麻省理工《数字通信原理》

MIT 6.450 Principles of Digital Communications

Lecture 1: Introduction to Digital Communication (PDF)

Lecture 2: Coding for Discrete Sources (PDF)

Lecture 3: Coding for Discrete Sources (continued) (PDF)

Lecture 4: Coding for Sequences of Source Symbols (PDF)

Lecture 5: Sources With Memory and the Lempel-Ziv Algorithm (PDF)

Lecture 6: Quantization (PDF)

Lecture 7: High-Rate Entropy-Coded Quantization (PDF)

Lecture 8-9: Analog Sources: Waveforms ↔ Sequences (PDF)

Lecture 10: Waveforms as Vectors in Signal-Space (PDF)

Lecture 11: Introduction to Channels and PAM (PDF)

Lecture 12: QAM (PDF)

Lecture 13: QAM and Noise (PDF)

Lecture 14: Noise and Gaussian Random Processes (PDF)

Lecture 15: Gaussian Noise, Covariance and Spectral Density (PDF)

Lecture 16: Spectral Density, Orthonormal Expansions (PDF)

Lecture 17-18: Detection (PDF)

Lecture 19: The Irrelevance Theorem and Orthogonal Signal Sets (PDF)

Lecture 20: Wireless Communication Systems (PDF)

Lecture 21: Input/Output Models for Wireless (PDF)

Lecture 22: Stochastic Wireless Models (PDF)

Lecture 23: Channel Measurement and Rake Receivers (PDF)

Lecture 24: Coding, IS-95, and CDMA (PDF)