Principles of Communication Systems - II

Principles of Communication Systems - II

@VTU COE

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What you will learn

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This course is a sequel to Principles of Communication-Part I and covers fundamental concepts of communication systems, especially focusing on various aspects of modern digital communication systems. However, all the modules in this course will be independent of the previous course and hence students who could not participate in Principles of Communication-Part I will also be able to follow the course. Beginning with the basic theory of digital communication systems pertaining to pulse shaping, modulation and optimal detection, the course will also cover several important digital modulation techniques such as Binary Phase Shift Keying (BPSK), Frequency Shift Keying (FSK), Quadrature Amplitude Modulation (QAM), M-ary Phase Shift Keying (M-PSK) etc. Other fundamental concepts such as Information Theory, Channel Capacity, Entropy Coding and Error Control Coding will be dealt with in the later parts of the course. This course is suitable for all UG/PG students and practicing engineers/ managers who are looking to enhance their knowledge of the fundamental principles underlying various communication systems as well as students preparing for their college/ university/ competitive exams.

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Digital Communication Receiver, Optimal SNR and Matched Filter
Preview 24:15
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Probability of Error in Digital Communication and Probability Density Functions of Output
Preview 24:41
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Probability of Error in Digital Communication, Optimal Decison Rule and Gaussian Q function
Preview 23:48
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Introduction to Binary Phase Shift Keying (BPSK) Modulation, Optimal Decision Rule and Probability of Bit-Error or Bit-Error Rate (BER)
Preview 34:47
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Introduction to Amplitude Shift Keying (ASK) Modulation
Preview 24:47
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Optimal Decision Rule for Amplitude Shift Keying (ASK), Bit Error Rate (BER) and Comparison with Binary Phase Shift Keying (BPSK) Modulation
Preview 24:49
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Introduction to Signal Space Concept and Orthonormal Basis Signals
Preview 24:09

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Introduction to Frequency Shift Keying (FSK)
Preview 27:45
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Optimal Decision Rule for FSK, Bit Error Rate (BER) and Comparison with BPSK, ASK
Preview 24:19
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Introduction to Quadrature Phase Shift Keying (QPSK)
Preview 22:24
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Waveforms of Quadrature Phase Shift Keying (QPSK)
Preview 23:20
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Matched Filtering, Bit Error Rate and Symbol Error Rate for Quadrature Phase Shift Keying (QPSK)
Preview 36:19
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Introduction to M-ary PAM (Pulse Amplitude Modulation), Average Symbol Power and Decision rules
Preview 30:31
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M-ary PAM (Pulse Amplitude Modulation) -Part-II, Optimal Decision Rule and Probability of Error
Preview 39:29

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M-ary QAM (Quadrature Amplitude Modulation) Part-I, Introduction, Transmitted Waveform and Average Symbol Energy
Preview 27:24
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M-ary QAM (Quadrature Amplitude Modulation) - Part-II, Optimal Decision Rule, Probability of Error and Contellation Diagram
Preview 37:24
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M-ary PSK (Phase Shift Keying) Part-I, Introduction , Transmitted Waveform and Constellation Diagram
Preview 25:45
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M-ary PSK (Phase Shift Keying) - Part- II, Optimal Decision Rule, Nearest Neighbor Criterion and Approximate Probability of Error
Preview 26:24
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Introduction to Information Theory, Relevance of Information Theory and Characterization of Information
Preview 33:22
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Definition of Entropy, Average of Information / Uncertainity of source and Properties of Entropy
Preview 24:32
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Entropy Example- Binary Source Maximum and Minimum Entropy of Binary Source
Preview 27:34

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Maximum Entropy of Source with M-ary Alphabet, Concave/Convex Functions and Jensens Inequality
Preview 28:48
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Joint Entropy , Definition of Joint Entropy of Two Sources and Simple Examples for Joint Entropy Computation
Preview 28:09
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Properties of Joint Entropy and Relation between Joint Entropy and Marginal Entropies
Preview 27:34
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Conditional Entropy, Example of Conditional Entropy and Properties of Conditional Entropy
Preview 30:00
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Mutual Information, Diagrammatic Representation and Properties of Mutual Information
Preview 24:24
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Simple Example of Mutual Information and Practical Example of Mutual Information-Binary Symmetric Channel
Preview 26:09
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Channel Capacity, Implications of Channel Capacity, Claude E. Shannon- Father of Information Theory and Example of Capacity of Binary Symmetric Channel
Preview 28:29

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Differential Entropy and Example for Uniform Probability Density function
Preview 19:09
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Differential Entropy of Gaussian Source and Insights
Preview 18:49
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Joint Conditional/ Differential Entropies and Mutual Information
Preview 16:29
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Capacity of Gaussian channel- Part I
Preview 24:18
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Capacity of Gaussian Channel Part-II, Practical Implications and Maximum rate in bits\sec
Preview 40:01
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Introduction to Source Coding and Data Compression, Variable Length codes and Unique Decodability
Preview 29:03
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Uniquely Decodable Codes, Prefix-free code, Instantaneous Code and Average Code length
Preview 26:09

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Binary Tree Representation of Code, Example and Kraft Inequality
Preview 32:09
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Lower Bound on Average Code Length and Kullback-Leibler Divergence
Preview 24:34
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Optimal Code length, Constrained Optimization and Morse Code Example
Preview 23:05
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Approaching Lower Bound on Average code length and Block Coding
Preview 28:18
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Huffman Code, Algorithm, Example and Average Code Length
Preview 38:24
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Introduction to channel coding, Rate of Code, Repetition Code and Hamming Distance
Preview 13:09
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Introduction to Convolutional Codes, Binary Field Arithmetic and Linear Codes
Preview 17:18

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Example of Convolutional Code Output and Convolution Operation for Code generation
Preview 18:09
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Matrix Representation of Convolutional Codes, Generator Matrix, Transform Domain Representation and Shift Register Architecture
Preview 27:22
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State Diagram Representation of Convolutional Code, State transitions and Example of Code Generation using State transitions
Preview 37:34
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Trellis Representation of Convolutional Code and Valid Code Words
Preview 34:29
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Decoding of the Convolutional Code, Minimum Hamming distance and Maximum Likelihood Codeword Estimate
Preview 27:21
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Principle of Decoding of Convolutional code
Preview 27:28
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Viterbi Decoder for Maximum Likelihood Decoding of Convolutional Code Using Trellis Representation, Branch Metric Calculation, State Metric Calculation and Example
Preview 14:43
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@VTU COE
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Instructor
3.2 Rating
5446 Students
800 Courses
About Instructor

VTU is one of the largest Technological Universities in India with 24 years of Tradition of excellence in Engineering & Technical Education, Research and Innovations. It came into existence in the year 1998 to cater the needs of Indian industries for trained technical manpower with practical experience and sound theoretical knowledge.

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  • Course Duration
    24 h 58 m 56 s
  • Course Level
    Intermediate
  • Student Enrolled
    0
  • Language
    English
This Course Includes
  • 24 h 58 m 56 s Video Lectures
  • 0 Quizzes
  • 0 Assignments
  • 0 Downloadable Resources
  • Full Lifetime Access
  • Certificate of Completion