(SEM VI) THEORY EXAMINATION 2017-18 DIGITAL COMMUNICATION

Digital Communication (NEC-602)

Complete Section-Wise Explanation – B.Tech Semester VI

Introduction to the Subject

Digital Communication deals with the transmission of information in digital form over communication channels. Unlike analog communication, digital communication provides better noise immunity, higher reliability, efficient bandwidth utilization, and easier integration with computers and modern networks.

This subject helps students understand:

Digital signaling and modulation techniques                     Random variables and random processes

Noise analysis and error probability                                   Information theory and channel capacity

Source coding and channel coding                                    Spread spectrum and multiple access techniques

The paper is divided into three sections: A, B, and C, each testing a different level of understanding.

SECTION A – Fundamental Concepts (Short Answers)

Pattern:
Attempt all questions
10 questions × 2 marks = 20 marks

Nature of Section A

Section A checks whether your basic concepts are strong. Answers must be short but technically correct. These questions are direct and mostly definition-based.

Explanation of Section A Topics

Baseband and Passband Signaling
Baseband signaling transmits signals directly without modulation, while passband signaling shifts the signal to a higher frequency using modulation, which is suitable for wireless communication.

Linear Time Invariant (LTI) System
An LTI system is one whose output response is linear and does not change with time. Such systems are fully characterized by their impulse response.

Wide Sense Stationary vs Strict Sense Stationary Process
A strict sense stationary process has statistical properties independent of time, while a wide sense stationary process requires only constant mean and autocorrelation depending on time difference.

Channel Capacity and Entropy Relationship
For error-free communication, the channel capacity must be greater than or equal to the source entropy. This relationship is explained by Shannon’s information theory.

Probability of Error: ASK vs BPSK
BPSK has a lower probability of error compared to ASK because BPSK is more resistant to noise.

Processing Gain
Processing gain is the ratio of spread bandwidth to original signal bandwidth, commonly used in spread spectrum systems.

Manchester and NRZ(L) Line Coding
This question tests understanding of line coding schemes by sketching waveforms for a given binary sequence.

Slow and Fast Frequency Hopping
In slow frequency hopping, multiple bits are transmitted per hop, whereas in fast frequency hopping, frequency changes occur multiple times within one bit duration.

Source Coding vs Channel Coding
Source coding reduces redundancy for efficient transmission, while channel coding adds redundancy to detect and correct errors.

AWGN Channel
An Additive White Gaussian Noise channel models noise that is Gaussian distributed, has constant power spectral density, and is added to the signal.

SECTION B – Conceptual Explanation & Numericals

Pattern:
Attempt any three questions
3 × 10 marks = 30 marks

Nature of Section B

This section requires detailed explanations written in paragraphs, often supported with block diagrams and numerical calculations.

Explanation of Important Questions

Differential Encoding and DPSK

Differential encoding transmits information based on phase difference rather than absolute phase. In DPSK, data is encoded by changing the phase of the carrier relative to the previous symbol. The modulator and demodulator diagrams must be clearly explained.

Intersymbol Interference (ISI) and Raised Cosine Filter

ISI occurs when pulses spread and interfere with neighboring symbols. A raised cosine filter is used to eliminate ISI by satisfying the Nyquist criterion, ensuring zero ISI at sampling instants.

Source Coding and Shannon-Fano Coding

Source coding reduces average code length. Shannon-Fano coding assigns shorter codes to more probable symbols. The question requires code construction, calculation of average code length, entropy, and coding efficiency.

Random Variable vs Random Process & PDF Problem

A random variable represents a single outcome, while a random process represents a collection of random variables over time. The numerical part requires finding constants in an exponential probability density function using normalization conditions.

Convolutional Encoder Numerical

This question tests understanding of convolutional coding. Students must sketch the encoder, draw the code tree and trellis diagram, and determine the output sequence for a given input.

Coherent and Non-Coherent Reception

Coherent reception requires carrier phase synchronization, while non-coherent reception does not. A coherent digital receiver block diagram is explained in detail.

SECTION C – Long Answer & Analytical Questions

Pattern:

Attempt any one part from each question
5 questions × 10 marks = 50 marks

This section has the highest weightage and plays a key role in final scoring.

Question 3

Matched Filter

A matched filter maximizes the signal-to-noise ratio at the output. The derivation shows that the output is proportional to the autocorrelation of the input signal. A proper block diagram and mathematical proof are expected.

M-ary Signaling and M-PSK

M-ary signaling increases data rate without increasing bandwidth. M-PSK modulation uses multiple phase states, and its constellation diagram must be explained.

Question 4

Gaussian Random Variable & Power Spectral Density

Gaussian random variables are widely used in noise modeling. Power spectral density describes how signal power is distributed over frequency for a WSS process.

CDMA and Spread Spectrum

CDMA allows multiple users to share the same bandwidth using unique spreading codes. This question explains how spreading improves capacity and connectivity.

Question 5

Mutual Information & Shannon Capacity Numerical

Mutual information measures information shared between input and output. Using Shannon’s formula, channel capacity is calculated for a given bandwidth and SNR.

Information Source Coding Problem

This numerical evaluates information per symbol, probability of 0s and 1s, and code efficiency using given symbol probabilities and codes.

Question 6

Entropy and Its Relationships

Entropy measures uncertainty. The derivation of joint and conditional entropy relationship is explained mathematically.
 

Block Code Design

This question requires designing a block code with minimum distance three, ensuring single-bit error correction.

Question 7

MSK Modulation & Comparison with QPSK

MSK is a continuous phase modulation technique with constant envelope. Differences between MSK and QPSK are explained in terms of bandwidth, phase continuity, and performance.

P-N Sequence Generation Numerical

This problem involves generating a pseudo-noise sequence using a shift register and calculating chip duration, sequence duration, and period.