Signalprocessing’ 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 & $ % ‘ $ Course Material 1. Text: Simon Haykin, Communication systems, 4th edition, John Wiley & Sons, Inc (2001) 2.
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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 & % ‘ $ Week 10:* Characteristics of a link, half-duplex, full-duplex, Time division multiplexing, frequency division multiplexing • Week 11:* Information, entropy, source coding theorem, mutual information • Week 12:* channel coding theorem, channel capacity, rate-distortion theory • Week 13:* Coding: linear block codes, cyclic codes, convolution codes • Week 14:* Revision & % ‘ Overview of the Course $ Target Audience: Computer Science Undergraduates who have not taken any course on Communication • Communication between a source and a destination requires a channel. A signal (voice/video/facsimile) is transmitted on a channel: Basics of Signals and Systems – This requires a basic understanding
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of signals ? Representation of signals – Each signal transmitted is characterised by power. – The power required by a signal is best understood by frequency characteristics or bandwidth of the signal: & ? Representation of the signal in the frequency domain Continuous Fourier transform % ‘ – A signal trasmitted can be either analog or digital ?
A signal is converted to a digital signal by ? st discretising the signal – Sampling theorem – Discrete-time Fourier transform ? Frequency domain interpretation of the signal is easier in terms of the Z-transform ? Signals are modi? ed by Communication media, the communication media are characterised as Systems ? The output to input relationship is characterised by a Transfer Function $ • Signal in communcation are characterised by Random variables – Basics of Probability – Random Variables and Random Processes – Expectation, Autocorrelation, Autocovariance, Power Spectral Density & % ‘ Analog Modulation Schemes – AM, DSB-SC, SSB-SC, VSB-SC, SSB+C, VSB+C – Frequency Division Muliplexing – Power required in each of the above $ • Digital Modulation Schemes – PAM, PPM, PDM (just mention last two) – Quantisation – PCM, DPCM, DM – Encoding of bits: NRZ, NRZI, Manchester – Power required for each of the encoding schemes • Information Theory – Uncertainty, Entropy, Information – Mutual information, Di? erential entropy – Shannon’s source and channel coding theorems & % ‘ $ – Shannon’s information capacity theorem – Analysis of Gaussian channels • Coding – Repetition code – Hamming codes – Error detection codes: CRC & %