This document provides an introduction and overview of a course on Introduction to Communication Systems. It outlines the course milestones, prerequisites, outcomes, units of study, reference materials, and potential job opportunities. The course covers topics such as continuous and discrete time signals, Fourier analysis, amplitude modulation, frequency modulation, noise theory, analog and digital modulation techniques, sampling, and software defined radio. Experiential learning opportunities include MATLAB and Python programming along with hands-on projects.

1. INTRODUCTIONTOCOMMUNICATIONSYSTEMS
SECTIONA:2022-23
FACULTY:DR.S.SRIDEVI,ASSOCIATEPROF
1

2. AGENDA
1. COURSE – MILESTONES
2. PRE-REQUISITE
3. COURSE OUTCOMES
4. UNIT 1 UNIT 3
5. REFERENCE
6. JOB OPPORTUNITIES
2

3. CO IV
Advanced communication
systems and prospects of AI
CO I
Introduction to Signals
& communication
systems
CO III
CO II
Noise in
communication
systems
Course
Milestones
21AIE204 – INTRODUCTION TO
COMMUNICATION SYSTEMS
Faculty: Dr.S.Sridevi
3
Analog & Digital
communication
systems

4. CONCEPTMAP
4

5. Course Outcomes
CO
Nos.
Course Outcomes
Level of learning
domain
CO1
Develop an understanding on the basic analog
communication engineering
K3
CO2
Develop an understanding on the Digital
communication engineering
K3
CO3
Understanding and implementation of Analog
and Digital modulation and De-modulation
techniques using MATLAB/GNURADIO along with
supporting hardware like RTL-SDR, ADALM PLUTO
etc
K3
CO4
Develop an appreciation of the role of
artificial intelligence in emerging
communication systems.
K3
5

6. Continuous Time Signal
CLASSIFICATION OF SIGNALS & SYSTEMS
Discrete Time Signal
System
6

7. BLOCK DIAGRAM
INTRODUCTION TO COMMUNICATION SYSTEMS 7

8. Continuous Time Signal
CLASSIFICATION OF SIGNALS & SYSTEMS
Discrete Time Signal
System
8

9. Fourier Series
CONTINUOUS TIME SIGNALS & SYSTEMS
Fourier Transform
9

10. Fourier Series
CONTINUOUS TIME SIGNALS & SYSTEMS
Fourier Transform
10

11. Fourier Series
CONTINUOUS TIME SIGNALS & SYSTEMS
Fourier Transform
11

12. Fourier Series
UNIT – II : CONTINUOUS TIME SIGNALS & SYSTEMS
Fourier Transform
= Equation
12

13. Fourier Series
UNIT – II : CONTINUOUS TIME SIGNALS & SYSTEMS
Fourier Transform
= Equation
13

14. Fourier Series
UNIT – II : CONTINUOUS TIME SIGNALS & SYSTEMS
Fourier Transform
= Equation
Time
Domain
Frequency
Domain
14

15. Need for Amplitude
Modulation
AMPLITUDE MODULATION
What is Amplitude
Modulation?
Modulation
System design
15
https://www.engineersgarage.com/circuit_d
esign/circuit-design-how-to-make-an-
amplitude-modulated-wave/
https://www.youtube.com/watch/CRXxm8N
7oKU
Types of Amplitude
Modulation techniques

16. TYPES OF AM
MODULATORS
TYPES OF AMPLITUDE MODULATION
TYPES OF
TRANSMITERS/
RECEIVERS
TYPES OF
RECEIVERS
16
TUTORIAL
PROBLEMS

17. FREQUENCY
MODULATION
ANGLE MODULATION
PHASE MODULATION
17

18. TYPES OF NOISE
NOISE THEORY
NOISE THEORY
18
TUTORIAL
PROBLEMS
HIGH
FREQUENCY
LOW
FREQUENCY

19. Analog and Digital Modulation
19

20. DIGITAL MODULATIONS
20

21. Sampling
DISCRETE TIME SIGNALS
Continuous Time Signal
Discrete Time Signal
21

22. WhyDigitalmodulation? 22

23. Software Defined
Radio
Software Defined Radio 23
A radio that uses software to perform signal-processing tasks that
were traditionally performed by hardware
The digital processing of signals, in our case RF signals
Digital Signal
Processing
GNU Radio is a free & open-source software development
toolkit that provides signal processing blocks to implement
software radios.

24. GNURADIO - SDR 24
GNU Radio is a free & open-source software development toolkit that provides signal
processing blocks to implement software radios. It can be used with readily-available
low-cost external RF hardware to create software-defined radios, or without hardware
in a simulation-like environment. It is widely used in research, industry, academia,
government, and hobbyist environments to support both wireless communications
research and real-world radio systems.

25. TEXT BOOKS
Text Books:
1. George Kennedy and Bernard Davis, “Electronics Communication Systems”’ Tata
McGraw-Hill Edition, 2011.
2. Simon Haykin, “Digital Communication Systems”, John Wiley & Sons, Inc., 2013
3. K C Raveendranathan, “Communication Systems Modelling and Simulation
Using MATLAB and Simulink”,Universities Press (INDIA) Private Limited, 2011
4. Robert W. Stewart Software Defined Radio Using MATLAB & Simulink and the
Rtl-Sdr, On-line book, 2015.
5. Qasim Chaudhari, Wireless Communications from the Ground Up: Fundamentals
of Digital Communication Systems, CreateSpace Independent Publishers, 2016
6. Reinventing Wireless with Deep Learning, https://www.deepsig.ai/
25

26. Experiential learning
• MATLAB programming
• Python programming
• MATLAB Grader / Python tasks for assignments
26

27. 27

28. 28

29. GATE EXAM - 2023
PUBLIC
SECTOR MAHARATNA
MINIRATNA
29

30. Online teaching tools andActivities
Online tools
• MATLAB software
• Python software
Activity Learning
• One minute paper
• Virtual Gallery walk
• Interactive content
• Quiz / Polling
• Peer seminar presentation
• Problem solving
• Zigsaw puzzle
• Experiential learning
• Flipped classroom
TOOLS 30

31. Natural domain of a signal
• Time domain – Form of information is called as Signal
• Apply Fourier Transform to a signal in Time Domain to transform it to
Frequency domain (Information is Spectrum)
• Laplace Transform (complex)
• Z-Transform (Discrete)
• Wavelet Transform (Time & Freq)
Why do we analyze signals in different domains? 31

32. Frequency domain
• Time domain information
Why do we analyze signals in different domains?
Storage
space
Less for
Frequency
Domain
More for
Time
Domain
32

33. Frequency domain
• Time domain information
Why do we analyze signals in different domains?
Hidden
Information
More
access
Less
access
Use of mathematical tool to open
the bag to find hidden information
Visible
information
33

34. System – Impulse Response h(n) & Frequency Response H(W)
h(n)
Input
Signal
Output
Signal
Morning
Night
Why do we study about Systems?
Time domain Frequency domain
Different people have
different characters and
different behaviour
h(n)
Similarly Systems
generates different output
for different input signals
Input
Signals
Output
Signals
34

35. Signals
Modulation techniques
Advanced Digital modulation
Software defined radio
Introduction to Machine Learning & Artificial Intelligence
Sequential Learning
35

36. • Medical diagnostics
• Artificial intelligence
• Speech recognition
• Augmented reality
• Virtual Reality
• Machine vision
• Artificial brain
• 5G cellular communications
• Internet of Things
• Radio-frequency identification
Contemporary Technologies
36

37. Why is it important to Study this course?
Cellular
mobile
Telecommunication,
Information coding
Digital
TV and
Audio
Voice
Regocnit
ion
Central ADSP
applications Vision
Satellite
Applicati
ons
Geospatial
applications
Radar
&
Sonar
37

38. Application Areas
• Control
• Communications
• Signal Processing
38

39. Control Applications
• Industrial control and automation (Control the velocity or
position of an object)
• Examples: Controlling the position of a valve or shaft of a
motor
• Important Tools:
• Time-domain solution of differential equations
• Transfer function (Laplace Transform)
• Stability
39

40. Communication Applications
• Transmission of information (signal) over a channel
• The channel may be free space, coaxial cable, fiber optic
cable
• A key component of transmission: Modulation (Analog
and Digital Communication)
40

41. Modulation
• Analog Modulation: Transmitting audio signals.
• Advantage: Higher frequency range good propagation
X
X(t)
L
ocal Oscillator
Ax(t)cos(wt)
41

42. Modulation
• Frequency Modulation (FM), modulate the angle of the
carrier.
• Advantage: More robust to interference
42

43. Digital Modulation
• Used in CDs, digital cellular service, digital phone lines
and computer modems.
• Advantages:
• Can be encrypted
• Electronic routing of data is easier
• Digital storage faster
• Multimedia capability
43

44. Signal Processing Applications
• Signal processing=Application of algorithms to
modify signals in a way to make them more
useful.
• Goals:
• Efficient and reliable transmission, storage and display
of information
• Information extraction and enhancement
• Examples:
• Speech and audio processing
• Multimedia processing (image and video)
• Underwater acoustic
• Biological signal analysis
44

45. Multimedia Applications
• Compression: Fast, efficient, reliable transmission and
storage of data
• Applied on audio, image and video data for transmission
over the Internet, storage
• Examples: CDs, DVDs, MP3, MPEG4, JPEG
• Mathematical Tools: Fourier Transform, Quantization,
Modulation
45

46. JPEG Example
43K 13K 3.5K
• JPEG uses Discrete-Cosine Transform (similar to Fourier
Transform)
46

47. Biological Signal Analysis
• Examples:
• Brain signals (EEG)
• Cardiac signals (ECG)
• Medical images (x-ray, PET, MRI)
• Goals:
• Detect abnormal activity (heart attack, seizure)
• Help physicians with diagnosis
• Tools: Filtering, Fourier Transform
47

48. Example
• Brain waves are usually contaminated by noise and hard
to interpret
48

49. Biometrics
• Identifying a person using physiological characteristics
• Examples:
• Fingerprint Identification
• Face Recognition
• Voice Recognition
49

50. Audio Signal Processing
• Active noise cancellation:Adaptive filtering
• Headphones used in cockpits
• Digital Audio Effects
• Add special music effects such as delay, echo, reverb
• Audio signal separation
• Separate speech from interference
• Wind sound from music in cars
50