Unmanned Aerial Vehicle Propagation Channel over Vegetation and Lake Areas: First- and Second-Order Statistical Analysis

Abstract

The use of unmanned aerial vehicles (UAV) to provide services such as the Internet, goods delivery, and air taxis has become a reality in recent years. The use of these aircraft requires a secure communication between the control station and the UAV, which demands the characterization of the communication channel. This paper aims to present a measurement setup using an unmanned aircraft to acquire data for the characterization of the radio frequency channel in a propagation environment with particular vegetation (Caatinga) and a lake. This paper presents the following contributions: identification of the communication channel model that best describes the characteristics of communication; characterization of the effects of large-scale fading, such as path loss and log-normal shadowing; characterization of small-scale fading (multipath and Doppler); and estimation of the aircraft speed from the identified Doppler frequency.

An Accurate Anchor-Free Contextual Received Signal Strength Approach Localization in a Wireless Sensor Network

“…Also, in [ 20 ], the authors proposed anchor-based range-free algorithms based on RSS measurements, namely support vector regression (SVR) and SVR + Kalman filter (KF). On the other hand, many studies, including [ 22 , 23 ], emphasize the importance of accurate signal propagation models in wireless communication systems, especially when accounting for phenomena like multi-path fading and shadowing effects. Traditional models like the lognormal distribution for large-scale shadowing effects [ 24 ] and the gamma distribution as an alternative [ 25 ] have their limitations.…”

Machine Learning-Based Air-to-Ground Channel Model Selection Method for UAV Communications Using Digital Surface Model Data

“…Various research results have been derived recently for studying A2G channel models in the air domain [ 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. Among them, representative results measured channel characteristics by mounting channel-sounding equipment on an aircraft and derived channel models based on the measured data.…”

“…Based on this, in [ 11 ], the measurement environment and A2G propagation scenario were organized by the topographical environments, and the difference in channel characteristics according to the different terrains was specified. Various studies have been conducted to develop A2G channel models in several types of topography, such as water surfaces [ 12 , 13 , 14 ], mountains [ 15 ], cities [ 16 , 17 ], and suburbs [ 16 ], based on actual measurement results using airplanes or UAVs. As a result, parameters such as pass loss, delay spread, K-factor, and the first and second-order statistics in the various frequency bands are different for each topography class.…”

A Tree Attenuation Factor Model for a Low-Power Wide-Area Network in a Ruby Mango Plantation

“…Low frequencies, such as 240 MHz, are less likely to be affected by weather conditions, such as rain and strong winds [10]. Identifying the communication channel pattern is important for describing the occurrence and nature of large-scale fading effects [11]. LoRa data transmission is vulnerable to near-ground effects and blockages caused by vegetation canopies and tree trunks, which interrupt the communication nodes and increase their energy consumption.…”