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)