources to construct and are therefore not suitable for remote areas and disaster scenarios. This challenge makes them unfit for deployment in remote areas or in disaster scenarios where fast network establishment is necessary. To address these challenges, cellular base stations installed on Unmanned Aerial Vehicles (UAVs) can be an alternative solution. UAVs provide quick deployment capability and can adapt to changing environmental situations, making them ideal for dynamic network scenarios. In this paper, we address the critical issue of UAV positioning to maximize the total user coverage, which can be formulated as a mixed-integer linear program. Given the complexity of larger-scale scenarios related to the number of users, we suggest a two-step method. First, we group users into clusters, and then we optimize the UAV positions with respect to these clusters. This approach introduces a trade-off between computational time efficiency and optimality, which can be tuned by adjusting the number of clusters. By varying the number of clusters, we balance computation time with the optimality of the UAV locations, allowing flexible deployment in diverse scenarios. Full article

It is gradually becoming popular to use multiple cellular-connected UAVs as inspectors to fulfill automatic surveillance and monitoring. However, in actual situations, UAVs should respond to several service requests from different hotspots, whilst the requests usually present randomness in the arrival time, data amount, and the concurrency. This paper proposes a dynamic dual-UAV response policy for multi-hotspot services based on single-agent deep Q-learning, where the UAVs controlled by a ground base station can be dispatched automatically to hotspots and then send videos back. First, this issue is formulated as an optimization problem, whose goal is to maximize the number of successfully served requests with the constraints of both the UAV’s energy limit and request waiting time. Second, a reward function based on service completion is designed to overcome the potential challenges posed by the delay reward. Finally, a simulation was conducted, comparing the conventional time priority algorithm and distance priority algorithm, respectively, to the proposed algorithm. The results illustrate that the proposed algorithm can achieve one more response than the others under different service densities, with the lowest failure number and appropriate average waiting time. This method can give a technical solution for the joint communication-and-control problem of multiple UAVs within complex situations. Full article

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. Full article

The Internet of Things (IoT) is gaining popularity and market share, driven by its ability to connect devices and systems that were previously siloed, enabling new applications and services in a cost-efficient manner. Thus, the IoT fuels societal transformation and enables groundbreaking innovations like autonomous transport, robotic assistance, and remote healthcare solutions. However, when considering the Internet of Remote Things (IoRT), which refers to the expansion of IoT in remote and geographically isolated areas where neither terrestrial nor cellular networks are available, internet connectivity becomes a challenging issue. Non-Terrestrial Networks (NTNs) are increasingly gaining popularity as a solution to provide connectivity in remote areas due to the growing integration of satellites and Unmanned Aerial Vehicles (UAVs) with cellular networks. In this survey, we provide the technological framework for NTNs and Remote IoT, followed by a classification of the most recent scientific research on NTN-based IoRT systems. Therefore, we provide a comprehensive overview of the current state of research in IoRT and identify emerging research areas with high potential. In conclusion, we present and discuss 3GPP’s roadmap for NTN standardization, which aims to establish an energy-efficient IoRT environment in the 6G era. Full article

An Unmanned Aerial Vehicle (UAV)-based cellular network over a millimeter wave (mmWave) frequency band addresses the necessities of flexible coverage and high data rate in the next-generation network. But, the use of a wide range of antennas and higher propagation loss in mmWave networks results in high power utilization and UAVs are limited by low-capacity onboard batteries. To cut down the energy cost of UAV-aided mmWave networks, Energy Harvesting (EH) is a promising solution. But, it is a challenge to sustain strong connectivity in UAV-based terrestrial cellular networks due to the random nature of renewable energy. With this motivation, this article introduces an intelligent resource allocation using an artificial ecosystem optimizer with a deep learning (IRA-AEODL) technique on UAV networks. The presented IRA-AEODL technique aims to effectually allot the resources in wireless UAV networks. In this case, the IRA-AEODL technique focuses on the maximization of system utility over all users, combined user association, energy scheduling, and trajectory design. To optimally allocate the UAV policies, the stacked sparse autoencoder (SSAE) model is used in the UAV networks. For the hyperparameter tuning process, the AEO algorithm is used for enhancing the performance of the SSAE model. The experimental results of the IRA-AEODL technique are examined under different aspects and the outcomes stated the improved performance of the IRA-AEODL approach over recent state of art approaches. Full article

Cellular-connected unmanned aerial vehicles (UAVs) present a viable solution to address communication and navigation limitations by leveraging base stations for air–ground communication. However, in complex urban scenarios with stringent communication requirements, achieving asymmetrical control becomes crucial to strike a balance between communication reliability and flight safety. Moreover, existing cellular-connected UAV trajectory planning algorithms often struggle to handle real scenes with sudden and intricate obstacles. To address the aforementioned challenges, this paper presents the multi-tier trajectory planning method (MTTP), which takes into account air–ground communication service assurance and collision avoidance in intricate urban environments. The proposed approach establishes a flight risk model that accounts for both the outage probability of UAV-ground base station (GBS) communication and the complexity of flight environments, and transforms the inherently complex three-dimensional (3D) trajectory optimization problem into a risk distance minimization model. To optimize the flight trajectory, a hierarchical progressive solution approach is proposed, which combines the strengths of the heuristic search algorithm (HSA) and deep reinforcement learning (DRL) algorithm. This innovative fusion of techniques empowers MTTP to efficiently navigate complex scenarios with sudden obstacles and communication challenges. Simulations show that the proposed MTTP method achieves a more superior performance of trajectory planning than the conventional communication-based solution, which yields a substantial reduction in flight distance of at least 8.49% and an impressive 10% increase in the mission success rate. Furthermore, a real-world scenario is chosen from the Yuhang District, Hangzhou (a southern Chinese city), to validate the practical applicability of the MTTP method in highly complex operating scenarios. Full article

The rapid expansion of the Internet and communication technologies is leading to significant changes in both society and the economy. This development is driving the evolution of smart cities, which utilize cutting-edge technologies and data analysis to optimize efficiency and reduce waste in their infrastructure and services. As the number of mobile devices and embedded computers grows, new technologies, such as fifth-generation (5G) cellular broadband networks and the Internet of Things (IoT), are emerging to extend wireless network connectivity. These cities are often referred to as unmanned aerial vehicles (UAVs), highlighting their innovative approach to utilizing technology. To address the challenges posed by continuously varying perturbations, such as unknown states, gyroscopic disturbance torque, and parametric uncertainties, an adaptive recursive sliding mode control (ARSMC) has been developed. The high computational cost and high-order nonlinear behavior of UAVs make them difficult to control. The controller design is divided into two steps. First, a confined stability analysis is performed using controllability and observability to estimate the system’s stability calculation. Second, a Lyapunov-based controller design analysis is systematically tackled using a recursive design procedure. The strategy design aims to enhance robustness through Lyapunov stability-based mathematical analysis in the presence of considered perturbations. The ARSMC introduces new variables that depend on state variables, controlling parameters, and stabilizing functions to minimize unwanted signals and compensate for nonlinearities in the system. The paper’s significant contribution is to improve the controlled output’s rise time and stability time while ensuring efficient robustness. Full article

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. Full article

Recently, unmanned aerial vehicle (UAV)-oriented applications have been growing worldwide. Thus, there is a strong interest in using UAVs for applications requiring wide-area connectivity coverage. Such applications might be power line inspection, road inspection, offshore site monitoring, wind turbine inspections, and others. The utilization of cellular networks, such as the fifth-generation (5G) technology, is often considered to meet the requirement of wide-area connectivity. This study quantifies the performance of 5G-enabled UAVs when sensor data throughput requirements are within the 5G network’s capability and when throughput requirements significantly exceed the capability of the 5G network, respectively. Our experimental results show that in the first case, the 5G network maintains bounded latency, and the application behaves as expected. In the latter case, the overloading of the 5G network results in increased latency, dropped packets, and overall degradation of the application performance. Our findings show that offloading processes requiring moderate sensor data rates work well, while transmitting all the raw data generated by the UAV’s sensors is not possible. This study highlights and experimentally demonstrates the impact of critical parameters that affect real-life 5G-enabled UAVs that utilize the edge-offloading power of a 5G cellular network. Full article

This paper proposes an effective global path planning technique for cellular-connected UAVs to enhance the reliability of unmanned aerial vehicles’ (UAVs) flights operating beyond the visual line of sight (BVLOS). Cellular networks are considered one of the leading enabler technologies to provide a ubiquitous and reliable communication link for UAVs. First, this paper investigates a reliable aerial zone based on an extensive aerial drive test in a 4G network within a suburban environment. Then, the path planning problem for the cellular-connected UAVs is formulated under communication link reliability and power consumption constraints. To provide a realistic optimization solution, all constraints of the optimization problem are defined based on real-world scenarios; in addition, the presence of static obstacles and no-fly zones is considered in the path planning problem. Two powerful intelligent optimization algorithms, the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, are used to solve the defined optimization problem. Moreover, a combination of both algorithms, referred to as PSO-GA, is used to overcome the inherent shortcomings of the algorithms. The performances of the algorithms are compared under different scenarios in simulation environments. According to the statistical analysis of the aerial drive test, existing 4G base stations are able to provide reliable aerial coverage up to a radius of 500 m and a height of 85 m. The statistical analysis of the optimization results shows that PSO-GA is a more stable and effective algorithm to rapidly converge to a feasible solution for UAV path planning problems, with a far faster execution time compared with PSO and GA, about two times. To validate the performance of the proposed solution, the simulation results are compared with the real-world aerial drive test results. The results comparison proves the effectiveness of the proposed path planning method in suburban environments with 4G coverage. The proposed method can be extended by identifying the aerial link reliability of 5G networks to solve the UAV global path planning problem in the current 5G deployment. Full article

The recent combination of ambient backscatter communication (ABC) with non-orthogonal multiple access (NOMA) has shown great potential for connecting large-scale Internet of Things (IoT) in future unmanned aerial vehicle (UAV) networks. The basic idea of ABC is to provide battery-free transmission by harvesting the energy of existing RF signals of WiFi, TV towers, and cellular base stations/UAV. ABC uses smart sensor tags to modulate and reflect data among wireless devices. On the other side, NOMA makes possible the communication of more than one IoT on the same frequency. In this work, we provide an energy efficient transmission design ABC-aided UAV network using NOMA. This work aims to optimize the power consumption of a UAV system while ensuring the minimum data rate of IoT. Specifically, the transmit power of UAVs and the reflection coefficient of the ABC system are simultaneously optimized under the assumption of imperfect channel state information (CSI). Due to co-channel interference among UAVs, imperfect CSI, and NOMA interference, the joint optimization problem is formulated as non-convex, which involves high complexity and makes it hard to obtain the optimal solution. Thus, it is first transformed and then solved by a sub-gradient method with low complexity. In addition, a conventional NOMA UAV framework is also studied for comparison without involving ABC. Numerical results demonstrate the benefits of using ABC in a NOMA UAV network compared to the conventional UAV framework. Full article

The unmanned aerial vehicle (UAV) industry is moving toward beyond visual line of sight (BVLOS) operations to unlock future internet of drones applications, including unmanned environmental monitoring and long-range delivery services. A reliable and ubiquitous mobile communication link plays a vital role in ensuring flight safety. Cellular networks are considered one of the main enablers of BVLOS operations. However, the existing cellular networks are designed and optimized for terrestrial use cases. To investigate the reliability of provided aerial coverage by the terrestrial cellular base stations (BSs), this article proposes six machine learning-based models to predict reference signal received power (RSRP) and reference signal received quality (RSRQ) based on the multiple linear regression, polynomial, and logarithmic methods. In this regard, first, a UAV-to-BS measurement campaign was conducted in a 4G LTE network within a suburban environment. Then, the aerial coverage was statistically analyzed and the prediction methods were developed as a function of distance and elevation angle. The results reveal the capability of terrestrial BSs in providing aerial coverage under some circumstances, which mainly depends on the distance between the UAV and BS and flight height. The performance evaluation shows that the proposed RSRP and RSRQ models achieved RMSE of 4.37 dBm and 2.71 dB for testing samples, respectively. Full article

Visibility and communication are the essential pillars for safe flight operations in dense airspaces. Small Unmanned Aerial Vehicles (UAVs) of the order of up to 25 kg are increasingly being used at airports as a cost-effective alternative for maintenance and calibration work. However, the joint operation of manned and unmanned aircraft in busy airspaces poses a major challenge. Due to the small diameter of such UAVs, the established principle of “see and avoid” is difficult or even impossible to implement, especially during take-off and landing. For this reason, a certified Mode A/C/S transponder supporting ADS-B was extended with an embedded system and a cellular interface to realize a Multi-Mode-Transceiver (MMT). Integrated into a UAV, the MMT can provide aircraft visibility in the context of traditional manned Air Traffic Management (ATM) and future UAS Traffic Management (UTM) at the same time. This multimodal communication approach was investigated in flight test campaigns with two commercially available UAS that were connected to an experimental UTM with a simulated controlled airspace. The results confirm the safety gain of the multimodal cooperative approach. Furthermore, the collaborative interface with ATC enables the digital transmission of transponder codes, entry clearances and emergency procedures without the need for a voice radio communication. However, the parallel operation of both radio technologies in a confined space requires modifications to the transmission power and alignment of the radio antennas to avoid mutual interference. Furthermore, different reference planes of barometric altitude measurement in manned and unmanned aviation pose additional challenges that need to be addressed. Full article

One of the main targets of future 5G cellular networks is enlarging the Internet of Things (IoT) devices’ connectivity while facing the challenging requirements of the available bandwidth, power and the restricted delay limits. Unmanned aerial vehicles (UAVs) have been recently used as aerial base stations (BSs) to empower the line of sight (LoS), throughput and coverage of wireless networks. Moreover, non-orthogonal multiple access (NOMA) has become a bright multiple access technology. In this paper, NOMA is combined with UAV for establishing a high-capacity IoT uplink multi-application network, where the resource allocation problem is formulated with the objective of maximizing the system throughput while minimizing the delay of IoT applications. Moreover, power allocation was investigated to achieve fairness between users. The results show the superiority of the proposed algorithm, which achieves 31.8% delay improvement, 99.7% reliability increase and 50.8% fairness enhancement when compared to the maximum channel quality indicator (max CQI) algorithm in addition to preserving the system sum rate, spectral efficiency and complexity. Consequently, the proposed algorithm can be efficiently used in ultra-reliable low-latency communication (URLLC). Full article