Reduced Rank Channel Estimation
IEEE Vehicular Technology Conference (VTC'99)
Houston, TX, May 16-20, 1999, pp 1126-1130. © 1999 IEEE
A space-time wireless communication channel can be decomposed as a
set of filters, each consisting of a scalar temporal
filter followed by a single spatial signature vector. If only a
small number of such filters is necessary to accurately describe
the space time channel, we call it a reduced rank channel.
We here consider different methods of exploiting this property to
improve channel estimation and subsequent space-time
Three methods have been studied, a maximum likelihood reduced rank
channel estimation method
and two different signal subspace
projection methods which projects
either the channel estimate
or the received data samples onto an estimate of
the signal subspace, the latter
being the new method proposed here.
Simulations indicate that even though
the maximum likelihood reduced rank method has the
smallest channel estimation errors, the BER of the detector based
on this model exceeds the BER of the detectors based on the channel
models obtained using the two signal subspace projection
methods. The best performance is obtained using the proposed method,
which also has the lowest complexity.
PhD Thesis by Erik Lindskog.
PIMRC'98 Conference paper
on "Reduced Rank Equalization".
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