MMSE-based algorithm for joint signal detection, channel and noise variance estimation for OFDM systems / Vincent Savaux, Yves Louët.

Format:

Book

Author/Creator:

Savaux, Vincent, author.

Louët, Yves, author.

Series:

Focus series (London, England)

Focus Series, 2051-249X

Language:

English

Subjects (All):

Orthogonal frequency division multiplexing.

Signal processing--Digital techniques.

Signal processing.

Wireless communication systems.

Physical Description:

1 online resource (138 p.)

Edition:

1st ed.

Place of Publication:

London, England ; Hoboken, New Jersey : ISTE : Wiley, 2014.

Language Note:

English

Summary:

This book presents an algorithm for the detection of an orthogonal frequency division multiplexing (OFDM) signal in a cognitive radio context by means of a joint and iterative channel and noise estimation technique. Based on the minimum mean square criterion, it performs an accurate detection of a user in a frequency band, by achieving a quasi-optimal channel and noise variance estimation if the signal is present, and by estimating the noise level in the band if the signal is absent. Organized into three chapters, the first chapter provides the background against which the system model is pr

Contents:

Cover Page; Half-Title Page; Title page; Copyright page; Contents; Introduction; 1: Background and System Model; 1.1. Channel model; 1.1.1. The multipath channel; 1.1.2. Statistics of the channel; 1.1.2.1. Rayleigh channel; 1.1.2.2. WSSUS model; 1.2. Transmission of an OFDM signal; 1.2.1. Continuous representation; 1.2.2. Discrete representation; 1.2.3. Discrete representation under synchronization mismatch; 1.3. Pilot symbol aided channel and noise estimation; 1.3.1. The pilot arrangements; 1.3.2. Channel estimation; 1.3.2.1. LS estimation; 1.3.2.2. LMMSE estimation

2.1.3.3.1. Polynomial expression of the problem to solve 2.1.3.3.2. Sign of the polynomial considering R_H; 2.1.3.3.3. Sign of the polynomial considering R ̆_H; 2.1.3.4. Characterization of the channel and noise estimations; 2.1.4. Simulation results: ideal approach; 2.1.4.1. Convergence of the noise variance estimation; 2.1.4.2. Speed of convergence of the algorithm; 2.1.4.3. Bias of the noise variance estimation; 2.1.4.4. Comparison of SNR estimation with other methods; 2.1.4.5. Channel estimation; 2.2. Algorithm in a practical approach; 2.2.1. Proposed algorithm: realistic approach

3.3.1. Probability density function of M under H1

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781119007906

1119007909

9781119005087

1119005086

9781119007890

1119007895

OCLC:

892243939