  This work addresses channel estimation in multiple antenna multicell
interference-limited networks. Channel state information (CSI) acquisition is
vital for interference mitigation. Wireless networks often suffer from
multicell interference, which can be mitigated by deploying beamforming to
spatially direct the transmissions. The accuracy of the estimated CSI plays an
important role in designing accurate beamformers that can control the amount of
interference created from simultaneous spatial transmissions to mobile users.
Therefore, a new technique based on the structure of the spatial covariance
matrix and the discrete cosine transform (DCT) is proposed to enhance channel
estimation in the presence of interference. Bayesian estimation and Least
Squares estimation frameworks are introduced by utilizing the DCT to separate
the overlapping spatial paths that create the interference. The spatial domain
is thus exploited to mitigate the contamination which is able to discriminate
across interfering users. Gains over conventional channel estimation techniques
are presented in our simulations which are also valid for a small number of
antennas.
