Statistical modeling of small-scale fading in directional radio channels

Abstract

After a brief review of the known description of time-variant channels by means of system and correlation functions, a consistent extension of this description to directional time-variant channels is described in the present paper. This extension allows a clear distinction between time-and space-variant effects in directional mobile radio channels. The major intention of the described directional extension however is the derivation of a statistical modeling approach for small-scale fading effects in time-variant wideband directional channels, which can be regarded as a consistent extension of the well established Rayleighor Rice-fading approach for nondirectional time-variant narrowband channels. The approach, which is based on the time and aperture-variant transfer function, appears to be preferable to the frequently used statistical modeling of the time-variant angle-resolved impulse response for several reasons. The major advantage is that the approach can cope with the demand for a great number of superimposing components as the basis for statistical modeling. The correlation between adjacent values is proposed to be achieved by filtering with appropriate directional scattering functions. The description of the modeling approach, as done in the present paper, is intended to be general and universal; for the application on certain channel types statistical distribution functions and parameters to be used with the approach can readily be determined from appropriate measurements.

The COST259 Directional Channel Model-Part I: Overview and Methodology

“…The summing up of the powers coming from different directions entails the assumption that these contributions are uncorrelated. This assumption is equivalent to the "extended WSSUS" assumption, which is discussed, e.g., in [33], and [9].…”

“…1 General modeling methodologies have been presented both for spatial wide-sense stationary uncorrelated scattering (WSSUS) [9] and geometrically-based approaches [10], [11], but these do not contain detailed parameterization for different environments. For indoor channels, the Saleh-Valenzuela model [12] has been generalized to include directional information in [13].…”

Experimental Characterization and Modeling of Outdoor-to-Indoor and Indoor-to-Indoor Distributed Channels

“…For moving measurements, time-, space-and frequencyselective fading share the same cause [23]: the motion of the node causes phase shifts in each multipath. Hence the fading statistics over time at a given frequency are similar to the statistics over frequency at any given time.…”

“…non-applicable non-applicable see (23) see (22) similarly to (30). Note that the correlation value for the I2I-single mobile-common moving case in Table III cannot be found directly in Table II, as we have aggregated single mobile Rx and single mobile Tx cases to build Table III. 3) Fading: The small-scale fading g nm is best described in amplitude by a Ricean distribution in nomadic cases (the Kfactor being related to the distance, see (20)), while in mobile scenarios, the SOSF distribution is used to model the fading amplitude, with (α, β) randomly distributed as given in Table III.…”

A Nonstationary Wideband MIMO Channel Model for High-Mobility Intelligent Transportation Systems

“…The received signal experiencing non-stationary wideband mobile channels can be defined as a 3-dimensional (3D) stochastic process in terms of time t, delay τ , and space x, which denotes the location of an antenna element in the antenna array in the Tx/Rx [36]. It can be described by the spacetime-variant channel impulse response h(t, τ, x) [36], [37]. An alternative description of the non-stationary channel in space-time-frequency domain is the space-time-variant transfer function that can be obtained by taking the Fourier transform of h (t, τ, x) in terms of delay τ , i.e.,…”