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Statistical Modeling of Multipath Fading Channels
Dec 25,2008 00:00
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admin
Statistical Modeling of Multipath Fading ChannelsWe first describe the statistical modeling of mobile wireless channels. We follow [396] closely. For a typical terrestrial wireless channel, we can assume the existence of multiple propagation paths between the transmitter and the receiver. With each transmission path we can associate a propagation delay and an attenuation factor, which are usually time-varying due to changes in propagation conditions resulting primarily from transceiver mobility. In the absence of additive noise, the received complex baseband signal in such a channel is given by
where x(t) is the transmitted baseband signal; an(t) and tn(t) are, respectively, the path attenuation and the propagation delay for the signal received on the nth path; and fc is the carrier frequency. By inspecting (9.1), we can see that we can model the multipath fading channel by a time-varying linear filter with impulse response g(t,t) given by
For some mobile channels, we can further assume that the received signal consists of a continuum of multipath components. Accordingly, for these channels, (9.1) is modified as follows:
where a(t,t) denotes the attenuation factor associated with a path delayed by t at time instant t. The corresponding baseband time-varying impulse response of the channel is then
By the central limit theorem, assuming a large enough number of paths between the transmitter and the receiver, and by further assuming that the associated attenuations per path are independent and identically distributed, the impulse response g(t, t) can be modeled by a complex-valued Gaussian random process. If the received signal r(t) has only a diffuse multipath component, g(t, t) is characterized by a zero-mean complex Gaussian random variable (i.e., |g(t,t)| has a Rayleigh distribution). In this case the channel is called a Rayleigh fading channel. Alternatively, if there are fixed scatterers or signal reflections in the medium, g(t, t) has a nonzero mean value and therefore |g(t, t)| has a Rician distribution. In this case the channel is a Rician fading channel. We will assume that the fading process g(t, t) is wide-sense stationary in t, and define its corresponding autocorrelation function as
A further reasonable assumption for most mobile communication channels is that the attenuation and phase shift associated with path delay t1 are uncorrelated with the corresponding attenuation and phase shift associated with a different path delay t2. This situation is known as uncorrelated scattering. Thus (9.5) can be expressed as
where Rg(t, Dt) represents the average
channel power as a function of the time delay t and the difference Dt in observation time. The multipath spread of the channel, Tm, is the range of values of the path delay
t for which Rg(t, 0) is essentially constant. Let Sg(f, Dt) = Ft{Rg(t, Dt)} [i.e., the Fourier transform of Rg(t, Dt) with respect to t]. Then Sg(f, Dt) is essentially the
frequency response function of the linear time-varying channel. The coherence bandwidth of the channel, Bc, is the range of values of frequency
f for which Sg(f, 0) is
essentially constant. Hence the multipath delay spread Tm and the coherence bandwidth Bc are related reciprocally (i.e., Bc We can also take the Fourier transform of Rg(t, Dt) with respect to Dt to obtain the scattering
function Sg(t, l)
= FDt{Rg(t, Dt)}. The Doppler spread
of the channel, Bd, is the range of
values of frequency l for which Sg(0, l) is
essentially constant. The channel coherence time
is given by Tc |