LEADER 04373nam 22005533 450 001 9911019779803321 005 20241223120546.0 010 $a9781394248131 010 $a139424813X 010 $a9781394248155 010 $a1394248156 010 $a9781394248148 010 $a1394248148 035 $a(MiAaPQ)EBC31854215 035 $a(Au-PeEL)EBL31854215 035 $a(CKB)37058851200041 035 $a(Perlego)4765900 035 $a(EXLCZ)9937058851200041 100 $a20241223d2025 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA Volterra Approach to Digital Predistortion $eSparse Identification and Estimation 205 $a1st ed. 210 1$aNewark :$cJohn Wiley & Sons, Incorporated,$d2025. 210 4$dİ2025. 215 $a1 online resource (274 pages) 311 08$a9781394248124 311 08$a1394248121 327 $aOverview of nonlinear effects in wireless communication systems -- Volterra series approach -- Discrete-time Volterra models -- Volterra models pruning based on circuit knowledge -- Regression of Volterra models -- Sparse machine learning -- Transmitter linearization with digital predistorters. 330 8 $aThorough discussion of the theory and application of the Volterra series for impairments compensation in RF circuits and systems A Volterra Approach to Digital Predistortion: Sparse Identification and Estimation offers a comprehensive treatment of the Volterra series approach as a practical tool for the behavioral modeling and linearization of nonlinear wireless communication systems. Although several perspectives can be considered when analyzing nonlinear effects, this book focuses on the Volterra series to study systems with real-valued continuous time RF signals as well as complex-valued discrete-time baseband signals in the digital signal processing field. A unified framework provides the reader with in-depth understanding of the available Volterra-based behavioral models; in particular, the book emphasizes those models derived by exploiting the knowledge of the physical phenomena that produce different types of nonlinear distortion. From these distinctive standpoints, this work remarkably contributes to theoretical issues of behavioral modeling. The book contributes to practical state-of-the-art questions on linearization, granting the reader practical guidance in designing digital predistortion schemes and adopting up-to-date machine learning methods to exploit the sparsity of the identification problem and reducing computational complexity. Later chapters include information on: * Identification of Volterra-based models as a linear regression problem, allowing the adoption of sparse machine learning methods to reduce computational complexity while keeping rich model structures * Deduction of Volterra models based on circuit model knowledge, offering pruned model structures that are better fitted for specific scenarios * Wireless communication systems and the nonlinear effects produced by power amplifiers, mixers, frequency converters or IQ modulators * Digital predistortion schemes and experimental results for both indirect and direct learning architectures A Volterra Approach to Digital Predistortion: Sparse Identification and Estimation is an essential reference on the subject for engineers and technicians who develop new products for the linearization of wireless transmitters, as well as researchers and students in fields and programs of study related to wireless communications. 606 $aWireless communication systems$xMathematical models 606 $aElectric networks, Nonlinear$xMathematical models 606 $aVolterra operators$3http://id.loc.gov/authorities/subjects/sh2002004441 615 0$aWireless communication systems$xMathematical models. 615 0$aElectric networks, Nonlinear$xMathematical models. 615 0$aVolterra operators 700 $aCrespo-Cadenas$b Carlos$01842709 701 $aMadero-Ayora$b Mari?a Jose?$01842710 701 $aBecerra$b Juan A$01842711 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019779803321 996 $aA Volterra Approach to Digital Predistortion$94422920 997 $aUNINA