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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 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) |