  The problem of capacity achieving (optimal) input probability measures has
been widely investigated for several channel models with constrained inputs. So
far, no outstanding generalizations have been derived. This paper does a
forward step in this direction, by introducing a set of new requirements, for
the class of real scalar conditional output probability measures, under which
the optimal input probability measure is shown to be discrete with a finite
number of probability mass points, when peak power limited.
