On the Capacity of the Peak Power Constrained Vector Gaussian Channel: An Estimation Theoretic Perspective

This paper studies the capacity of an n -dimensional vector Gaussian noise channel subject to the constraint that an input must lie in the ball of radius R centered at the origin. It is known that in this setting, the optimizing input distribution is supported on a finite number of concentric spheres. However, the number, the positions, and the probabilities of the spheres are generally unknown. This paper characterizes necessary and sufficient conditions on the constraint R, such that the input distribution supported on a single sphere is optimal. The maximum Rn, such that using only a single sphere is optimal, is shown to be a solution of an integral equation. Moreover, it is shown that Rn scales as √ n and the exact limit of Rn√n is found.