  Ricean channel model is widely used in wireless communications to
characterize the channels with a line-of-sight path. The Ricean K factor,
defined as the ratio of direct path and scattered paths, provides a good
indication of the link quality. Most existing works estimate K factor based on
either maximum-likelihood criterion or higher-order moments, and the existing
works are targeted at K-factor estimation at receiver side. In this work, a
novel approach is proposed. Cast as a classification problem, the estimation of
K factor by neural network provides high accuracy. Moreover, the proposed
K-factor estimation is done at transmitter side for transmit processing, thus
saving the limited feedback bandwidth.
