Due to the decrease in cost, size and weight, Unmanned Aerial Vehicles (UAVs) are becoming more and more popular for general-purpose civil and commercial applications. Provision of communication services to UAVs both for user data and control messaging by using off-the-shelf terrestrial cellular deployments introduces several technical challenges. In this paper, an approach to the air-to-ground channel characterization for low-height UAVs based on an extensive measurement campaign is proposed, giving special attention to the comparison of the results when a typical directional antenna for network deployments is used and when a quasi-omnidirectional one is considered. Channel characteristics like path loss, shadow fading, root mean square delay and Doppler frequency spreads and the K-factor are statistically characterized for different suburban scenarios.

In the world of Internet of Things (IoT), obtaining the physical location of devices has always been a task of great interest for developing increasingly complex location-based services (LBS). That is why in recent years wireless communication standards have been incorporating new additions focused on providing localization mechanisms to technologies widely used in the IoT world, such as Wi-Fi or Bluetooth. In particular, the IEEE 802.11-2016 Wi-Fi standard introduced ranging estimation between two devices through the so-called fine time measurement (FTM) protocol, defined by the IEEE 802.11mc. FTM is not yet widespread in the IoT field, but commercial modules capable of offering this functionality at a reasonable price are starting to appear. In early 2021, the most widespread system on a chip (SOC) family among IoT devices, the ESP32-XX series, added support for this Wi-Fi standard, enabling, for the first time, the use of a standard designed for location-based systems. This article analyzes the performance of this FTM implementation by carrying out and studying several measurement campaigns in different indoor and outdoor scenarios. Additionally, this work proposes an alternative real-time implementation for distance estimation inside the ESP32 using an approach based on machine learning. Such an implementation is successfully validated in a scenario totally different than those considered for the training and test sets. Finally, both the measurement sets and the developed software are available to the scientific community.

During the last few years, with the decrease of their sizes and costs, Unmanned Aerial Vehicles (UAVs) became more feasible for their use in general-purpose applications. As an important basis for UAV applications, the Air-to-Ground (A2G) radio propagation channel has gained attention in the channel modeling literature. However, whereas the A2G propagation channel is inherently dynamic (time-varying), the majority of the available models do not consider such a time-variability. This paper proposes a channel multi-path component (MPC) tracking algorithm and shows its ability to analyze the data collected by a real A2G measurement campaign in a suburban environment. Based on the obtained results, a time-varying statistical channel model for A2G communications in realistic suburban scenarios is proposed. The model is able to stochastically characterize parameters related to the birth and time-of-life of the multi-path components (MPCs), as well as their evolution in terms of delay, Doppler frequency or magnitude. Correlation coefficients to relate different channel characteristics are also obtained. Our work shows that the MPCs evolution over time for the UAV A2G channel can be described by simple regular patterns.

Ultra-wideband (UWB) technology enables centimeter-level localization systems based on the accurate estimation of the actual distance between transmitter and receiver, by means of the precise estimation of the signal time-of-flight. However, this is only possible when correctly detecting the first path of the incoming signal instead of a bounce or a reflection, which becomes challenging in non line-of-sight (NLOS) situations. There are many different approaches in the literature to alleviate the wrong detection of the first incoming UWB signal path. One of them considers machine learning techniques to design classifiers capable of distinguishing between line-of-sight (LOS) and NLOS propagation from available signal features. However, the performance and complexity of the obtained classifiers depend largely on the size of the input data associated to such features. Thus, features such as the channel impulse response (CIR) produce large amounts of data, yielding very complex classifiers. In this paper, we propose using a downsampled power delay profile (PDP) as an alternative feature consisting of input data much smaller than the CIR, although sufficiently representative, hence resulting in a lower computational cost while exhibiting a similar classification performance. Furthermore, another of the tasks addressed in this work is the study of the impact on the classification results of using a dataset for training where the samples of each class are not balanced from the point of view of energy. Finally, this work also studies how the classifiers based on the CIR or the PDP improve their performance when considering additional signal features such as the estimated range value or its energy level.