Unmanned Aerial Vehicle Scenario Research Articles

Suboptimal Trajectory Planning Technique in Real UAV Scenarios with Partial Knowledge of the Environment

In recent years, the issue of trajectory planning for autonomous unmanned aerial vehicles (UAVs) has received significant attention due to the rising demand for these vehicles across various applications. Despite advancements, real-time trajectory planning remains computationally demanding, particularly with the inclusion of 3D localization using computer vision or advanced sensors. Consequently, much of the existing research focuses on semi-autonomous systems, which rely on ground assistance through the use of external sensors (motion capture systems) and remote computing power. This study addresses the challenge by proposing a fully autonomous trajectory planning solution. By introducing a real-time path planning algorithm based on the minimization of the snap, the optimal trajectory is dynamically recalculated as needed. Evaluation of the algorithm’s performance is conducted in an unknown real-world scenario, utilizing both simulations and experimental data. The algorithm was implemented in MATLAB and subsequently translated to C++ for onboard execution on the drone.

An Unmanned Aerial Vehicle Indoor Low-Computation Navigation Method Based on Vision and Deep Learning.

Recently, unmanned aerial vehicles (UAVs) have found extensive indoor applications. In numerous indoor UAV scenarios, navigation paths remain consistent. While many indoor positioning methods offer excellent precision, they often demand significant costs and computational resources. Furthermore, such high functionality can be superfluous for these applications. To address this issue, we present a cost-effective, computationally efficient solution for path following and obstacle avoidance. The UAV employs a down-looking camera for path following and a front-looking camera for obstacle avoidance. This paper refines the carrot casing algorithm for line tracking and introduces our novel line-fitting path-following algorithm (LFPF). Both algorithms competently manage indoor path-following tasks within a constrained field of view. However, the LFPF is superior at adapting to light variations and maintaining a consistent flight speed, maintaining its error margin within ±40 cm in real flight scenarios. For obstacle avoidance, we utilize depth images and YOLOv4-tiny to detect obstacles, subsequently implementing suitable avoidance strategies based on the type and proximity of these obstacles. Real-world tests indicated minimal computational demands, enabling the Nvidia Jetson Nano, an entry-level computing platform, to operate at 23 FPS.

A High-Precision Detection Model of Small Objects in Maritime UAV Perspective Based on Improved YOLOv5

Object detection by shipborne unmanned aerial vehicles (UAVs) equipped with electro-optical (EO) sensors plays an important role in maritime rescue and ocean monitoring. However, high-precision and low-latency maritime environment small-object-detection algorithms remain a major challenge. To address this problem, this paper proposes the YOLO-BEV (“you only look once”–“bird’s-eye view”) model. First, we constructed a bidirectional feature fusion module—that is, PAN+ (Path Aggregation Network+)—adding an extremely-small-object-prediction head to deal with the large-scale variance of targets at different heights. Second, we propose a C2fSESA (Squeeze-and-Excitation Spatial Attention Based on C2f) module based on the attention mechanism to obtain richer feature information by aggregating features of different depth layers. Finally, we describe a lightweight spatial pyramid pooling structure called RGSPP (Random and Group Convolution Spatial Pyramid Pooling), which uses group convolution and random channel rearrangement to reduce the model’s computational overhead and improve its generalization ability. The article compares the YOLO-BEV model with other object-detection algorithms on the publicly available MOBDrone dataset. The research results show that the mAP0.5 value of YOLO-BEV reached 97.1%, which is 4.3% higher than that of YOLOv5, and the average precision for small objects increased by 22.2%. Additionally, the YOLO-BEV model maintained a detection speed of 48 frames per second (FPS). Consequently, the proposed method effectively balances the accuracy and efficiency of object-detection in shipborne UAV scenarios, outperforming other related techniques in shipboard UAV maritime object detection.

A Distributed Algorithm for UAV Cluster Task Assignment Based on Sensor Network and Mobile Information

Cluster formation and task processing are standard features for leveraging the performance of unmanned aerial vehicles (UAVs). As the UAV network is aided by sensors, functions such as clustering, reformation, and autonomous working are adaptively used for dense task processing. In consideration of the distributed nature of the UAV network coupled with wireless sensors, this article introduces a Rational Clustering Method (RCM) using dense task neighbor information exchange. The Rational Clustering Method (RCM) is an algorithm for dense task neighbor information exchange that can be used to cluster objects according to their shared properties. Each object’s task neighbors, and the similarities between them, are calculated using this method. Starting with the task density of its neighbors, the RCM algorithm gives each object in the dataset a weight. This information exchange process identifies a UAV units’ completing tasks and free slots. Using this information, high-slot UAVs within the communication range can be grouped as clusters. Unlike wireless sensor clusters, task allocation is performed on the basis of available slots and UAV longevity within the cluster; this prevents task incompletion/failures and delays in a densely populated UAV scenario. Cluster sustainability or dispersion is recommended when using distributed state learning. State learning transits between the pending task and UAV longevity; an intermediate state is defined for task reassignment amid immediate cluster deformation. This triple feature-based distributed method balances tasks between failures, overloading, and idle UAVs. The RCM was verified using task processing rate, completion ratio, reassignment, failures, and delay. Task processing rate was increased by 8.16% and completion ratio was increased by 10.3% with the proposed RCM-IE. Reassignment, failure, and delay were all reduced by 12.5%, 9.87%, and 11.99%, respectively, using this method.

UAVStereo: A Multiple Resolution Dataset for Stereo Matching in UAV Scenarios

Stereo matching is a fundamental task in three-dimensional scene reconstruction. Recently, deep learning-based methods have proven effective on some benchmark datasets, such as KITTI and SceneFlow. Unmanned aerial vehicles (UAVs) are commonly used for surface observation, and the images captured are frequently used for detailed 3D reconstruction because of their high resolution and low-altitude acquisition. Currently, mainstream supervised learning networks require a significant amount of training data with ground-truth labels to learn model parameters. However, owing to the scarcity of UAV stereo-matching datasets, learning-based stereo matching methods in UAV scenarios is not fully investigated yet. To facilitate further research, this study proposes a pipeline for generating accurate and dense disparity maps using detailed meshes reconstructed based on UAV images and LiDAR point clouds. Through the proposed pipeline, we constructed a multi-resolution UAV scenario dataset called UAVStereo, with over 34,000 stereo image pairs covering three typical scenes. To the best of our knowledge, UAVStereo is the first stereo matching dataset for UAV low-altitude scenarios. The dataset includes synthetic and real stereo pairs to enable generalization from the synthetic domain to the real domain. Furthermore, our UAVStereo dataset provides multi-resolution and multi-scene image pairs to accommodate various sensors and environments. In this study, we evaluated traditional and state-of-the-art deep learning methods, highlighting their limitations in addressing challenges in UAV scenarios and offering suggestions for future research. Our dataset is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/rebecca0011/UAVStereo.git.</uri>

Correlation Filter of Multiple Candidates Match for Anti-Obscure Tracking in Unmanned Aerial Vehicle Scenario

Due to the complexity of Unmanned Aerial Vehicle (UAV) target tracking scenarios, tracking drift caused by target occlusion is common and has no suitable solution. In this paper, an occlusion-resistant target tracking algorithm based on the correlated filter tracking model is proposed. First, instead of the traditional target tracking model that uses single template matching to locate the target, we locate the target by finding the optimal match based on multiple candidates templates matching. Then, in order to increase the accuracy of matching, we use the self-attentive mechanism for feature enhancement. We experiment our proposed algorithm on datasets OTB100 and UAV123, respectively, and the results show that the tracking accuracy of our algorithm outperforms the traditional correlated filtered target tracking model. In addition, we have also tested the anti-occlusion performance of our proposed algorithm on some video sequences in which the target is occluded. The results show that our proposed algorithm has a certain resistance to occlusion, especially in the UAV tracking scenario.

Opportunities for Physical Layer Security in UAV Communication Enhanced with Intelligent Reflective Surfaces

Unmanned aerial vehicles (UAVs) are an important component of next-generation wireless networks that can assist in high data rate communications and provide enhanced coverage.Their high mobility and aerial nature offer deployment flexibility and low-cost infrastructure support to existing cellular networks and provide many applications that rely on mobile wireless communications. However, security is a major challenge in UAV communications, and physical layer security (PLS) is an important technique to improve the reliability and security of data shared with the assistance of UAVs. Recently, the intelligent reflective surface (IRS) has emerged as a novel technology to extend and/or enhance wireless coverage by reconfiguring the propagation environment of communications. This article provides an overview of how the IRS can improve the PLS of UAV networks. We discuss different use cases of PLS for IRS-enhanced UAV communications and briefly review the recent advances in this area. Then, based on the recent advances, we also present a case study that utilizes alternate optimization to maximize the secrecy capacity for an IRS-enhanced UAV scenario in the presence of multiple Eves. Finally, we highlight several open issues and research challenges to realize PLS in IRS-enhanced UAV communications.

Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks

Unmanned aerial vehicles (UAV) have emerged as prime technologies due to their compatible size and flexible architecture. UAV technology offers services in vast application such as inter-UAV communication, wireless sensors, and the future Internet of Things (IoT) due to its compatible architecture. A UAV’s speed varies while roaming, which may increase the risk of a connection failure. Various routing schemes have provided solutions to address this essential issue for three-dimensional (3D) UAV-based networks. The main category of UAV routing schemes is position-based routing schemes, which choose the best route based on the UAV’s location. However, position-based routing has the drawback that it depends on exact positioning and tracking. An efficient routing scheme can resolve the significant issue associated with UAV mobility in a 3D environment. This paper aims to address the issues of static preloaded location values by presenting a hybrid routing scheme named the Position-Monitor-based Hybrid Routing Protocol (PMHRP), which takes advantage of both geographic and topology-based routing protocols. The PMHRP establishes the shortest possible route based on a UAV’s Global Positioning System (GPS). Moreover, the proposed protocol utilizes the links for data forwarding. Furthermore, a disaster-based UAV scenario is adopted to provide connections to IoT devices. A detailed comparison analysis shows the proposed scheme’s extreme performance and results in up to 65% to 73% better packet delivery ratio (PDR) than batch mark schemes under standard 3D UAV scenarios. Compared to earlier work, the proposed scheme reduces the average delay by up to 68% to 75%. Further proposed routing schemes offer 70% to 72% more throughput than the existing routing schemes, and NRL (%) is 42% to 49% lower than the existing routing schemes. This happens because of the global routing information available at each UAV which is provided by the position head coordinator (PHC) UAV in the proposed work.

Impact of Antenna Pattern on TOA Based 3D UAV Localization Using a Terrestrial Sensor Network

In this article, we explore the fundamental limits of the 3-dimensional (3D) localization of unmanned aerial vehicles (UAVs) in conjunction with the effects of 3D antenna radiation patterns. Although localization of UAVs has been studied to some extent in the literature, effects of antenna characteristics on 3D localization remains mostly unexplored. To study such effects, we consider a scenario where a fixed number of radio-frequency (RF) sensors equipped with single or multiple dipole antennas are placed at some known locations on the ground, and they derive the time-difference-of-arrival (TDOA) measurements from the time-of-arrival (TOA) data collected for the UAV that is also equipped with a dipole antenna. We then use these measurements to estimate the 3D location of the UAV, and to derive the Cramer-Rao lower bounds (CRLBs) on the localization error for various orientations of the dipole antennas at the transmitter and the receiver. Namely, we consider vertical-vertical (VV), horizontal-horizontal (HH), and vertical-horizontal (VH) radiation patterns in a purely line-of-sight (LoS) environment and a mixed LoS/Non-line-of-sight (NLoS) environment. We show that the localization accuracy changes in a non-monotonic pattern with respect to the UAV altitude and identify the respective critical altitudes for each of the VV, VH and HH orientations. Subsequently, we propose a multi-antenna signal acquisition technique that mitigates the accuracy degradation due to the antenna pattern mismatches, and we derive the localization CRLB for the multi-antenna scenario. Our numerical results characterize achievable localization accuracy for various antenna configurations, UAV heights, and propagation conditions for representative UAV scenarios.

Modeling and Characterization of Traffic Flow Patterns and Identification of Airspace Density for UTM Application

Current airspace has limited resources, and the widespread use of unmanned aerial vehicles (UAVs) increases airspace density, which is already crowded with manned aircraft. This demands the improvement of airspace safety and capacity while considering all parametric uncertainties that may hinder aircraft and UAV mobility such as dynamic airspace structures and weather conditions. This paper proposes a data analytics framework to characterize traffic flow patterns of unmanned traffic management (UTM) airspace by analyzing simulated historical data. Mission patterns are characterized and identified by considering multiple UAV missions and scenarios with different priority levels to highlight UAVs’ trajectories and deviations from the actual path due to these constraints. The pertinent data analysis supports risk analysis and improves trajectory planning in different airspace regions considering all dynamic parameters such as extreme weather, emergency services, and dynamic airspace structures. The data processing framework, which is density-based spatial clustering of applications with noise (DBSCAN), identified significant deviations in mission patterns with almost 82% confidence level. The UTM traffic flow characterization is conducted by three key characterization parameters mainly Distance from Centroid (DFC), Distance to Complete Mission (DTCM) and Time to Complete Mission (TTCM). This work also analyzed the airspace congestion using the Kernel density estimation (KDE). This analysis identified some regions of interference as potential congested areas representing safety concerns. The proposed framework is envisioned to assist UTM authority by characterizing air traffic behavior, managing its flow, improving airspace design, and providing the basis for developing predictive capabilities that support traffic flow management.

Adding unmanned aerial vehicles to HIV supply chains in remote settings: modeling feasibility and cost in Turkana, Kenya

Background Viral load (VL) testing of patients on antiretroviral therapy (ART) is critical to suppressing HIV. Timely transport of VL samples from health facilities to the laboratory for testing is often problematic in Turkana County, Kenya, and transportation barriers cause delays that can negatively impact the health of ART patients. Unmanned Aerial Vehicles (UAVs), or drones, are being promoted as a potential, novel way to reduce transportation times for laboratory samples and medical commodities. Our study models the use of UAVs in a remote area of Kenya to estimate costs and feasibility of their application for the Afya Nyota ya Bonde project, a large HIV service delivery program. Methods We gathered data from 12 Afya-supported facilities on VL sample transportation and turnaround time, ART drug transportation, and transportation practices and costs. The annual cost and average turnaround time for the transportation of VL samples and ART drugs was calculated using baseline data from the program to establish a comparison for scenarios that use a UAV. We then designed and costed four transportation scenarios that used UAVs to pick up VL samples and deliver ART drugs through a logistics company that will be adding UAV services. Results The modeled scenarios demonstrate that UAVs could transport nearly 100% of VL samples to the lab within the three-day goal compared to the 3% that are transported under the current system. Additionally, using UAVs could reduce the burden on health workers who must personally transport the samples, reducing opportunity costs resulting from facility closures due to provider absence. However, the UAV scenario cost more than three times the current costs (US$ 56,350/Kenyan Shillings (KSh) 5,635,000 per year compared to between US$ 172,878/Ksh 17,287,800 and US$ 200,208/Ksh 20,020,800), and the consolidation of VL testing days required to accommodate the UAV may be problematic for ART patients. Conclusions As the cost of UAV transport declines, this technology could be an important tool for optimizing the transport of lab samples and medications.

Real-Time Collision-Free Navigation of Multiple UAVs Based on Bounding Boxes

Predictably, future urban airspaces will be crowded with autonomous unmanned aerial vehicles (UAVs) offering different services to the population. One of the main challenges in this new scenario is the design of collision-free navigation algorithms to avoid conflicts between flying UAVs. The most appropriate collision avoidance strategies for this scenario are non-centralized ones that are dynamically executed (in real time). Existing collision avoidance methods usually entail a high computational cost. In this work, we present Bounding Box Collision Avoidance (BBCA) algorithm, a simplified velocity obstacle-based technique that achieves a balance between efficiency and cost. The performance of the proposal is analyzed in detail in different airspace configurations. Simulation results show that the method is able to avoid all the conflicts in two UAV scenarios and most of them in multi-UAV ones. At the same time, we have found that the penalty of using the BBCA collision avoidance technique on the flying time and the distance covered by the UAVs involved in the conflict is reasonably acceptable. Therefore, we consider that BBCA may be an excellent candidate for the design of collision-free navigation algorithms for UAVs.

Impact of UAV Wobbling on the Air-to-Ground Wireless Channel

This paper studies the impact of unmanned aerial vehicle (UAV) wobbling on the coherence time of the wireless channel between UAVs and a ground user equipment (UE), using a Rician multi-path channel model. We consider two different scenarios for the number of UAVs: (i) single UAV scenario (SUS), and (ii) multiple UAV scenario (MUS). For each scenario, we model UAV wobbling by two random processes, i.e., the Wiener and sinusoidal processes, and characterize the channel autocorrelation function (ACF) which is then used to derive the coherence time of the channel. For the MUS, we further show that the UAV-UE channels for different UAVs are uncorrelated from each other. A key observation in this paper is that even for small UAV wobbling, the coherence time of the channel may degrade quickly, which may make it difficult to track the channel and establish a reliable communication link.

UAV object tracking by background cues and aberrances response suppression mechanism

Real-time object tracking for unmanned aerial vehicles (UAVs) is an essential and challenging research topic for computer vision. However, the scenarios that UAVs deal with are complicated, and the UAV tracking targets are small. Therefore, general trackers often fail to take full advantage of their performance in UAV scenarios. In this paper, we propose a tracking method for UAV scenes, which utilizes background cues and aberrances response suppression mechanism to track in 4 degrees-of-freedom. Firstly, we consider the tracking task as a similarity measurement problem. In this study, we decompose this problem into two subproblems for optimization. Secondly, to alleviate the problem of small targets in UAV scenes, we utilize background cues fully. Also, to reduce interference by background information, we employ an aberrance response suppression mechanism. Then, to obtain accurate target state information, we introduce a logarithmic polar coordinate system. We perform phase correlation calculations in logarithmic polar coordinates to obtain the rotation and scale changes of the target. Finally, target states are obtained through response fusion, which includes displacement, scale, and rotation angle. Our approach is carried out in a large number of experiments on various UAV datasets, such as UAV123, DBT70, and UAVDT2019. Compared with the current advanced trackers, our method is superior on UAV small target tracking.

Joint 3D Maneuver and Power Adaptation for Secure UAV Communication With CoMP Reception

This paper studies a secrecy unmanned aerial vehicle (UAV) communication system with coordinated multi-point (CoMP) reception, in which one UAV sends confidential messages to a set of cooperative ground receivers (GRs), in the presence of several suspicious eavesdroppers. In particular, we consider two types of eavesdroppers that are non-colluding and colluding, respectively. Under this setup, we exploit the UAV's maneuver in three dimensional (3D) space together with transmit power adaptation for optimizing the secrecy communication performance. First, we consider the quasi-stationary UAV scenario, in which the UAV is placed at a fixed but optimizable location during the communication period. In this scenario, we jointly optimize the UAV's 3D placement and transmit power control to maximize the secrecy rate. Under both non-colluding and colluding eavesdroppers, we obtain the optimal solutions to the joint 3D placement and transmit power control problems in well structures. Next, we consider the mobile UAV scenario, in which the UAV has a mission to fly from an initial location to a final location during the communication period. In this scenario, we jointly optimize the UAV's 3D trajectory and transmit power allocation to maximize the average secrecy rate during the whole communication period. To deal with the difficult joint 3D trajectory and transmit power allocation problems, we present alternating-optimization-based approaches to obtain high-quality solutions. Finally, we provide numerical results to validate the performance of our proposed designs. It is shown that due to the consideration of CoMP reception, our proposed design with 3D maneuver significantly outperforms the conventional design with two dimensional (2D) (horizontal) maneuver only, by exploiting the additional degrees of freedom in altitudes. It is also shown that the non-colluding and colluding eavesdroppers lead to distinct 3D UAV maneuver behaviors, e.g., under colluding eavesdroppers, the UAV should fly farther apart from them (than that under the non-colluding ones) for avoiding their collaborative interception.

Research on multi-UAV communication connectivity maintenance based on dynamic programming

With the continuous development of unmanned aerial vehicle (UAV) technology and its falling cost, UAV has been widely used in important fields, such as agriculture, industry, security, and military. In operation, UAVs often need to maintain communication connectivity with ground stations. However, in the case of long distance or shadowing, the quality of the wireless channels will be seriously degraded, which will affect the communication connectivity. To solve this problem, this paper considers deploying multiple UAVs as communication relays. In this paper, we select the long-term channel capacity as the optimization objective and propose two planning methods for UAV relays based on dynamic programming (DP): CMMP-AT and CMMP-OBO. The CMMP-AT method emphasizes the deployment of all relay UAVs at the beginning of the task. This method is simpler to implement, but the computational complexity is intolerable. The CMMP-OBO method proposes to deploy relay UAVs one by one according to the task requirements. Although the CMMP-OBO method may lose performance, it has a lower computational complexity and better scalability and can save more motion energy. The experimental results reveal that the CMMP-AT method requires 40.03 h to obtain the planning results for a two-relay scenario, whereas the CMMP-OBO method only needs 57.66 s and can save 3.87% motion energy. In addition, to precisely control the relay UAVs to follow the planned trajectory, this paper implements trajectory tracking, based on model predictive control (MPC), and realizes the simulation of the multi-relay UAV scenario in the VREP environment. The simulation results reveal that compared with the PID control method, the MPC method can track the planned trajectory more accurately. In two simulation scenarios, the tracking error of MPC is only 43.97% and 41.42% of the PID control method.

A Two-Stage Reinforcement Learning Approach for Multi-UAV Collision Avoidance Under Imperfect Sensing

Unlike autonomous ground vehicles (AGVs), unmanned aerial vehicles (UAVs) have a higher dimensional configuration space, which makes the motion planning of multi-UAVs a challenging task. In addition, uncertainties and noises are more significant in UAV scenarios, which increases the difficulty of autonomous navigation for multi-UAV. In this letter, we proposed a two-stage reinforcement learning (RL) based multi-UAV collision avoidance approach without explicitly modeling the uncertainty and noise in the environment. Our goal is to train a policy to plan a collision-free trajectory by leveraging local noisy observations. However, the reinforcement learned collision avoidance policies usually suffer from high variance and low reproducibility, because unlike supervised learning, RL does not have a fixed training set with ground-truth labels. To address these issues, we introduced a two-stage training method for RL based collision avoidance. For the first stage, we optimize the policy using a supervised training method with a loss function that encourages the agent to follow the well-known reciprocal collision avoidance strategy. For the second stage, we use policy gradient to refine the policy. We validate our policy in a variety of simulated scenarios, and the extensive numerical simulations demonstrate that our policy can generate time-efficient and collision-free paths under imperfect sensing, and can well handle noisy local observations with unknown noise levels.

Analytical Channel Models for Millimeter Wave UAV Networks Under Hovering Fluctuations

The integration of unmanned aerial vehicles (UAVs) and millimeter wave (mmWave) wireless systems has been recently proposed to provide high data rate aerial links for next generation wireless networks. However, establishing UAV-based mmWave links is quite challenging due to the random fluctuations of hovering UAVs which can induce antenna gain mismatch between transmitter and receiver. To assess the benefit of UAV-based mmWave links, in this paper, tractable, closed-form statistical channel models are derived for three UAV communication scenarios: (i) a direct UAV-to-UAV link, (ii) an aerial relay link in which source, relay, and destination are hovering UAVs, and (iii) a relay link in which a hovering UAV connects a ground source to a ground destination. The accuracy of the derived analytical expressions is corroborated by performing Monte-Carlo simulations. Numerical results are then used to study the effect of antenna directivity gain under different channel conditions for establishing reliable UAV-based mmWave links in terms of achieving minimum outage probability. It is shown that the performance of such links is largely dependent on the random fluctuations of hovering UAVs. Moreover, higher antenna directivity gains achieve better performance at low SNR regime. Nevertheless, at the high SNR regime, lower antenna directivity gains result in a more reliable communication link. The developed results can therefore be applied as a benchmark for finding the optimal antenna directivity gain of UAVs under the different levels of instability without resorting to time-consuming simulations.

Machine learning based null allocation design for adaptive beamforming in unmanned aerial vehicle communications

In order to communicate with target unmanned aerial vehicles (UAVs), ground control stations (GCSs) typically adopt adaptive beamforming with high antenna gain and co-channel interference rejection. Multiple interfering signals arriving from different directions arise from other UAVs and other GCSs, and the beamformer installed in the home GCS will usually attempt to null unwanted signals from all these directions of arrival (DoAs) without analyzing the distribution of the angles of arrival. Consequently, the beamformer will fail to allocate nulls in some directions, and the signal-to-interference-plus-noise (SINR) performance of the home GCS is impaired. In this paper, a new approach to null allocation is proposed, based on machine learning using k-means clustering. The design first involves the collection of information about the DoAs and the corresponding received signal strengths of all the interfering signals into a two-dimensional dataset. Secondly, this dataset is broken down into clusters by using k-means clustering, and the cluster centroids are calculated. In each cluster, the interfering signal that has the shortest Euclidean distance to the centroid is identified as the approximated centroid. Only the approximated centroids are selected as input to the beamformer, with the aim that each complete cluster of interference sources can be nulled by allocating one null per cluster. To optimize the number of clusters k used in the null allocation process, the design adopts the particle swarm optimization technique to adaptively update the value of k to maximize the SINR at the home GCS. Simulation results show that our design yields a maximum SINR improvement of about 12 dB when compared to cases where no null allocation is considered. Moreover, our design also outperforms null steering in the UAV scenarios. Advantageously, this enhanced performance is obtained without the need for additional power amplification or hardware modification to the beamformer.

Task number maximization offloading strategy seamlessly adapted to UAV scenario

Mobile edge computing (MEC) has been proposed in recent years to process resource-intensive and delay-sensitive applications at the edge of mobile networks, which can break the hardware limitations and resource constraints at user equipment (UE). In order to fully use the MEC server resource, how to maximize the number of offloaded tasks is meaningful especially for crowded place or disaster area. In this paper, an optimal partial offloading scheme POSMU (Partial Offloading Strategy Maximizing the User task number) is proposed to obtain the optimal offloading ratio, local computing frequency, transmission power and MEC server computing frequency for each UE. The problem is formulated as a mixed integer nonlinear programming problem (MINLP), which is NP-hard and challenging to solve. As such, we convert the problem into multiple nonlinear programming problems (NLPs) and propose an efficient algorithm to solve them by applying the block coordinate descent (BCD) as well as convex optimization techniques. Besides, we can seamlessly apply POSMU to UAV (Unmanned Aerial Vehicle) enabled MEC system by analyzing the 3D communication model. The optimality of POSMU is illustrated in numerical results, and POSMU can approximately maximize the number of offloaded tasks compared to other schemes.