Machine Learning-Based Field Data Analysis and Modeling for Drone Communications

Abstract

In recent years, unmanned aerial vehicle (UAV), also called a drone, is getting more and more important in many emerging technology areas. For communication area, the drone also takes an important role in lots of significant topics like emergency communications, device-to-device (D2D) communications, and the Internet of Things (IoT). One of the important drone applications is to collect and share data among drones and other aircraft, which is useful for drone control so that dangerous conditions can be avoided. In particular, the drone control and safety guarantees are difficult to attain, especially, when drones fly beyond the line of sight (BLOS). For this reason, we develop a drone location information sharing system using the 920-MHz band. We use this system to do a long distance propagation field experiment for model establishment. Unfortunately, the current data collection for model establishment work needs a great effort and time to do experiments to collect a huge number of data for data analysis so that a suitable model can be established. Therefore, in this paper, we propose a novel method, which is based on machine learning approach, to data analysis and model establishment for drone communications, so that the effort and cost for establishing model can be reduced and a model, which captures more details about the drone communications, can be obtained. The results of this paper validate that the proposed method can indeed establish a more complicated model with less effort. Specifically, from the distribution of the training error, it can be known that there are over 80% training errors with intensity less than 5, which ensures the error performance of the proposed method.INDEX TERMS Unmanned aerial vehicle (UAV), drone communication, machine learning, Internet of Things (IoT).

Beamforming Using Uniform Spherical Arrays: Array Construction, Beam Characteristics, and Multi-Rank Transmission

“…Please note that no specific boresight exists for the isotropic source. When we apply the pattern in (10), the antenna boresight is aligned to the direction of the vector from the origin to the antenna element. The uniform beam characteristics of USAs can be confirmed by generating a pair of users in random directions over the 3D space and evaluating their mutual interference levels.…”

“…Evaluation results in earlier sections are based on the assumption that each antenna element has the isotropic radiation pattern. In order to understand the effect of antenna gains, we apply the radiation pattern in (10) to each antenna element of the spherical array and analyze the resulting transmission performance. The 3dB beamwidth specified in [48] is B 3dB = 65 • .…”

“…An increasing demand for vehicle-toeverything (V2X) communications also requires radio access and beam management (BM) techniques to handle highly mobile users in various locations [8], [9]. The coverage for radio access is now broadened to all directions in the entire three-dimensional (3D) space with aerial applications including drone mobiles and unmanned aerial vehicle (UAV) base stations [10]- [12]. It is very clear that aforementioned types of transceivers will benefit from antenna arrays capable of forming accurate beams and providing a seamless coverage in isotropic fashion.…”

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

“…Moreover, in the work done by [144], a comprehensive deep learning methodology was proposed to generate an absolute or relative point cloud estimation of a digital elevation model (DEM) given a single satellite or drone image for a wide range of applications and disciplines such as 3D flight planning, autonomous driving, and satellite navigation. Shan et al [145] also formed a new method based on a machine learning approach to collect and share data among drones and other aircraft, analyze data and establish models, and capture more detailed characteristics about drone communications, which is useful for avoiding hazardous conditions. It was shown the effectiveness of the proposed strategy in being able to better create complex models with less effort to improve drone control.…”

A survey of radio propagation channel modelling for low altitude flying base stations

“…LTE based), they can provide an initiative for a UAV enabled 3D channel models [93,94]. Furthermore, the placement of UAV BS for effective energy utilization with maximum coverage [95] while also taking into account the overall network delay [96], interference management from adjacent cells [97,98], dynamic spectrum access for UAV enabled networks [99], and 3D positing control [100] are vital research areas for UAV enabled network and required further investigation.…”