An Introduction to Digital Signal Processing

An Introduction to Digital Signal Processing aims at undergraduate students who have basic knowledge in C programming, Circuit Theory, Systems and Simulations, and Spectral Analysis. The book is focused on basic concepts of digital signal processing, MATLAB simulation and implementation on selected DSP hardware in which the candidate is introduced to the basic concepts first before embarking to the practical part which comes in the later chapters. Initially Digital Signal Processing evolved as a postgraduate course which slowly filtered into the undergraduate curriculum as a simplified version of the latter. The goal was to study DSP concepts and to provide a foundation for further research where new and more efficient concepts and algorithms can be developed. Though this was very useful it did not arm the student with all the necessary tools that many industries using DSP technology would require to develop applications. This book is an attempt to bridge the gap. It is focused on basic concepts of digital signal processing, MATLAB simulation and implementation on selected DSP hardware. The objective is to win the student to use a variety of development tools to develop applications. Contents• Introduction to Digital Signal processing.• The transform domain analysis: the Discrete-Time Fourier Transform• The transform domain analysis: the Discrete Fourier Transform• The transform domain analysis: the z-transform• Review of Analogue Filter• Digital filter design.• Digital Signal Processing Implementation Issues• Digital Signal Processing Hardware and Software• Examples of DSK Filter Implementatio

An Introduction to Digital Signal Processing

An Introduction to Digital Signal Processing aims at undergraduate students who have basic knowledge in C programming, Circuit Theory, Systems and Simulations, and Spectral Analysis. The book is focused on basic concepts of digital signal processing, MATLAB simulation and implementation on selected DSP hardware in which the candidate is introduced to the basic concepts first before embarking to the practical part which comes in the later chapters. Initially Digital Signal Processing evolved as a postgraduate course which slowly filtered into the undergraduate curriculum as a simplified version of the latter. The goal was to study DSP concepts and to provide a foundation for further research where new and more efficient concepts and algorithms can be developed. Though this was very useful it did not arm the student with all the necessary tools that many industries using DSP technology would require to develop applications. This book is an attempt to bridge the gap. It is focused on basic concepts of digital signal processing, MATLAB simulation and implementation on selected DSP hardware. The objective is to win the student to use a variety of development tools to develop applications. Contents• Introduction to Digital Signal processing.• The transform domain analysis: the Discrete-Time Fourier Transform• The transform domain analysis: the Discrete Fourier Transform• The transform domain analysis: the z-transform• Review of Analogue Filter• Digital filter design.• Digital Signal Processing Implementation Issues• Digital Signal Processing Hardware and Software• Examples of DSK Filter Implementatio

Soft-input iterative channel estimation for bit-interleaved turbo-coded MIMO-OFDM systems

Bit-interleaved coded modulation (BICM) is a robust multiplexing technique for achieving multiplexing gain in multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. However, in order to benefit maximally from the various advantages offered by BICM-based MIMO-OFDM systems, availability of accurate MIMO channel state information (CSI) at the receiver end of the system is essential. Without accurate MIMO CSI, accurate MIMO demapping and coherent detection and decoding of the transmitted message symbols at the system’s receiver would be impossible. In such cases, the multiplexing gain offered by the BICM technique, as well as the higher data rate made possible by the MIMO-OFDM system, is not benefitted from in full. In this paper, we propose a soft input based decision-directed channel estimation scheme for the provision of MIMO CSI for coherent detection of signals in MIMO-OFDM systems. The proposed channel estimator works in iterative mode with a MIMO demapper and a turbo decoder, and is based on the fast data projection method (FDPM) and the variable step size normalised least mean square (VSSNLMS) algorithm. Simulation results of the proposed estimator based on the FDPM and VSSNLMS algorithms indicate better performance in comparison with the same estimator employing minimum mean square error criteria and deflated projection approximation subspace tracking algorithms for both slow- and fast-fading channel scenarios. The proposed estimator would be suitable for use at the receiver end of MIMO-OFDM wireless communication systems operating in either slow- or fast-fading channels

Soft-input iterative channel estimation for bit-interleaved turbo-coded MIMO-OFDM systems

Bit-interleaved coded modulation (BICM) is a robust multiplexing technique for achieving multiplexing gain in multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. However, in order to benefit maximally from the various advantages offered by BICM-based MIMO-OFDM systems, availability of accurate MIMO channel state information (CSI) at the receiver end of the system is essential. Without accurate MIMO CSI, accurate MIMO demapping and coherent detection and decoding of the transmitted message symbols at the system’s receiver would be impossible. In such cases, the multiplexing gain offered by the BICM technique, as well as the higher data rate made possible by the MIMO-OFDM system, is not benefitted from in full. In this paper, we propose a soft input based decision-directed channel estimation scheme for the provision of MIMO CSI for coherent detection of signals in MIMO-OFDM systems. The proposed channel estimator works in iterative mode with a MIMO demapper and a turbo decoder, and is based on the fast data projection method (FDPM) and the variable step size normalised least mean square (VSSNLMS) algorithm. Simulation results of the proposed estimator based on the FDPM and VSSNLMS algorithms indicate better performance in comparison with the same estimator employing minimum mean square error criteria and deflated projection approximation subspace tracking algorithms for both slow- and fast-fading channel scenarios. The proposed estimator would be suitable for use at the receiver end of MIMO-OFDM wireless communication systems operating in either slow- or fast-fading channels