Linear Estimation and Detection in Krylov Subspaces [electronic resource] /
Dietl, Guido K. E.author.
This book focuses on the foundations of linear estimation theory which is essential for effective signal processing. In its first part, it gives a comprehensive overview of several key methods like reduced-rank signal processing and Krylov subspace methods of numerical mathematics. Based on the derivation of the multistage Wiener filter in its most general form, the relationship between statistical signal processing and numerical mathematics is presented. In the second part, the theory is applied to iterative multiuser detection receivers (Turbo equalization) which are typically desired in wireless communication systems. The investigations include -exact computational complexity considerations and - performance analysis based on extrinsic information transfer charts as well as Monte-Carlo simulations.
Description based upon print version of record.
Includes bibliographical references and index.
Theory: Linear Estimation in Krylov Subspaces -- Efficient Matrix Wiener Filter Implementations -- Block Krylov Methods -- Reduced-Rank Matrix Wiener Filters in Krylov Subspaces -- Application: Iterative Multiuser Detection -- System Model for Iterative Multiuser Detection -- System Performance -- Conclusions.
This book focuses on the foundations of linear estimation theory which is essential for effective signal processing. In its first part, it gives a comprehensive overview of several key methods like reduced-rank signal processing and Krylov subspace methods of numerical mathematics. Based on the derivation of the multistage Wiener filter in its most general form, the relationship between statistical signal processing and numerical mathematics is presented. In the second part, the theory is applied to iterative multiuser detection receivers (Turbo equalization) which are typically desired in wireless communication systems. The investigations include -exact computational complexity considerations and - performance analysis based on extrinsic information transfer charts as well as Monte-Carlo simulations.
Electronics.
Microelectronics.
Signal processing.
Image processing.
Speech processing systems.
Computer mathematics.
Statistics .
Computer science—Mathematics.
Applied mathematics.
Engineering mathematics.
Electronics and Microelectronics, Instrumentation.
Signal, Image and Speech Processing.
Computational Science and Engineering.
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Math Applications in Computer Science.
Mathematical and Computational Engineering.
Foundations in Signal Processing, Communications and Networking