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The authors consider a two-stage framework for linear interference mitigation, in which a transformation performs dimensionality reduction followed by a reduced-rank filter. A generic reduced-rank scheme that jointly optimizes the transformation and the reduced-rank filter by using the Minimum Mean Squared Error (MMSE) criterion is investigated. Then, they impose constraints on the design of the transformation and propose the Switched Approximations of Adaptive Basis Functions (SAABF) scheme, in which the transformation is chosen instantaneously from a set of mapping matrices and adaptive basis functions.
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