  The entropy computation of Gaussian mixture distributions with a large number
of components has a prohibitive computational complexity. In this paper, we
propose a novel approach exploiting the sphere decoding concept to bound and
approximate such entropy terms with reduced complexity and good accuracy.
Moreover, we propose an SNR region based enhancement of the approximation
method to reduce the complexity even further. Using Monte-Carlo simulations,
the proposed methods are numerically demonstrated for the computation of the
mutual information including the entropy term of various channels with finite
constellation modulations such as binary and quadratic amplitude modulation
(QAM) inputs for communication applications.
