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Home » Applied Sciences » Engineering » Principles of Digital Communications I (M-I-T) » Discrete source encoding (M-I-T)

Discrete source encoding (M-I-T)

Course:Principles of Digital Communications I (M-I-T)
Discipline:Applied Sciences
Institute:MIT
Instructor(s): Prof. Dr. Lizhong Zheng, Prof. Dr. Robert Gallager
Level:Graduate

Principles of Digital Communications I (M-I-T)

Baseband detection and complex Gaussian processes (M-I-T)
Case study — code division multiple access (CDMA) (M-I-T)
Degrees of freedom, orthonormal expansions, and aliasing (M-I-T)
Detection for flat rayleigh fading and incoherent channels, and rake receivers (M-I-T)
Detection for random vectors and processes (M-I-T)
Discrete source encoding (M-I-T)
Discrete-time baseband models for wireless channels (M-I-T)
Discrete-time fourier transforms and sampling theorem (M-I-T)
Doppler spread, time spread, coherence time, and coherence frequency (M-I-T)
Entropy and asymptotic equipartition property (M-I-T)
High rate quantizers and waveform encoding (M-I-T)
Introduction of wireless communication (M-I-T)
Introduction: A layered view of digital communication (M-I-T)
Jointly Gaussian random vectors and processes and white Gaussian noise (WGN) (M-I-T)
Linear functionals and filtering of random processes (M-I-T)
Markov sources and Lempel-Ziv universal codes (M-I-T)
Measure, fourier series, and fourier transforms (M-I-T)
Memory-less sources, prefix free codes, and entropy (M-I-T)
Nyquist theory, pulse amplitude modulation (PAM), quadrature amplitude modulation (QAM), and frequency translation (M-I-T)
Quantization (M-I-T)

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