eng URI http://hdl.handle.net/10795/3086 ασύρματη τηλεπικοινωνία In this chapter system identification algorithms for sparse nonlinear multi input multi output (MIMO) systems are developed. These algorithms are potentially useful in a variety of application areas including digital transmission systems incorporating power amplifier(s) along with multiple antennas, cognitive processing, adaptive control of nonlinear multivariable systems, and multivariable biological systems. Sparsity is a key constraint imposed on the model. The presence of sparsity is often dictated by physical considerations as in wireless fading channel-estimation. In other cases it appears as a pragmatic modelling approach that seeks to cope with the curse of dimensionality, particularly acute in nonlinear systems like Volterra type series. Three dentification approaches are discussed: conventional identification based on both input and output samples, semi–blind identification placing emphasis on minimal input resources and blind identification whereby only output samples are available plus a–priori information on input characteristics. Based on this taxonomy a variety of algorithms, existing and new, are studied and evaluated by simulations 51 pp. LOMv1.0 creator LOMv1.0 Project Deliverable Number D2.3.1 LOMv1.0 Project Final Beneficiary LOMv1.0 Project Executing Organisation 2013-04-30 Digital Library of the Operational Programme "Education and Lifelong Learning" abstract types Text Algorithms Nonlinear multi input multi output (MIMO) 569699 application/pdf http://repository.edulll.gr/edulll/bitstream/10795/3086/2/3086_D2.3.1.pdf URI http://hdl.handle.net/10795/3086 LOMv1.0creator 2016-05-10T10:09:12Z LOMv1.0validator 2016-05-10T10:09:12Z LOMv1.0 gre no no Copyright EYD-EPEDBM (Operational Programme "Education and Lifelong Learning")