Linear estimation and detection in Krylov subspaces
β Scribed by Guido K. E. Dietl
- Publisher
- Springer
- Year
- 2007
- Tongue
- English
- Leaves
- 248
- Series
- Foundations in Signal Processing, Communications and Networking No. 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
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 communications systems.
The investigations include exact computational complexity considerations and performance analysis based on extrinsic information transfer charts as well as Monte-Carlo simulations.
π SIMILAR VOLUMES
This book focuses linear estimation theory, which is essential for effective signal processing. The first section offers a comprehensive overview of key methods like reduced-rank signal processing and Krylov subspace methods of numerical mathematics. Also, the relationship between statistical signal
This book focuses linear estimation theory, which is essential for effective signal processing. The first section offers a comprehensive overview of key methods like reduced-rank signal processing and Krylov subspace methods of numerical mathematics. Also, the relationship between statistical signal
Content: <br>Chapter 1 Introduction (pages 1β18): <br>Chapter 2 Classical Detection and Estimation Theory (pages 19β165): <br>Chapter 3 Representations of Random Processes (pages 166β238): <br>Chapter 4 Detection of SignalsβEstimation of Signal Parameters (pages 239β422): <br>Chapter 5 Estimation of
<p><span>This book focuses on Krylov subspace methods for solving linear systems, which are known as one of the top 10 algorithms in the twentieth century, such as Fast Fourier Transform and Quick Sort (SIAM News, 2000). Theoretical aspects of Krylov subspace methods developed in the twentieth centu