Signal Processing Techniques for Communication

The reference text discusses signal processing tools and techniques used for the design, testing, and deployment of communication systems. It further explores software simulation and modeling tools like MATLAB, GNU Octave, Mathematica, and Python for modeling, simulation, and detailed analysis leading to comprehensive insights into communication systems. The book explains topics such as source coding, pulse demodulation systems, and the principle of sampling and aliasing. This book: Discusses modern techniques including analog and digital filter design, and modulation principles including quadrature amplitude modulation, and differential phase shift keying. Covers filter design using MATLAB, system simulation using Simulink, signal processing toolbox, linear time-invariant systems, and non-linear time-variant systems. Explains important pulse keying techniques including Gaussian minimum shift keying and quadrature phase shift keying. Presents signal processing tools and techniques for communication systems design, modeling, simulation, and deployment. Illustrates topics such as software-defined radio (SDR) systems, spectrum sensing, and automated modulation sensing. The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science, and engineering. Cover Page Half-Title Page Title Page Copyright Page Dedication Page Contents Foreword Preface About the author Chapter 1 Introduction to Signals, Systems, and Processing 1.1 Signals and Systems 1.1.1 Elementary Signals-Unit Impulse, Unit Step, and Unit Ramp 1.1.2 Basic Operations on Independent Variable of Signals-Folding, Scaling, and Shifting 1.1.3 Operations on Dependent Variable-Level Shifting and Amplitude Scaling 1.2 Signal Processing 1.2.1 Analog Signal Processing 1.2.2 Digital Signal Processing 1.3 Systems 1.3.1 Classification of Signals 1.3.2 Continuous Time Systems 1.3.3 Discrete-Time Systems 1.3.4 Digital Systems 1.4 Linear Time-Invariant (LTI) Systems 1.5 Response of an LTI continuous time system 1.5.1 The Unit Impulse Response h(t) 1.5.2 Convolution Integral 1.6 Non-Linear Time Variant (NLTV) Systems 1.7 Recent Advancements in Signal Processing 1.8 Concluding Remarks Further Reading Exercises Chapter 2 System Simulation Tools 2.1 Introduction to MATLAB & Simulink 2.1.1 An Introduction to MATLAB 2.1.2 Basic Features of MATLAB 2.1.3 Getting MATLAB Help Online 2.1.4 Introduction to Simulink 2.2 Toolboxes and Blocksets in MATLAB 2.2.1 Communications Toolbox 2.2.2 Signal Processing Toolbox 2.2.3 DSP System Toolbox 2.2.4 Image Processing Toolbox 2.2.5 Control System Toolbox 2.2.6 Deep Learning Toolbox 2.2.7 Statistics and Machine Learning Toolbox 2.2.8 Computer Vision Toolbox 2.2.9 Parallel Computing Toolbox 2.3 Filter Design Using MATLAB 2.3.1 filterBuilder app 2.4 System Simulation Using Simulink 2.4.1 Simulink Building Blocks 2.4.2 Simulink Subsystems 2.4.3 Simulation Examples Using Simulink 2.5 MATLAB Online 2.6 Introduction to Mathematica 2.6.1 Wolfram Alpha 2.7 Introduction to GNU Octave 2.8 Introduction to Python 2.8.1 Python Library Softwares 2.8.2 Anaconda 2.8.3 Python Editors 2.9 Concluding Remarks Further Reading Exercises Chapter 3 Signal Processing in Frequency Domain 3.1 Introduction 3.2 Transforms Used in Frequency Domain Analysis 3.2.1 Laplace Transform and Inverse 3.2.2 Unilateral Laplace Transform (ULT) 3.2.3 Properties of Laplace Transform 3.2.4 Analysis of LTI Systems Using Laplace Transforms 3.2.5 Fourier Transform and Inverse 3.2.6 Discrete-Time Fourier Transform (DTFT) and Inverse 3.2.7 z-Transform and Inverse 3.2.8 Discrete Fourier Transform (DFT) 3.2.9 Hilbert Transform and Inverse 3.2.10 Discrete Cosine Transform (DCT) and Inverse 3.2.11 Karhunen-Loeve Transform (KLT) 3.2.12 Hough Transform 3.2.13 Hankel Transform and Inverse 3.2.14 Short Time Fourier Transform (STFT) and Inverse 3.2.15 Wavelet Transform and Inverse 3.3 Signal Theory 3.3.1 Output of an LTI System 3.3.2 Output of a NLTV System 3.4 Concluding Remarks Further Reading Exercises Chapter 4 Introduction to Communication Systems 4.1 Introduction 4.2 Overview of Communication Systems 4.2.1 Source Encoder 4.2.2 Channel Encoder 4.3 Analog Communication Systems 4.3.1 Need for Modulation & Demodulation 4.4 Amplitude Modulation (AM) 4.4.1 Mathematical Model of AM 4.4.2 Simulation of Amplitude Modulation 4.4.3 Variants of AM-DSBSC, SSB, and VSB 4.5 Angle Modulation 4.5.1 Frequency Modulation (FM) 4.5.2 Phase Modulation (PM) 4.6 Noise in Analog Modulation Systems 4.6.1 Noise in Baseband Systems 4.6.2 Noise in AM Receivers 4.6.3 Noise in Coherent DSBSC Receivers 4.6.4 Noise in SSB Receivers 4.6.5 Noise in FM Receivers 4.7 Introduction to Digital Communication Systems 4.8 Principle of Sampling and Aliasing 4.8.1 Mathematical Proof of Sampling Theorem 4.8.2 Aliasing 4.9 Source Coding and Channel Coding 4.10 Pulse Modulation Systems 4.10.1 Pulse Amplitude Modulation (PAM) 4.10.2 Pulse Width Modulation (PWM) 4.10.3 Pulse Position Modulation (PPM) 4.11 Pulse Code Modulation (PCM) 4.11.1 Sampling 4.11.2 Companding 4.11.3 Demodulation of Pulse Code Modulation 4.11.4 Differential Pulse Code Modulation (DPCM) 4.11.5 Adaptive Differential Pulse Code Modulation (ADPCM) 4.11.6 Delta Modulation (DM) 4.11.7 Adaptive Delta Modulation (ADM) 4.11.8 Sigma Delta Modulation (SDM) 4.11.9 Continuously Variable Slope Delta Modulation (CVSD) 4.12 Pulse Keying Techniques 4.12.1 Amplitude Shift Keying (ASK) 4.12.2 Frequency Shift Keying (FSK) 4.12.3 Phase Shift Keying (PSK) 4.12.4 Differential Phase Shift Keying (DPSK) 4.12.5 M-ary Phase Shift Keying (MPSK) 4.12.6 Quadrature Amplitude Modulation (QAM) 4.12.7 Minimum Shift Keying (MSK) 4.12.8 Simulation of Minimum Shift Keying Waveforms in MATLAB 4.12.9 Gaussian Minimum Shift Keying (GMSK) 4.12.10 Theoretical Bandwidth Efficiency Limits 4.12.11 Performance Comparison and Applications 4.12.12 The Competing Goals of Spectral Efficiency and Power Consumption 4.12.13 Square-Root-Raised-Cosine Filter (SRRC) 4.12.14 The Raised Cosine Filter (RCF) 4.13 Eye Diagrams 4.13.1 Probability of Bit Error 4.14 Concluding Remarks Further Reading Exercises Chapter 5 Design of Analog Filters Using MATLAB 5.1 Introduction 5.2 Channel Noise and Need for Filtering 5.3 Design and Simulation of Low Pass Filters (LPF) 5.3.1 Analog Filter Design and Simulation Using Signal Processing Toolbox 5.4 Design and Simulation of High Pass Filters (HPF) 5.5 Design and Simulation of Band-Pass Filters (BPF) 5.5.1 Filter Visualization Tool, fvtool(.) 5.5.2 Filter Designer App 5.6 Design and Simulation of Band Elimination (Band Reject) Filters (BRF) 5.7 Design and Simulation of Comb Filters 5.7.1 Analog Comb Filter 5.8 Concluding Remarks Further Reading Exercises Chapter 6 Design of Digital Filters 6.1 Introduction 6.2 Mitigation of Quantization Noise 6.2.1 Mitigation of Quantization Error 6.3 Digital FIR and IIR Filters 6.3.1 FIR Filters 6.3.2 IIR Filters 6.4 Design of Digital Low Pass Filter 6.4.1 Digital Filter Design and Simulation Using DSP System Toolbox 6.5 Design of Digital High Pass Filters (HPF) 6.6 Design of Digital Band Pass Filters (BPF) 6.6.1 The filterBuilder App 6.6.2 Design of Digital Filters Using the fdesign Object 6.7 Design and Simulation of Digital Comb Filters 6.8 Digital All-Pass Filters 6.9 Design of Moving Average Filter 6.10 Wavelet Denoising and Wiener Filtering 6.10.1 Wavelet Denoising 6.10.2 Wiener Filtering 6.11 Concluding Remarks Further Reading Exercises Chapter 7 Introduction to Modern Communication Systems 7.1 Introduction 7.1.1 Beamforming 7.1.2 Spatial Multiplexing 7.1.3 Space-Time Coding 7.2 Multiple Input Multiple Output (MIMO) Systems 7.2.1 Space-Time Coding (STC) 7.2.2 Space-Time Block Codes (STBC) 7.2.3 Space-Time Trellis Codes 7.3 Configurations of Wireless MIMO Systems 7.3.1 Orthogonal Space-Time Block Coding (OSTBC) 7.4 Tricks and Techniques in MIMO Systems 7.4.1 Configuration of 4×4 MIMO Systems 7.4.2 Going Beyond 4×4 MIMO 7.5 Signal Processing Challenges in MIMO Systems 7.6 MATLAB Communications Toolbox 7.7 Concluding Remarks Further Reading Exercises Chapter 8 Signal Processing in Wireless Communication 8.1 Introduction 8.2 Mobile Cellular Wireless Communication 8.2.1 Subsystems of Mobile Cellular Communication System 8.3 Introduction to Digital Cellular Mobile Communication 8.3.1 Generations of Cellular Mobile Technology 8.3.2 Global System for Mobile (GSM) 8.3.3 General Packet Radio Service (GPRS) 8.4 Mitigation of CCI and ACI 8.4.1 Co-Channel Interference 8.4.2 Adjacent Channel Interference 8.5 Inter-symbol Interference (ISI) 8.5.1 Mitigation of ISI 8.6 Channel Equalization 8.7 Non-Orthogonal Multiple Access (NOMA) 8.8 Concluding Remarks Further Reading Exercises Chapter 9 Advanced Communication Systems 9.1 Introduction 9.2 Multiple Access Schemes 9.2.1 Space Division Multiple Access (SDMA) 9.2.2 Time Division Multiple Access (TDMA) 9.2.3 Frequency Division Multiple Access (FDMA) 9.2.4 Spectral Efficiency 9.2.5 Code Division Multiple Access (CDMA) 9.3 Orthogonal Frequency Division Multiplexing (OFDM) 9.4 Random Multiple Access Schemes 9.4.1 Carrier Sense Multiple Access (CSMA) 9.5 Error Control Coding 9.5.1 Channel Coding 9.5.2 Linear Block Codes 9.5.3 Convolutional Codes 9.5.4 Trellis Coded Modulation (TCM) 9.5.5 Turbo Codes 9.6 Software Defined Radio Networks 9.6.1 Intelligent, Smart, and Reconfigurable Antenna 9.6.2 Programmable RF Module 9.6.3 DAC and ADC Modules 9.6.4 Digital Signal Processing 9.6.5 Interconnects in SDR 9.6.6 Software Subsystems in SDR 9.7 Cognitive Radio Networks (CRN) 9.8 New Trends in Satellite Communications 9.9 Wireless Sensor Networks (WSN) 9.9.1 Wireless Adhoc Network 9.10 Internet of Things (IoT) 9.10.1 LoRaWAN Technology 9.10.2 WiFi HaLow 9.10.3 Radio Frequency Identification (RFID) 9.10.4 Cyber-Physical System (CPS) 9.10.5 Big Data Analytics 9.11 Concluding Remarks Further Reading Exercises Chapter 10 Machine Learning for Communication Systems 10.1 Introduction 10.2 Machine Learning (ML) 10.2.1 Applications Supported by ML 10.3 Deep Learning (DL) 10.4 Machine Learning versus Deep Learning 10.5 Spectrum Sensing 10.5.1 Impacts of Spectrum Sensing in 5G/6G Communications 10.6 Automatic Modulation Recognition (AMR) 10.6.1 Technology Enablers for AMR 10.7 Concluding Remarks Further Reading Exercises Index