About this previous year paper
INFORMATION THEORY AND CODING – B.Tech (Semester IV), REC-406
This is a 3-hour, 70-mark theory exam designed to evaluate students’ understanding of information theory fundamentals, entropy, source coding, channel capacity, coding theorems, error-correcting codes, and decoding concepts.
The paper contains three structured sections, testing basic definitions, derivations, numerical problems, and advanced coding concepts.
SECTION A — Short Answer Questions (2 × 7 = 14 Marks)
This section has 7 brief questions targeting conceptual clarity.
Topics include:
Channel capacity Information rate
Relation between information & probability Why logarithms are used to measure information
Extension of discrete memoryless source Properties of entropy
Source coding theorem
These short questions test basic definitions, formulas, and theoretical understanding.
SECTION B — Descriptive Questions (7 × 3 = 21 Marks)
Attempt any 3 out of 5.
This section assesses reasoning, derivations, coding logic, and theory explanation.
Major topics include:
Mutual information & capacity of Binary Erasure Channel (BEC)
Properties of a typical set (AEP – Asymptotic Equipartition Property)
Channel coding theorem preview & channel capacity properties
Proof that average code length ≥ entropy (L ≥ H)
Physical significance of different entropies
This section focuses on analytical and mathematical reasoning.
SECTION C — Long Answer Questions (7 marks each)
Each major question contains two choices (a or b) requiring detailed proofs, numerical solutions, coding procedures, or diagrams.
Q3 – Coding Inequalities & DSP Concepts
Extended Kraft Inequality + proving codeword lengths
Log-Sum Inequality & Data-Processing Inequality
Q4 – Communication System Probability Questions
Includes error probabilities using Bayes’ theorem:
Calculating p(r0), p(r1)
Probability of correct/incorrect detection
Receiver error probability
or
Entropy, code efficiency & redundancy using Huffman coding for 8 symbols
Q5 – Convolution Codes / Channel Capacity
Encoding convolution codes using shift registers, code tree & state diagram
Deriving capacity of infinite bandwidth channel
Q6 – Linear Block & Cyclic Codes
Standard arrays
Generator matrix for (7,4) cyclic code & systematic coding
Q7 – Trellis & Viterbi Decoding / Shortened Codes
Trellis diagram + Viterbi decoding (error detection & correction)
Concept and application of shortened codes
This section tests deep understanding of coding theory, error correction, probability, and decoding algorithms.
OVERALL PURPOSE OF THE EXAM
The question paper aims to evaluate whether the student can:
Understand entropy, mutual information & channel capacity
Apply information theory laws to practical communication channels
Construct & analyze various source and channel coding schemes
Perform Huffman, convolutional & cyclic coding
Use decoding techniques such as Viterbi and Standard Array
Apply mathematical inequalities (Kraft, Log-Sum, Data-Processing)
Compute error probabilities in noisy communication systems
This paper ensures students gain strong foundations in digital communication and coding theory, essential for networking, telecommunications, and data compression applications.