In recent years, there has been a dramatic increase in the use of unmanned aerial vehicles (UAVs), particularly for small UAVs, due to their affordable prices, ease of availability, and ease of operability. Existing and future applications of UAVs include remote surveillance and monitoring, relief operations, package delivery, and communication backhaul infrastructure. Additionally, UAVs are envisioned as an important component of 5G wireless technology and beyond. The unique application scenarios for UAVs necessitate accurate air-to-ground (AG) propagation channel models for designing and evaluating UAV communication links for control/non-payload as well as payload data transmissions. These AG propagation models have not been investigated in detail when compared to terrestrial propagation models. In this paper, a comprehensive survey is provided on available AG channel measurement campaigns, large and small scale fading channel models, their limitations, and future research directions for UAV communication scenarios.

This paper describes an algorithm that exploits multipath propagation for position estimation of mobile receivers. We apply a novel algorithm based on recursive Bayesian filtering, named Channel-SLAM. This approach treats multipath components as signals emitted from virtual transmitters which are time synchronized to the physical transmitter and static in their positions. Contrarily to other approaches, Channel-SLAM considers also paths occurring due to multiple numbers of reflections or scattering as well as the combination. Hence, each received multipath component increases the number of transmitters resulting in a more accurate position estimate or enabling positioning when the number of physical transmitters is insufficient. Channel-SLAM estimates the receiver position and the positions of the virtual transmitters simultaneously, hence, the approach does not require any prior information such as a room-layout or a database for fingerprinting. The only prior knowledge needed is the physical transmitter position as well as the initial receiver position and moving direction. Based on simulations, the position precision of Channel-SLAM is evaluated by a comparison to simplified algorithms and to the posterior Cramér-Rao lower bound. Furthermore, the paper shows the performance of Channel-SLAM based on measurements in an indoor scenario with only a single physical transmitter.

No abstract

No abstract

Novel joint delay Doppler probability density functions for vehicle-to-vehicle communications channels are introduced. Prior measurements of vehicle-to-vehicle channels have unveiled their nonstationarity; thus, the wide-sense stationary and also the uncorrelated scattering assumption for such channels is often violated, which makes their modeling challenging. In this work it is proposed to exploit geometry-based stochastic modeling to cope with the nonstationarity of vehicle-to-vehicle channels. To this end, delay-dependent Doppler pdfs are derived for arbitrary times. It is assumed that scatterers are randomly distributed on an ellipse with two moving vehicles being in its foci. The proposed approach allows reducing the dimensionality of the resulting problem. This in turn leads to a significantly simplified derivation of the delay-dependent Doppler pdfs for general vehicle-to-vehicle propagation environments; moreover, the resulting computations can be performed almost fully analytically. By combining the calculated Doppler pdf with a delay pdf, the joint pdf of delay and Doppler is obtained. The joint pdf then can be put into relation with the generalized local scattering function. The presented modeling approach is simple yet very scalable and accurate, which allows its application in different vehicular scenarios. The obtained modeling results correspond very well with measurement data reported in prior works.Index Terms-Geometric-stochastic channel modeling, nonstationary modeling, scatter channel, vehicle-to-vehicle channel, wideband channel model.