  This work proposes a blind adaptive reduced-rank scheme and constrained
constant-modulus (CCM) adaptive algorithms for interference suppression in
wireless communications systems. The proposed scheme and algorithms are based
on a two-stage processing framework that consists of a transformation matrix
that performs dimensionality reduction followed by a reduced-rank estimator.
The complex structure of the transformation matrix of existing methods
motivates the development of a blind adaptive reduced-rank constrained (BARC)
scheme along with a low-complexity reduced-rank decomposition. The proposed
BARC scheme and a reduced-rank decomposition based on the concept of joint
interpolation, switched decimation and reduced-rank estimation subject to a set
of constraints are then detailed. The proposed set of constraints ensures that
the multi-path components of the channel are combined prior to dimensionality
reduction. In order to cost-effectively design the BARC scheme, we develop
low-complexity decimation techniques, stochastic gradient and recursive least
squares reduced-rank estimation algorithms. A model-order selection algorithm
for adjusting the length of the estimators is devised along with techniques for
determining the required number of switching branches to attain a predefined
performance. An analysis of the convergence properties and issues of the
proposed optimization and algorithms is carried out, and the key features of
the optimization problem are discussed. We consider the application of the
proposed algorithms to interference suppression in DS-CDMA systems. The results
show that the proposed algorithms outperform the best known reduced-rank
schemes, while requiring lower complexity.
