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

• Introduction: A layered view of digital communication (M-I-T)
• Discrete source encoding (M-I-T)
• Memory-less sources, prefix free codes, and entropy (M-I-T)
• Entropy and asymptotic equipartition property (M-I-T)
• Markov sources and Lempel-Ziv universal codes (M-I-T)
• Quantization (M-I-T)
• High rate quantizers and waveform encoding (M-I-T)
• Measure, fourier series, and fourier transforms (M-I-T)
• Discrete-time fourier transforms and sampling theorem (M-I-T)
• Degrees of freedom, orthonormal expansions, and aliasing (M-I-T)
• Signal space, projection theorem, and modulation (M-I-T)
• Nyquist theory, pulse amplitude modulation (PAM), quadrature amplitude modulation (QAM), and frequency translation (M-I-T)
• Random processes (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)
• Review: introduction to detection (M-I-T)
• Detection for random vectors and processes (M-I-T)
• Theorem of irrelevance, M-ary detection, and coding (M-I-T)
• Baseband detection and complex Gaussian processes (M-I-T)
• Introduction of wireless communication (M-I-T)
• Doppler spread, time spread, coherence time, and coherence frequency (M-I-T)
• Discrete-time baseband models for wireless channels (M-I-T)
• Case study — code division multiple access (CDMA) (M-I-T)