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Coding Theory Fundamentals

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1077 min

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Why enroll

Participants join this course to clearly understand how reliable communication is achieved in real systems like mobile networks, satellite communication, and the internet. The course explains complex ideas in an easy and step-by-step manner, making it suitable for beginners as well as working professionals. It helps learners build strong fundamentals that are useful for higher studies, competitive exams, and research. Many join to gain practical insight into modern coding techniques used in 4G, 5G, and data transmission systems. Overall, this course helps learners feel confident about error control coding and its real-world importance.

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Is this course for you?

You should take this if

You work in Telecommunication or Electronics & Instrumentation
You're a Electronics & Telecommunication / Computer Science professional
You have 3+ years of hands-on experience in this field
You want to build skills in Engineering & Design, Research & Developmnet

You should skip if

You're new to this field with no prior experience
You need a different specialisation outside Electronics & Telecommunication
You need live interaction with an instructor

Course details

Error control coding plays a crucial role in ensuring reliable data transmission in every digital communication system. In this course, you will learn why errors occur during transmission due to noise, interference, and channel imperfections, and how coding techniques help detect and correct these errors. The course starts with very basic concepts such as repetition codes so that even beginners can easily build a strong understanding of the fundamentals. It then moves step by step to linear block codes and convolutional codes, explaining their structure, working principles, and practical importance. You will clearly understand how encoding is done at the transmitter and how decoding is performed at the receiver to recover the original data. Well-known codes like Hamming codes and Reed–Muller codes are explained in detail with intuitive reasoning. The course also introduces modern and powerful coding techniques such as LDPC and turbo codes, which are widely used in today’s high-speed communication systems. Throughout the course, real-world examples and applications are discussed to connect theory with practice. By the end of the course, you will gain a solid understanding of how simple codes can be combined and concatenated to create highly efficient and powerful error-correcting systems used in modern communication technologies.

Source: An Introduction to coding theory [Youtube Channel] NPTEL

Course suitable for

Key topics covered

Basics of error control coding
Introduction to coding theory fundamentals
Definition of codes and terminology
Simple repetition codes and their use
Linear block code basics
Generator and parity-check matrices
Hamming distance and error detection
Syndrome decoding method
Introduction to convolutional codes
Trellis and state diagrams
Encoding using convolutional codes
Viterbi decoding algorithm overview
Comparison of block vs convolutional codes
Real-world applications of coding
Summary and future directions in error control

Course content

The course is readily available, allowing learners to start and complete it at their own pace.

30 lectures17 hr 57 min

Introduction to Error Control Coding-I
24 min
Introduction to Error Control Coding-II
19 min
Introduction to Error Control Coding-III
35 min
Introduction to Linear Block Codes, Generator Matrix and Parity Check Matrix
29 min
Syndrome, Error Correction and Error Detection
19 min
Problem Solving Session-I
30 min
Decoding of Linear Block Codes
47 min
Distance Properties of Linear Block Codes-I
21 min
Distance Properties of Linear Block Codes-II
39 min
Problem Solving Session-II
41 min
Some Simple Linear Block Codes-I
50 min
Some Simple Linear Block Codes-II: Reed Muller Codes
51 min
Bounds on the Size of a Code
32 min
Problem Solving Session-III
18 min
Introduction to Convolutional Codes-I: Encoding
46 min
Introduction to Convolutional Codes-II: State Diagram, Trellis Diagram
26 min
Convolutional Codes: Classification, Realization
43 min
Convolutional Codes:Distance Properties
30 min
Decoding of Convolutional Codes-I: Viterbi Algorithm
34 min
Decoding of Convolutional Codes-II: BCJR Algorithm
66 min
Problem solving session-IV
40 min
Problem solving session-V
32 min
Low Density Parity Check Codes
51 min
Performance Bounds for Convolutional Codes
34 min
Decoding of Low Density Parity Check Codes-I
28 min
Decoding of Low Density Parity Check Codes-II: Belief Propagation Algorithm
41 min
Turbo Codes
40 min
Turbo Decoding
52 min
Problem Solving Sessions-VI
26 min
Distance Properties of Turbo Codes
33 min

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Questions and Answers

Q: You're on the HIL bench validating a CAN-FD link and googling "how to verify Hamming distance during CAN-FD loop check" after homologation fails by 3% BER margin. What do you verify first to decide if the coding scheme itself is at fault rather than the transceiver?

A: That's the most common mistake — treating channel quality as interchangeable with code capability. The difference matters because minimum Hamming distance is a property of the encoder/decoder pair, and you isolate it by deterministic error injection before chasing PHY effects that sit outside the DFMEA for coding logic.

Q: During a safety review you're searching "ISO 26262 communication error detection coding requirement rationale" because two seniors disagree. Why does ISO 26262 push for a quantified residual error probability rather than naming a specific code like CRC-16?

A: That's the most common mistake — assuming the standard is shy about specifics. The difference matters because functional safety cares about quantified risk reduction, not the badge on the algorithm, and that flexibility is what lets you close the safety case when architectures differ.

Q: Field data shows rising frame errors and you're searching "coding theory bit errors due to connector corrosion automotive" to sanity-check assumptions. Which degradation mechanism is most likely masquerading as a coding weakness?

A: That's the most common mistake — blaming math for metallurgy. The difference matters because increased noise from intermittent contacts inflates BER, and no linear block code will save you from a connector that's violating its material assumptions.

Q: The datasheet says the LDPC decoder achieves 10^-7 BER at Eb/N0 = 4 dB, but you measure 3% worse and google "LDPC decoder datasheet BER mismatch acceptance test". What's the correct next check before declaring nonconformance?

A: That's the most common mistake — comparing apples to a different code configuration. The difference matters because LDPC performance is tightly bound to block size and iterations, and acceptance testing has to mirror those knobs before you escalate a supplier NCR.