Optimization of adaptive signal processing algorithms for
wireless communications is based on a model of the underlying
propagation channel. In practice, this model is never known
perfectly. For example, its parameters have to be estimated and are
only known with significant errors.
In this book, a systematic treatment of this practical design
problem is provided for signal processing in the physical layer
with multiple antennas. The design of robust signal processing
algorithms is based on a description of the errors and the
uncertainties in the system's model. It applies principles of
modern estimation, optimization, and information theory. Tutorial
introductions to relevant literature and mathematical foundations
give the necessary background and context to the reader.
The book provides detailed derivations and enlightening insights
into the related technical problems covering the following topics
in detail: An overview of the principles of training-based
multiple-input multiple-output (MIMO) channel estimation. Robust
minimax estimation of the wireless communication channel. Robust
minimax prediction of the wireless communication channel based on
the maximum Doppler frequency. Identification of channel and noise
correlations (power delay profile, spatial and temporal
correlations, spatial correlations of interference). Interpolation
of band-limited autocovariance sequences. Robust linear and
nonlinear precoding for the multi-user downlink with multiple
antennas which is based on incomplete channel state information or
channel correlations (performance measures, duality, robust
Tomlinson-Harashima precoding, robust vector precoding, nonlinear
beamforming).
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