Energy-Harvesting Aided Unmanned Aerial Vehicles for Reliable Ground User Localization and Communications Under Lognormal-Nakagami-$m$ Fading Channels

Abstract

In this paper, we propose a wireless localization system based on energy-harvesting aided unmanned aerial vehicles (UAV). Our proposed system consists of a ground station (GS), a UAV, and multiple users located on the ground, in which both the ground station and all the ground users (GUs) want to know the locations of the ground users. To this end, the UAV first harvests energy from the GS, and then broadcasts signals to the GUs for localization. Each GU will estimate its location, and then transmits data, including its location information, to the GS with the help of the UAV. The links between nodes experience both large-scale lognormal shadowing and small-scale Nakagami-m fading. We first derive the Cramer-Rao lower bound (CRLB) under spatially correlated shadowing for localization performance evaluation. Next, we analyze the system throughput under delay-limited and delay-tolerant transmission modes. To derive exact closed-form expressions as well as high signal-to-noise ratio (SNR) approximations of the performance metrics, we consider a mixture gamma distribution approximation for the probability density functions (PDF) of the composite fading channels. We evaluate the impact of several key system parameters such as the number of waypoints and the altitude of the UAV, correlated shadowing and energy-harvesting time both on the localization performance and on the achievable throughput. Simulations are provided to validate the theoretical analysis.

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

“…A change in view angle is used to obtain a deeper understanding of the scene's 3D structure [100]. For example, the fusion of LiDAR and RGB camera images enhances the mapping process to represent the environment [50]. The localization methods use image recognition to locate UAVs while simultaneously mapping the environment, perforingm sensor fusion, and perforingm VO to estimate the UAV's displacement [52].…”

“…C-SLAM considers the global perspective for the position and map of each UAV to estimate the shared global reference frame [50]. When a UAV can partially see a landmark in one location, then q n is not known for all l j .…”

Wireless-Powered Full-Duplex UAV Relay Networks Over FTR Channels

“…In [26], the performance of a HD UAV-assisted cognitive radio network with underlay spectrum sharing operating under the DF protocol with EH was analyzed in terms of the secrecy outage probability over Nakagami-m fading channels. Recently, in [27], the exact closed-form expressions for the outage probability and throughput were derived for a wireless localization system based on EH, aided by a HD-DF UAV, over Nakagami-m fading and lognormal shadowing to take into account small-and large-scale effects. However, in both [26], [27], only the HD relaying mode was considered, while SWIPT techniques were not employed, thus diminishing the overall network spectral efficiency when compared to joint use of FD-and SWIPT-based approaches.…”

“…Recently, in [27], the exact closed-form expressions for the outage probability and throughput were derived for a wireless localization system based on EH, aided by a HD-DF UAV, over Nakagami-m fading and lognormal shadowing to take into account small-and large-scale effects. However, in both [26], [27], only the HD relaying mode was considered, while SWIPT techniques were not employed, thus diminishing the overall network spectral efficiency when compared to joint use of FD-and SWIPT-based approaches. More recently, in [28], [29], the use of a FD-UAV relay in wireless networks was investigated.…”

“…where we have used the definitions of X and U given in (25) and (27), respectively. The integration region R 1 for the outage event in the argument of Pr(•) in ( 39) is illustrated in Fig.…”