Learn · Principles of Communication Systems - II
Lecture 15 of 57Week-3
Introduction to Frequency Shift Keying (FSK)
Prof. Aditya K. Jagannatham · IIT Kanpur
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- Principles of Communication Systems - II, IIT Kanpur (at NPTEL)
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All the lectures
- Introduction to Digital Communication Systems
- Spectrum of Transmitted Digital Communication Signal and Wide Sense Stationarity
- Spectrum of Transmitted Digital Communication Signal, Autocorrelation Function and Power Spectral Density
- Spectrum of Transmitted Digital Communication Signal, Relation to Energy Spectral Density and Introduction to AWGN Channel
- Additive White Gaussian Noise (AWGN) Properties, Gaussian Noise and White Noise
- Structure of Digital Communication Receiver, Receiver Filter and Signal-to-Noise Power Ratio (SNR)
- Digital Communication Receiver, Noise Properties and Output Noise Power
- Digital Communication Receiver, Optimal SNR and Matched Filter
- Probability of Error in Digital Communication and Probability Density Functions of Output
- Probability of Error in Digital Communication, Optimal Decison Rule and Gaussian Q function
- Introduction to Binary Phase Shift Keying (BPSK) Modulation, Optimal Decision Rule and Probability of Bit-Error or Bit-Error Rate (BER)
- Introduction to Amplitude Shift Keying (ASK) Modulation
- Optimal Decision Rule for Amplitude Shift Keying (ASK), Bit Error Rate (BER) and Comparison with Binary Phase Shift Keying (BPSK) Modulation
- Introduction to Signal Space Concept and Orthonormal Basis Signals
- Introduction to Frequency Shift Keying (FSK)
- Optimal Decision Rule for FSK, Bit Error Rate (BER) and Comparison with BPSK, ASK
- Introduction to Quadrature Phase Shift Keying (QPSK)
- Waveforms of Quadrature Phase Shift Keying (QPSK)
- Matched Filtering, Bit Error Rate and Symbol Error Rate for Quadrature Phase Shift Keying (QPSK)
- Introduction to M-ary PAM (Pulse Amplitude Modulation), Average Symbol Power and Decision rules
- M-ary PAM (Pulse Amplitude Modulation) -Part-II, Optimal Decision Rule and Probability of Error
- M-ary QAM (Quadrature Amplitude Modulation) Part-I, Introduction, Transmitted Waveform and Average Symbol Energy
- M-ary QAM (Quadrature Amplitude Modulation) - Part-II, Optimal Decision Rule, Probability of Error and Contellation Diagram
- M-ary PSK (Phase Shift Keying) Part-I, Introduction , Transmitted Waveform and Constellation Diagram
- M-ary PSK (Phase Shift Keying) - Part- II, Optimal Decision Rule, Nearest Neighbor Criterion and Approximate Probability of Error
- Introduction to Information Theory, Relevance of Information Theory and Characterization of Information
- Definition of Entropy, Average of Information / Uncertainity of source and Properties of Entropy
- Entropy Example- Binary Source Maximum and Minimum Entropy of Binary Source
- Maximum Entropy of Source with M-ary Alphabet, Concave/Convex Functions and Jensens Inequality
- Joint Entropy , Definition of Joint Entropy of Two Sources and Simple Examples for Joint Entropy Computation
- Properties of Joint Entropy and Relation between Joint Entropy and Marginal Entropies
- Conditional Entropy, Example of Conditional Entropy and Properties of Conditional Entropy
- Mutual Information, Diagrammatic Representation and Properties of Mutual Information
- Simple Example of Mutual Information and Practical Example of Mutual Information-Binary Symmetric Channel
- Channel Capacity, Implications of Channel Capacity, Claude E. Shannon- Father of Information Theory and Example of Capacity of Binary Symmetric Channel
- Differential Entropy and Example for Uniform Probability Density function
- Differential Entropy of Gaussian Source and Insights
- Joint Conditional/ Differential Entropies and Mutual Information
- Capacity of Gaussian channel- Part I
- Capacity of Gaussian Channel Part-II, Practical Implications and Maximum rate in bits\sec
- Introduction to Source Coding and Data Compression, Variable Length codes and Unique Decodability
- Uniquely Decodable Codes, Prefix-free code, Instantaneous Code and Average Code length
- Binary Tree Representation of Code, Example and Kraft Inequality
- Lower Bound on Average Code Length and Kullback-Leibler Divergence
- Optimal Code length, Constrained Optimization and Morse Code Example
- Approaching Lower Bound on Average code length and Block Coding
- Huffman Code, Algorithm, Example and Average Code Length
- Introduction to channel coding, Rate of Code, Repetition Code and Hamming Distance
- Introduction to Convolutional Codes, Binary Field Arithmetic and Linear Codes
- Example of Convolutional Code Output and Convolution Operation for Code generation
- Matrix Representation of Convolutional Codes, Generator Matrix, Transform Domain Representation and Shift Register Architecture
- State Diagram Representation of Convolutional Code, State transitions and Example of Code Generation using State transitions
- Trellis Representation of Convolutional Code and Valid Code Words
- Decoding of the Convolutional Code, Minimum Hamming distance and Maximum Likelihood Codeword Estimate
- Principle of Decoding of Convolutional code
- Viterbi Decoder for Maximum Likelihood Decoding of Convolutional Code Using Trellis Representation, Branch Metric Calculation, State Metric Calculation and Example
- Live Session 09-11-2019