Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Digital Communication & Information Theory: Masterclass
Rating: 3.5 out of 5(1 rating)
108 students

Digital Communication & Information Theory: Masterclass

Unlock the Future of Communication
Last updated 6/2025
English

What you'll learn

  • Information theory & coding techniques
  • Digital transmission technologies
  • Analog transmission methods
  • Error control in transmission

Course content

7 sections60 lectures11h 29m total length
  • Section 1 Overview1:10

    Explore the foundations of information theory, measurement of information, and channel capacity for discrete memoryless and continuous channels, with Huffman and Shannon-Fano coding in digital communication systems.

  • Lec 1.1 Introduction to Information Theory4:36

    This introduction to information theory distinguishes data, information, and knowledge, using examples such as temperature, stock market values, and rainfall. It explains how probability and uncertainty shape information content.

  • Lec 1.2 Measurement of Information4:07

    Explore how information content is inversely proportional to the probability of an event, using log base two, and distinguish discrete from continuous information sources with a dart example.

  • Lec 1.3 Channel Capacity7:56

    Explain how a communication system uses a source, a channel, and a destination, defining channel capacity as the maximum bits per second governed by bandwidth and signal-to-noise ratio.

  • Lec 1.4 Hartley Shanon Law Justified10:35

    Justifies Hartley–Shannon law for Gaussian channels by defining channel capacity as the maximum reliable transmission rate. Shows c = B log2(1 + S/N) governs bandwidth and SNR trade-offs.

  • Lec 1.5 Entropy8:47

    Explore entropy as the average information per source symbol, its bounds for a stationary, independent source, and log-base-2 calculations in bits per symbol.

  • Lec 1.6 Layered View of Digital Communication6:13

    Understand the layered digital communication system, from source encoding and channel encoding with redundancy to combat noise, through modulation and channel transmission, to channel decoding, demodulation, and source decoding.

  • Lec 1.7 Discrete Source Encoding Algorithm4:41

    Study the discrete source encoding algorithm and source entropy, showing that the average number of binary digits per message equals the entropy, even with nonuniform message probabilities.

  • Lec 1.8 Huffman Code8:31

    Explore the Huffman coding algorithm for source encoding, using a six-symbol example to show assigning shorter codes to common messages, building the probability tree, and deriving bits, entropy, and efficiency.

  • Lec 1.9 Decoding of r-ary Huffman Code12:15

    Decode r-ary Huffman codes by handling dummy symbols, derive codewords for quaternary encoding, and evaluate average length, entropy, efficiency, and redundancy.

  • Lec 1.10 Shanon Fano Algorithm3:41

    Learn the Shannon-Fano algorithm for source encoding by ranking probabilities, splitting into near-equal parts, and encoding symbols with shorter codes for higher-probability messages.

  • Lec 1.11 Communication Channels12:03

    Examine how to reduce error probability on noisy channels by managing energy per bit and bit rate, within channel capacity, and study binary symmetric and asymmetric discrete memoryless channels.

  • Lec 1.12 Channel Capacity of Discrete Memoryless Channels13:54

    Explore the channel capacity of discrete memoryless channels by defining mutual information and equivocation, and show how h(x) minus h(x|y) equals I(x;y) and its maximization.

  • Lec 1.13 Channel Capacity of Continuous Channels7:54

    Explore the channel capacity of continuous channels, deriving from differential entropy and Gaussian pdf, and apply mutual information to show C = B log2(1 + S_p/N_p) for AWGN channels.

  • Lec 1.14 Exam Questions Solved Part 116:04

    Solve gate-related problems in information theory by computing entropy and channel capacity, including entropy of the time to first head, AWGN limits, and binary symmetric channels.

  • Lec 1.15 Exam Questions Solved Part 213:08

    Solve gate-style information theory problems, including at-most-one-error probability in a binary channel, entropy of memoryless sources, and encoding efficiency for equiprobable symbols.

  • Section 1 Quiz

Requirements

  • Preliminary mathematics

Description

Are you ready to master the core principles of digital communication and become a confident professional in today’s high-demand tech world?

Join our all-in-one “Masterclass: Digital Communication and Information Theory” – a comprehensive, career-enhancing online course designed for engineering students, tech enthusiasts, and aspiring communication experts. Through 6 expertly structured modules, this course takes you from the fundamentals to advanced techniques in digital and analog communication, information theory, transmission, modulation, and multimedia networking.

What You’ll Learn – Course Modules Overview:

Module 1: Foundations of Information Theory

  • Understand Hartley-Shannon law, entropy, channel capacity

  • Explore Huffman and Shannon-Fano coding

  • Learn the difference between discrete and continuous communication channels

  • Practice with exam-style Q&A discussions and solutions

Module 2: Digital Transmission Techniques

  • Dive into digital-to-digital conversion and line coding

  • Learn PCM, Delta modulation, analog-to-digital conversion

  • Understand various communication types and transmission modes

Module 3: Analog Transmission and Modulation

  • Explore digital-to-analog conversions: ASK, FSK, PSK

  • Learn analog-to-analog techniques: AM, FM, PM

  • Gain practical insights into real-world analog communication systems

Module 4: Multiplexing & Switching Techniques

  • Master bandwidth utilization using FDM, TDM, WDM

  • Understand spread spectrum technologies

  • Explore switching techniques: circuit switching, packet switching

  • Learn about transmission media in modern networks

Module 5: Error Detection and Correction

  • Learn how to detect and correct errors with:

    • Linear block codes

    • Cyclic redundancy checks (CRC)

    • Checksums and coding theory principles

Module 6: Multimedia Networking Applications

  • Discover audio/video digitization and compression methods

  • Understand streaming (live and stored) and VOIP technologies

  • Gain valuable skills for real-world applications like Zoom, YouTube, and online conferencing

Course Features:

  • Practice Quizzes for Every Module

  • Final Course Completion Quiz

  • Hands-on Q&A with Real Exam Patterns

  • Certificate of Completion

Who Should Enroll?

  • Undergraduate and graduate engineering students

  • Computer science & electronics majors

  • Competitive exam aspirants (GATE, NET, etc.)

  • Anyone interested in digital communication careers

Why Take This Course?

  • Boost your academic performance

  • Build industry-relevant skills

  • Prepare for tech job interviews

  • Learn at your own pace, anytime, anywhere

Don’t miss out on your chance to gain in-demand digital communication skills that companies are looking for!


Enroll now and start your journey toward mastering digital communication and information theory!

Who this course is for:

  • Beginner electronics and communication engineering students
  • Enthusiasts who want to work with codes