Principles of Digital Communication

Course Description

This course serves as an introduction to the theory and practice behind many of today's communications systems. 6.450 forms the first of a two-course sequence on digital communication. The second class, 6.451, is offered in the spring.

Topics covered include: digital communications at the block diagram level, data compression, Lempel-Ziv algorithm, scalar and vector quantization, sampling and aliasing, the Nyquist criterion, PAM and QAM modulation, signal constellations, finite-energy waveform spaces, detection, and modeling and system design for wireless communication.

Lectures

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   Lecture 1 - Introduction to digital communication

   Introduction: A layered view of digital communication

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   Lecture 2 - Discrete source encoding

   Discrete source encoding

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   Lecture 3 - Memory-less sources

   Memory-less sources, prefix free codes, and entropy

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   Lecture 4 - Entropy and asymptotic equipartition property

   Entropy and asymptotic equipartition property

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   Lecture 5 - Markov sources and Lempel-Ziv universal codes

   Markov sources and Lempel-Ziv universal codes

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   Lecture 6 - Quantization

   Quantization

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   Lecture 7 - High rate quantizers and waveform encoding

   High rate quantizers and waveform encoding

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   Lecture 8 - Fourier Series

   Measure, fourier series, and fourier transforms

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   Lecture 9 - Discrete-time fourier transforms

   Discrete-time fourier transforms and sampling theorem

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    Lecture 10 - Degrees of freedom

    Degrees of freedom, orthonormal expansions, and aliasing

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    Lecture 11 - Signal space

    Signal space, projection theorem, and modulation

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    Lecture 12 - Nyquist theory

    Nyquist theory, pulse amplitude modulation (PAM), quadrature amplitude modulation (QAM), and frequency translation

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    Lecture 13 - Random processes

    Random processes

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    Lecture 14 - Gaussian random vectors white Gaussian noise (WGN)

    Jointly Gaussian random vectors and processes and white Gaussian noise (WGN)

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    Lecture 15 - Linear functionals and filtering of random processes

    Linear functionals and filtering of random processes

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    Lecture 16 - Introduction to detection

    Review; introduction to detection

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    Lecture 17 - Detection for random vectors and processes

    Detection for random vectors and processes

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    Lecture 18 - Theorem of irrelevance

    Theorem of irrelevance, M-ary detection, and coding

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    Lecture 19 - Baseband detection and complex Gaussian processes

    Baseband detection and complex Gaussian processes

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    Lecture 20 - Introduction of wireless communication

    Introduction of wireless communication

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    Lecture 21 - Doppler spread

    Doppler spread, time spread, coherence time, and coherence frequency

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    Lecture 22 - Discrete-time baseband models for wireless channels

    Discrete-time baseband models for wireless channels

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    Lecture 23 - Rrayleigh fading and incoherent channels

    Detection for flat rayleigh fading and incoherent channels, and rake receivers

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    Lecture 24 - Code division multiple access (CDMA)

    Case study — code division multiple access (CDMA)