, for example, in planar near-field antenna measurement systems, wherein a Nyquist distance n 2/2 is usually adopted under the hypothesis that the reactive energy of the plane-wave spectrum is negligible [71]. The electrical dimension is the dimension measured in wavelengths. M. D. Migliore Such a simple "a priori information" on the radiated field gives a number of sampling positions much larger than the NDF of the field. This is a clear indication that the sampling representation is redundant. While in antenna measurements the oversampling of the representation is acceptable (and in some cases advantageous since the goal is the interpolation of the data, and oversampling reduces the effect of noise in measured data), in spatial communication it gives correlation between the samples. Loosely speaking, it is not possible to associate independent information to each spatial sample. Consequently, we are interested in a sampling representation of the field at an E level of approximation with a number of samples equal to the NDFé of the field. In this case it is possible to associate independent information to each spatial sample, obtaining the maximum number of parallel spatial subchannels. However, this is generally not possible, and bandlimited approximations of the field require a number of samples larger than NDFé at an E level of approximation. In fact, the optimal basis, given in Eq. 2.4, generally requires quite complicated functions that depend on the geometry of both the radiating and the receiving domain, while bandlimited approximation uses basis functions that do not depend on the geometry of the problem. Clearly, since we have no flexibility in the choice of basis functions, the only way to improve the bandlimited representation is to include source and observation domains in the sampling step. This solution has been proposed in a series of elegant papers by Bucci, Franceschetti, and co-workers [20-22]. The papers are very accurate and complete from a mathematical point of view, and the interested reade