Course Objectives

In this graduate level introductory course, students will learn the basic principles involved in the analysis and design of digital communication systems. Basic mathematical tools such as probability theory, random processes, complex signals and systems theory, signal and vector space, Fourier analysis of signals and systems will be reviewed. Students will learn the following materials in this course: basic digital communication system, communication channel models, sampling theorem, information measure, entropy coding, AWGN channel capacity, M-ary modulation systems (MSK, PSK and FSK; CPFSK, MSK), symbol error probability, inter symbol interference, precoding, spectrum shaping, optimal receiver structure, correlator/matched filter, MAP, ML, MD, MLSD detectors, Nyquist pulse design, performance characterization and analysis of digital communication systems. Prerequisites An undergraduate course in digital communication with emphasis on probability and random processes and signal analysis in time/frequency domain. Course Outline Topics covered include: 1. Review of Fundamental Concepts: introduction to digital communication, probability theory, random processes, autocorrelation and power spectrum density, complex signals and systems, signal space representation, Fourier analysis of signals and systems (Ch. 1.1-1.3, 2.1-2.2, 4.1-4.2) [3 weeks] 2. Source Entropy and Channel Capacity: entropy, source coding, rate distortion, channel capacity and coding (Ch. 3.1-3.4, 7.1-7.2) [2 weeks