Multi-antenna communication systems are attracting research interest as a means to increase the information capacity, reliability, and spectral efficiency of wireless information transfer. Ray-tracing methods predict the behavior of wireless channels using a model of the propagation environment and are a low-cost alternative to direct measurements. We use ray tracing simulations to validate the statistical time and angle of arrival characteristics of an indoor multipath channel and compare model parameter estimates with estimates derived from channel sounding measurements. Ray tracing predicts the time and angle clustering of multipaths observed in the measurements and provides model parameter estimates which are closely correlated with measured estimates. The ray tracing parameters relating to power characteristics show more deviation from measurements than the time and angle related parameters. Our results also indicate that the description of reflective scatterers in the propagation environment is more important to the quality of the predicted statistical behavior than the description of bulk materials. We use a ray synthesis model to investigate means of efficiently representing the channel for feedback information to the transmitter as a means to increase the information capacity. Several methods of selecting the ray-model feedback information are demonstrated with results from simulated and measured channels. These results indicate that an ESPRIT algorithm coupled with ad hoc transmit/receive pairing can yield better than 90% of the ideal waterfilling capacity when adequate training-based channel estimates are available. Additionally, we investigate a covariance feedback method for providing channel feedback for increased capacity. Both the ray-based and covariance-based feedback methods yield their highest capacity improvements when the signal to noise ratio is low. This results because of the larger benefit of focusing transmit power into the most advantageous eigenmodes of the channel when fewer eigenmodes have power allocated to them by the waterfilling capacity solution. In higher signal to noise ratio cases, more eigenmodes of the channel receive power when waterfilling, and the capacity improvement from feedback information decreases relative to a uniform power allocation. In general, ray model feedback methods are preferable because the covariance feedback quickly requires higher computational effort as the array sizes increase and typically results in lower capacity for a given amount of feedback information.