Digital Communication (English)

   Monday, December 18, 2023   

Starting a new Lecture Notes Series on Digital Communication

Youtube Lecture Playlist Credits 

Channel Name: Dopamine

So Let Us Start to This Journey of Learning 

Digital Communication By Lecture Notes together!

Lecture 1: L 1 | Introduction Digital Communication

Lecture 2: L 2 | Line Encoding Techniques

Lecture 3: L 3 | Properties of Line Encoding

Lecture 4: L 4 | Scrambling

Lecture 5: L 5 | Digital Receiver & Eye Diagram

Lecture 6: L 6 | Modulation Techniques | ASK | PSK | FSK | QAM | M- Ary Modulation

Lecture 7: L 7 | ASK | Amplitude Shift Key

Lecture 8: L 8 | FSK Frequency Shift Key

Lecture 9: L 9 | BPSK | Binary Phase Shift Key

Lecture 10: L 10 | QPSK | Quadrature Phase Shift Key

Lecture 11: L 11 | DPSK | Differential Phase Shift Key

Lecture 12: L 12 | MSK | Minimum Shift Key

Lecture 13: L 13 | M- Ary PSK | 8-Ary PSK

Lecture 14: L 14 | Part 1 | PSD of Unipolar Line Encoding | Power Spectral Density of Unipolar Line Encoding

Lecture 15: L 15 | Part 2 | PSD of Unipolar Line Encoding | Power Spectral Density of Unipolar Line Encoding

Lecture 16: L 16 | PSD of Polar Line Encoding | Power Spectral Density of Polar Line Encoding

Lecture 17: L 17 | PSD of Bipolar Line Encoding | Power Spectral Density of Bipolar Line Encoding

Lecture 18: L 18 | PSD of Manchester Line Encoding

Lecture 19: L 1 | Part 1 | Introduction to Information | Information Theory & Coding

Lecture 20: L 1 | Part 2 | Introduction to Information | Information Theory & Coding

Lecture 21: L 2 | Entropy | Information Theory & Coding

Lecture 22: L 3 | Shannon Coding | Information Theory & Coding

Lecture 23: L 4 | Huffman Coding | Information Theory & Coding

Lecture 24: L 5 | Discrete Memoryless Channel | Information Theory & Coding

Lecture 25: L 6 | Shannon Heartley Channel Capacity Theorem | Information Theory & Coding

Lecture 26: L 7 | Error Control Coding | Introduction | Information Theory & Coding

Lecture 27: L 8 | Part II | Error Control Coding | Information Theory & Coding

Lecture 28: L 9 | Linear Block Code | Information Theory & Coding

Lecture 29: L 10 | Linear Block Code Question | Information Theory & Coding

Lecture 30: Linear Block Code Part-1

Lecture 31: Linear Block Code Part-2

Lecture 32: L 11 | Hamming Code Intro | Information Theory & Coding

Lecture 33: L 12 | Cyclic Code - Introduction | Information Theory

Lecture 34: L 13 | Cyclic Code Generation | Polynomial Method | Systematic & Non Systematic Method

Lecture 35: L 14 | Cyclic Code Generator Matrix Method | Information Theory & Coding

Lecture 36: L 15 | Cyclic Code Decoding & Encoding | Information Theory & Coding

Lecture 37: L 16 | Convolution Code- Intro | Information Theory & Coding

Lecture 38: L 17 | Code Tree | Convolutional Code | Information Theory & Coding

Lecture 39: L 19 | State Diagram | Convolution Code | Information Theory & Coding

Lecture 40: L 20 | Trellis Diagram | Convolution Code | Information Theory & Coding

Lecture 41: L 20 | Trellis Diagram | Convolution Code | Information Theory & Coding

Lecture 42: L 21 | Sequential Decoding | Convolution Code | Information Theory & Coding

Lecture 43: L 22 | Turbo Code Introduction | Information Theory & Coding

Lecture 44: L 1 | Probability Intro I | Digital Communication | Probability & Statistics

Lecture 45: L 2 | Conditional Probability | Probability Intro II | Probability & Statistics

Lecture 46: L 3 | Baye's Theorem | Probability & Statistics

Lecture 47: Random Variable With Animation | Complete Analysis With Examples

Lecture 48: L 11 | PMF, CDF, PDF | Probability & Statistics

Lecture 49: L 12 | PMF, CDF, PDF | Probability & Statistics

Lecture 50: L 13 | PMF, CDF, PDF | Probability & Statistics

Lecture 51: L 14 | Mean | Variance | Standard Deviation | Mathematical Expectation

Lecture 52: L 15 | Mean | Variance | Standard Deviation | Mathematical Expectation

Lecture 53: L 16 | Mean | Variance | Standard Deviation | Mathematical Expectation

Lecture 54: L 17 | Degenerate | Bernoulli | Binomial | Poisson | Geometric

Lecture 55: L 18 | Uniform | Exponential | Gamma | Weibull Distribution

Lecture 56: L 19 | Gaussian Distribution | CDF of Gaussian Distribution

Lecture 57: L 20 | Mean & Variance of Gaussian Distribution

Lecture 58: L 21 | 2 D Random Variable | Probability & Statistics

Lecture 59: L 22 | 2D Random Variable Questions

Lecture 60: L 23 | 2D Random Variable Questions

Lecture 61: 2 Dimensional Random Variable

Lecture 62: L 24 | Marginal & Conditional Distribution

Lecture 63: L 25 | Marginal & Conditional Distribution Questions

Lecture 64: L 26 | Mathematical Expectations | 2 D Random Variable

Lecture 65: L 27 | Cauchy Schwarz Inequality

Lecture 66: L 28 | Error Function | Complimentary Error Function

Lecture 67: Lec 30 | Correlation | Probability & Statistics

Lecture 68: Lec 31 | Correlation Question

Lecture 69: Lec 32 | Central Limit Theorem

Lecture 70: L 29 | Chebyshev Inequality

Lecture 71: L 33 | Central Limit Theorem Questions

Lecture 72: L 34 | Random Process

Lecture 73: L 35 | Classification of Random Process

Lecture 74: Mean and Variance Practice Questions | Numericals on Random Variable

Lecture 75: L 36 | Ergodic Process

Lecture 76: L 37 | Random Process Practice Question

Lecture 77: L 38 | Random Process Practice Questions 2

Lecture 78: L 39 | PSD | Power Spectral Density

Lecture 79: L 40 | PSD Practice Questions

Lecture 80: L 41 | Gaussian process

Lecture 81: L 42 | Multiple random process

Lecture 82: Lec 1 | Spread Spectrum Technique

Lecture 83: Lec 2 | Properties of Pseudo Random Code | Spread Spectrum

Lecture 84: Lec 3 | Direct Sequence Spread Spectrum

Lecture 85: Lec 4 | Frequency Hopping Spread Spectrum

Lecture 86: Lec 5 | Multiple Access Techniques | FDMA | TDMA | CDMA | OFDM

Lecture 87: Lec 6 | CDMA | Code Division Multiple Access

Lecture 88: Lec 7 | CDMA | Code Division Multiple Access | Multiuser Detection

Lecture 89: Lec 8 | Orthogonal Frequency Division Multiplexing

Lecture 90: L 1 | Noise introduction | Natural Noise | Man-made Noise

Lecture 91: L 2 | AWGN | Noise introduction | Additive White Gaussian Noise

Lecture 92: L 3 |Optimum Linear Detection | Binary Polar Signalling

Lecture 93: L 4 | SNR of Integrate and Dump Filter

Lecture 94: L 5 | Probability of Error | Integrate and Dump Filter

Lecture 95: L 6 | Probability of Error of Optimum Receiver

Lecture 96: L 7 | SNR & Transfer Function of Optimum Receiver

Lecture 97: L 8 | Introduction to Matched Filter | SNR of Matched Filter

Lecture 98: L 9 | General Expression of Probability of Error

Lecture 99: L 10 | Analysis of Matched Filter

Lecture 100: L 11 | Correlator/ Coherent Receiver for Digital Carrier Modulation

Lecture 101: L 12 | Probability of Error of ASK | Amplitude Shift Key

Lecture 102: L 13 | Probability of Error of PSK | Phase Shift Key

Lecture 103: L14 | Probability of Error of FSK | Frequency Shift Key

Lecture 104: Random Process Numerical | Stationarity

Lecture 105: Random Process Numerical | Stationarity

Lecture 106: Random Process Numerical | Stationarity | Mean & Autocorreation of Random Process

Lecture 107: Autocorrelation Function Numerical | Random Process