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Principles of Communication


• The communication process: Sources of data, communication programs, modulation process, and communication networks • Representation of signals and systems: Alerts, Continuous Fourier transform, Sampling theorem, sequences, z-transform, convolution and relationship. • Stochastic processes: Likelihood theory, random processes, electrical power spectral density, Gaussian method. & • Modulation and encoding: %

' Fundamental modulation approaches and binary data tranny: AM, FM, Pulse Modulation, PCM, DPCM, Delta Modulation • Info theory: Info, entropy, source coding theorem, mutual info, channel coding theorem, route capacity, rate-distortion theory. • Error control coding: linear bloc rules, cyclic unique codes, convolution requirements &





Course Material

1 . Text message: Simon Haykin, Communication systems, 4th model, John Wiley & Kids, Inc (2001) 2 . Referrals (a) W. P. Lathi, Modern Digital and Analog Communcations Devices, Oxford School Press (1998) (b) Alan V. Oppenheim and Ronald W. Schafer, Discrete-Time signal processing, Prentice-Hall of India (1989) (c) Andrew Tanenbaum, Computer Sites, 3rd release, Prentice Hall(1998). (d) Bob Haykin, ”Digital Communication Systems, ” Ruben Wiley & Sons, Incorporation. & %

' *Duration: * 14 Weeks

Course Schedule


• Week 1: 2. Source of information; communication channels, modulation process and Interaction Networks • Week 2 - 3: * Signals, Continuous Fourier transform, Sample theorem • Week 4-5: * sequences, z-transform, convolution, correlation • Week 6th: * Possibility theory - basics of probability theory, random processes • Week 7: 2. Power unreal density, Gaussian process • Week almost 8: * Modulation: amplitude, phase and regularity • Week 9: 5. Encoding of binary info, NRZ, NRZI, Manchester, 4B/5B & %



• Week 10: 2. Characteristics of a link, half-duplex, full-duplex, Period division multiplexing, frequency section multiplexing • Week 14: *...



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