Lecture 1: Introduction

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Lecture 2: Discrete Source Encoding

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Lecture 3: Memory-less Sources

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Lecture 4: Entropy and Asymptotic Equipartition Property

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Lecture 5: Markov Sources

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Lecture 6: Quantization

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Lecture 7: High Rate Quantizers and Waveform Encoding

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Lecture 8: Measure

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Lecture 9: Discrete-Time Fourier Transforms

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Lecture 10: Degrees of Freedom

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Lecture 11: Signal Space

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Lecture 12: Nyquist Theory

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Lecture 13: Random Processes

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Lecture 14: Jointly Gaussian Random Vectors

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Lecture 15: Linear Functionals

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Lecture 16: Review; Introduction to Detection

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Lecture 17: Detection for Random Vectors and Processes

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Lecture 18: Theory of Irrelevance

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Lecture 19: Baseband Detection

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Lecture 20: Introduction of Wireless Communication

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Lecture 21: Doppler Spread

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Lecture 22: Discrete-Time Baseband Models for Wireless Channels

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Lecture 23: Detection for Flat Rayleigh Fading and Incoherent Channels

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Lecture 24: Case Study on Code Division Multiple Access

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