  In this contribution, models of wireless channels are derived from the
maximum entropy principle, for several cases where only limited information
about the propagation environment is available. First, analytical models are
derived for the cases where certain parameters (channel energy, average energy,
spatial correlation matrix) are known deterministically. Frequently, these
parameters are unknown (typically because the received energy or the spatial
correlation varies with the user position), but still known to represent
meaningful system characteristics. In these cases, analytical channel models
are derived by assigning entropy-maximizing distributions to these parameters,
and marginalizing them out. For the MIMO case with spatial correlation, we show
that the distribution of the covariance matrices is conveniently handled
through its eigenvalues. The entropy-maximizing distribution of the covariance
matrix is shown to be a Wishart distribution. Furthermore, the corresponding
probability density function of the channel matrix is shown to be described
analytically by a function of the channel Frobenius norm. This technique can
provide channel models incorporating the effect of shadow fading and spatial
correlation between antennas without the need to assume explicit values for
these parameters. The results are compared in terms of mutual information to
the classical i.i.d. Gaussian model.
