Since it is based on a well-studied distribution with with special properties, the Rayleigh distribution lends itself to analysis, and the key features that affect the performance of a wireless network have analytic expressions. Note that the parameters discussed here are for a non-static channel. If a channel is not changing with time, clearly it does not fade and instead remains at some particular level. Separate instances of the channel in this case will be uncorrelated with one another owing to the assumption that each of the scattered components fades independently. Once relative motion is introduced between any of the transmitter, receiver and scatterers, the fading becomes correlated and varying in time.
The normalized Doppler power spectrum of Rayleigh fading with a maximum Doppler shift of 10Hz.
The Doppler power spectral density of a fading channel describes how much spectral broadening it causes. This shows how a pure frequency e.g. a pure sinusoid, which is an impulse in the frequency domain is spread out across frequency when it passes through the channel. It is the Fourier transform of the time-autocorrelation function. For Rayleigh fading with a vertical receive antenna with equal sensitivity in all directions, this has been shown to be where is the frequency shift relative to the carrier frequency. Clearly, this equation is only valid for values of between ; the spectrum is zero outside this range. This spectrum is shown in the figure for a maximum Doppler shift of 10Hz. The ''''bowl shape'''' or ''''bathtub shape'''' is the classic form of this doppler spectrum.
Jakes模型 In his book,[6] Jakes popularised a model for Rayleigh fading based on summing sinusoids. Let the scatterers be uniformly distributed around a circle at angles α<sub>n</sub> with k rays emerging from each scatterer. The Doppler shift on ray n is <dl> <dd></dd> </dl> and, with M such scatterers, the Rayleigh fading of the kth waveform over time t can be modelled as: <dl> <dd>.</dd> </dl> Here, and the and are model parameters with usually set to zero, chosen so that there is no cross-correlation between the real and imaginary parts of R(t): <dl> <dd></dd> </dl> and used to generate multiple waveforms. If a single-path channel is being modelled, so that there is only one waveform then can be zero. If a multipath, frequency-selective channel is being modelled so that multiple waveforms are needed, Jakes suggests that uncorrelated waveforms are given by: <dl> <dd>.</dd> </dl> In fact, it has been shown that the waveforms are correlated among themselves — they have non-zero cross-correlation — except in special circumstances.[7] The model is also deterministic (it has no random element to it once the parameters are chosen). A modified Jakes&#39;&#39;&#39;&#39; model[8] chooses slightly different spacings for the scatterers and scales their waveforms using Walsh-Hadamard sequences to ensure zero cross-correlation. Setting <dl> <dd> and ,</dd> </dl> results in the following model, usually termed the Dent model or the modified Jakes model: <dl> <dd>.</dd> </dl> The weighting functions A<sub>n</sub>(k) are the kth Walsh-Hadamard sequence in n. Since these have zero cross-correlation by design, this model results in uncorrelated wavforms. The phases can be initialised randomly and have no effect on the correlation properties. The Jakes&#39;&#39;&#39;&#39; model also popularised the Doppler spectrum associated with Rayleigh fading, and, as a result, this Doppler spectrum is often termed Jakes&#39;&#39;&#39;&#39; spectrum.
Filtered white noise Another way to generate a signal with the required Doppler power spectrum is to pass a white Gaussian noise signal through a filter with a frequency response equal to the square-root of the Doppler spectrum required. Although simpler than the models above, and non-deterministic, it presents some implementation questions related to needing high-order filters to approximate the irrational square-root function in the response and sampling the Guassian waveform at an appropriate rate.