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Principles of Communication
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• The communication process: Sources of information, communication channels, modulation process, and communication networks • Representation of signals and systems: Signals, Continuous Fourier transform, Sampling theorem, sequences, z-transform, convolution and correlation. • Stochastic processes: Probability theory, random processes, power spectral density, Gaussian process. & • Modulation and encoding: %
' Basic modulation techniques and binary data transmission:AM, FM, Pulse Modulation, PCM, DPCM, Delta Modulation • Information theory: Information, entropy, source coding theorem, mutual information, channel coding theorem, channel capacity, rate-distortion theory. • Error control coding: linear bloc codes, cyclic codes, convolution codes &
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Course Material
1. Text: Simon Haykin, Communication systems, 4th edition, John Wiley & Sons, Inc (2001) 2. References (a) B.P. Lathi, Modern Digital and Analog Communcations Systems, Oxford University Press (1998) (b) Alan V. Oppenheim and Ronald W. Schafer, Discrete-Time signal processing, Prentice-Hall of India (1989) (c) Andrew Tanenbaum, Computer Networks, 3rd edition, Prentice Hall(1998). (d) Simon Haykin, ”Digital Communication Systems,” John Wiley & Sons, Inc. & %
' *Duration:* 14 Weeks
Course Schedule
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• Week 1:* Source of information; communication channels, modulation process and Communication Networks • Week 2-3:* Signals, Continuous Fourier transform, Sampling theorem • Week 4-5:* sequences, z-transform, convolution, correlation • Week 6:* Probability theory - basics of probability theory, random processes • Week 7:* Power spectral density, Gaussian process • Week 8:* Modulation: amplitude, phase and frequency • Week 9:* Encoding of binary data, NRZ, NRZI, Manchester, 4B/5B & %
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• Week 10:* Characteristics of a link, half-duplex, full-duplex, Time division multiplexing, frequency division multiplexing • Week 11:*
Principles of Communication
$
• The communication process: Sources of information, communication channels, modulation process, and communication networks • Representation of signals and systems: Signals, Continuous Fourier transform, Sampling theorem, sequences, z-transform, convolution and correlation. • Stochastic processes: Probability theory, random processes, power spectral density, Gaussian process. & • Modulation and encoding: %
' Basic modulation techniques and binary data transmission:AM, FM, Pulse Modulation, PCM, DPCM, Delta Modulation • Information theory: Information, entropy, source coding theorem, mutual information, channel coding theorem, channel capacity, rate-distortion theory. • Error control coding: linear bloc codes, cyclic codes, convolution codes &
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%
'
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Course Material
1. Text: Simon Haykin, Communication systems, 4th edition, John Wiley & Sons, Inc (2001) 2. References (a) B.P. Lathi, Modern Digital and Analog Communcations Systems, Oxford University Press (1998) (b) Alan V. Oppenheim and Ronald W. Schafer, Discrete-Time signal processing, Prentice-Hall of India (1989) (c) Andrew Tanenbaum, Computer Networks, 3rd edition, Prentice Hall(1998). (d) Simon Haykin, ”Digital Communication Systems,” John Wiley & Sons, Inc. & %
' *Duration:* 14 Weeks
Course Schedule
$
• Week 1:* Source of information; communication channels, modulation process and Communication Networks • Week 2-3:* Signals, Continuous Fourier transform, Sampling theorem • Week 4-5:* sequences, z-transform, convolution, correlation • Week 6:* Probability theory - basics of probability theory, random processes • Week 7:* Power spectral density, Gaussian process • Week 8:* Modulation: amplitude, phase and frequency • Week 9:* Encoding of binary data, NRZ, NRZI, Manchester, 4B/5B & %
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• Week 10:* Characteristics of a link, half-duplex, full-duplex, Time division multiplexing, frequency division multiplexing • Week 11:*