be gen- normal distribution, and the Rayleigh one is used to erated, the procedure should be as follows: model the short-term fading, the overall signal distribu- tion can be modelled by the Suzuki distribution [Pars92]. 1. generate n + 1 matrices, go, g1, gn, every element of It is usually well accepted that long- and short-term which is a Gaussian random variable with zero mean effects can be modelled separately. In [Ande02], it is and standard deviation o; shown that the long-term fading in general may not be 2. given a correlation coefficient of shadowing from dif- due to shadowing, but rather to the slow variation of the ferent BSs equal to p, produce n shadowing maps, coupling between scatterers, when the MT is moving. according to This means that the long-term fading is just as un- predictable as the short-term one, since it originates from the same scatterers. Shadowing will still exist behind i = 1,2, n (4.7.9) major changes in the environment. A new model using multiple scattering, replacing the traditional subdivision of the total fading into a slow 3. compute the two-dimensional inverse Fourier trans- log-normal and a fast Rayleigh component is proposed in form to obtain h(x, y); [Ande02]. The physical basis is a model of forward 4. use two-dimensional convolution to filter each shad- scattering between scatterers, introducing multiple owing map, G, to obtain the desired filtered maps. scattered waves defining a new transfer function, H. This function consists of a sum of a small number of terms, This scheme for simulating shadowing provides the basis where each term is a multiple product of complex for carrying out more realistic system level simulations, Gaussian distributions since it provides a link between MT location and shadowing, which is coherent with the environment H = K + H1 + aH2H3 - BH4H5H6 + (4.7.10) description. where Hi are complex, independent Gaussian variables 4.7.5.5 Narrowband short-term fading (Rayleigh in magnitude), and K is a constant related to the Rice factor. Parameters K,