Course10810409857 lectures
Principles of Communication Systems - II
Prof. Aditya K. Jagannatham · IIT Kanpur
Lectures
Week-1
- 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
Week-2
- 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
Week-3
- 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
Week-4
- 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
Week-5
- 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
Week-6
- 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
Week-7
- 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
Week-8
- 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
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT Kanpur
- Department
- Electrical Engineering
- Teacher
- Prof. Aditya K. Jagannatham
- Level
- Undergraduate
- Language
- English
- Subjects
- Engineering
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed
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