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School of Engineering · ANU College of Systems and Society
This course presents the principles and techniques fundamental to the analysis and design of digital communication systems. It focuses on the basic building blocks of a digital communication system (source encoder/decoder, channel encoder/decoder, digital modulator/demodulator and channel characteristics). The emphasis is on mathematical underpinnings of communications theory along with practical applications and emerging machine learning tools. Specific topics include: • Review of Probability and Random Processes and Modelling of Gaussian noise process. • Digital Modulation Techniques: Signal space analysis, BPSK, QPSK, QAM. • Digital Demodulation & Detection Techniques: Correlator-demodulator, Maximum likelihood detection (MLD) in additive white Gaussian noise (AWGN), bit error rate (BER) performance. • Channel Encoder/Decoder: Linear block codes, Cyclic codes, Convolutional codes, Viterbi algorithm. • Information Theory and Source Coding: Source Entropy, Huffman Coding, Channel Capacity. Matlab and/or Python-based programming environment is used in the analysis and design.
Assignments
Learning outcomes 1, 2, 3, 4, 5, 6
Labs
Learning outcomes 1, 2, 3, 5, 6, 7
Project
Learning outcomes 2, 6, 7
Final exam
Learning outcomes 1, 2, 3, 4, 5
10 hours per week is expected. Approximately 33 hours of lecture content, seven computer and project-based labs and drop-in sessions (each 2 - 2.5 hours), six tutorials (each 1.5 hours), plus adequate self-study, drop-in discussions, assignments, reports and exam preparation.
Not applicable Preliminary Reading Recommended reading: - Proakis, J.G. & Salehi, M., Communications Systems Engineering, 2nd edition, Pearson/Prentice Hall, 2002 - H. Nguyen and E. Shwedyk, A first course in digital communications, Cambridge University press, 2009