  Cognitive radios have been proposed as agile technologies to boost the
spectrum utilization. This paper tackles the problem of channel estimation and
its impact on downlink transmissions in an underlay cognitive radio scenario.
We consider primary and cognitive base stations, each equipped with multiple
antennas and serving multiple users. Primary networks often suffer from the
cognitive interference, which can be mitigated by deploying beamforming at the
cognitive systems to spatially direct the transmissions away from the primary
receivers. The accuracy of the estimated channel state information (CSI) plays
an important role in designing accurate beamformers that can regulate the
amount of interference. However, channel estimate is affected by interference.
Therefore, we propose different channel estimation and pilot allocation
techniques to deal with the channel estimation at the cognitive systems, and to
reduce the impact of contamination at the primary and cognitive systems. In an
effort to tackle the contamination problem in primary and cognitive systems, we
exploit the information embedded in the covariance matrices to successfully
separate the channel estimate from other users' channels in correlated
cognitive single input multiple input (SIMO) channels. A minimum mean square
error (MMSE) framework is proposed by utilizing the second order statistics to
separate the overlapping spatial paths that create the interference. We
validate our algorithms by simulation and compare them to the state of the art
techniques.
