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In this paper, we derive a vehicle-to-vehicle (V2V) channel model assuming a typical propagation scenario in which the local scatterers move with random velocities in random directions. The complex channel gain of the proposed V2V channel model is provided. Subsequently, for different scatterer velocity distributions, the corresponding autocorrelation functions (ACFs) are derived, illustrated, and compared with the classical ACF derived under the assumption of fixed scatterers. Furthermore, under specific conditions, highly accurate approximations for the ACFs are provided in closed form. Since the proposed V2V channel model covers several communication scenarios as special cases, including fixed-to-vehicle (F2V) and fixed-to-fixed (F2F) scenarios in the presence of both fixed and moving scatterers, it is obvious that the presented results are important for the designers of cutting-edge vehicular communication systems.

The emerging non-wearable fall detection systems rely on processing radio waves reflected off the body of the home user who has no active interaction with the system, increasing the user privacy and acceptability. This paper proposes a nonstationary channel model that is important for the development of such systems. A three-dimensional stochastic trajectory model is designed to capture targeted mobility patterns of the home user. The model is featured with a forward fall mechanism, which is actuated at a random point along the path. A transmitter emits radio waves throughout an indoor propagation environment, while a receiver collects fingerprints of the scattering objects on the emitted waves. The corresponding radio channel is modelled by a process capturing the time-variant Doppler effect caused by the home occupant. The time-frequency behaviour of the non-stationary channel is studied by computing the Doppler power spectral density and by performing spectrogram analysis. The instantaneous mean Doppler shift and Doppler spread are derived and simulated. The model is confirmed with experimental results performed at 5.9 GHz. The results are insightful for developing reliable fall detection algorithms, while the model is useful for studying the impact of different walking/falling patterns on the overall fall detection system performance.

This paper introduces a new approach to develop stochastic non-stationary channel models, the randomness of which originates from a random trajectory of the mobile station (MS), rather than from the scattering area. The new approach is employed by utilizing a random trajectory model based on the primitives of Brownian fields (BFs), while the position of scatterers can be generated from an arbitrarily two-dimensional (2D) distribution function. The employed trajectory model generates random paths, along which the MS travels from a given starting point to a fixed predefined destination point.To capture the path loss, the gain of each multipath component is modelled by a negative power law applied to the travelling distance of the corresponding plane wave, while the randomness of the path travelled results in large-scale fading. It is shown that the local received power is well approximated by a Gaussian process in logarithmic scale even for a very limited number of scatterers. It is also shown that the envelope of the complex channel gain follows closely a Suzuki process, indicating that the proposed channel model superimposes small-scale fading and large-scale fading. The local power delay profile (PDP) and the local Doppler power spectral density (PSD) of the channel model are also derived and analyzed.

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