Influence of time interval and filter bandwidth on measured rain fade slope

Abstract

[1] The rate of change of rain attenuation, or 'fade slope,' observed on a microwave link, is dependant on system parameters of the receiver. This paper assesses its dependence on two data processing parameters: the time interval over which the fade slope is defined, and the low-pass filter bandwidth used to remove tropospheric scintillation. A theoretical expression of this dependence is derived. The expression is compared with measured results, which show a good agreement. The results show that the time interval has a strong influence when it is larger than the inverse of the filter bandwidth, and the filter bandwidth has a strong influence when its inverse is larger than the time interval. In general, the fade slope always depends strongly on at least one of the two parameters.

Rain fade slope predicted from rain rate data

“…[12] As a first step, the variance of rain fade slope s zr 2 derived from measured data will be compared to an equivalent parameter determined from measured rain rate data [van de Kamp, 2005b].…”

“…Commonly used filter bandwidths are in the order of 0.01–0.02 Hz [ Poiares Baptista and Davies , 1994]. The resulting fade slope of the filtered signal is dependent on the filter bandwidth used [ van de Kamp and Clérivet , 2004; ITU , 2005].…”

“…Values of Δ t used in practice vary between 2 s [ van de Kamp , 2003a] and 10 s [ Feil et al , 1997]. The resulting fade slope depends also on Δ t [ van de Kamp and Clérivet , 2004; ITU , 2005].…”

“…An empirical prediction model of fade slope, based on measurements from various sites [van de Kamp, 2003a] is currently recommended by International Telecommunication Union (ITU) [2005]. An overview of published measurements and models is given by van de Kamp and Castanet [2002].…”

“…[9] The variance of relative fade slope s zr 2 is useful for more purposes than the prediction of fade slope statistics: with Dt = 10 s it is also one of the parameters from which the input parameters of the ''two-sample model'' can be derived, as is shown by van de Kamp [2003bKamp [ , 2005a. The two-sample model [van de Kamp, 2002;van de Kamp, 2003c] predicts the probability distribution of rain attenuation a short time after a measured value, dependent on the values of two previous samples of rain attenuation.…”