Course Syllabus - Spring 2016
This is the first graduate course covering the principles of digital transmission of information. ECE 535 introduces a notion of information. We rigorously define the amount of information, introduce information measures and explain how to extract a pure information from a source and how to protect information during transmission. The largest portion of the course is devoted to studying how to translate information into a digital signal to be transmitted, and how to retrieve the information back from the received signal. We study in depth various digital modulation schemes through a concept of signal space. We build analytical and simulation models for digital modulation systems in presence of noise, and define the performances of digital communication systems through a probability of reliable transmission of information. We also build optimal receiver models for digital base-band and band-pass modulation schemes.
The successful student will be able to:
- understand and compute Shannon capacity of various communications, channels.
- write a software and analyze source coding algorithms such as: Huffman, arithmetic and Ziv-Lempel coding, and channel coding schemes as convolutional codes and linear block codes.
- rigorously analyze and develop simulation models for coded digital communications systems, such as PSK, ASK, QAM etc…
- design optimal detectors in presence of AWGN.
Class Information
| Instructor: | Dr. Bane Vasić |
| Phone: | (520) 626-5550 |
| Email: | vasic@ece.arizona.edu |
| Office Hours: | TBA, and by appointment. |
| Textbook: | None required |
| References: | J. G. Proakis, Digital Communications, 4th Edition, McGraw-Hill, 2000. S. G. Wilson, Digital Modulation and Coding, Prentice-Hall, 1995. R. Blahut, Digital Transmission of Information, Addison-Wesley, 1990. J. Wozencraft and I. Jacobs, Principles of Communication Engineering, Wiley, 1965 S. Haykin, Introduction to Communication Systems, 4rd ed., Wiley, 2000. |
| Credits: | ECE 535 is a three-unit, A-E based graduate course. |
| TA/Grader: | Mohsen Bahrami (bahrami@email.arizona.edu) |
Administrative Details and Policies
| Prerequisites: | 1. ECE 340 (Engineering Systems Analysis) (signal characterization in frequency domain, Fourier transform, discrete-time systems) 2. ECE 529 (Digital Signal Processing) 3. ECE 503 (Random Processes for Engineering Applications) | ||||||||||||||||
| Attendance: | Optional, but recommended. | ||||||||||||||||
| Punctuality: | Entering the classroom after the instructor is strongly discouraged! | ||||||||||||||||
| Participation: | Students are encouraged to take part in general class discussions. | ||||||||||||||||
| Student Questions: | The instructor will not be able to answer questions submitted by e-mail or phone, nor to accept student visits out of the office hours. | ||||||||||||||||
| Projects and Homework: | There will be no homework in this class, i.e., homework if assigned will be graded as a reference to the final grade. Solved problems will be posted on the instructor’s web page. There will be couple of medium size computer projects instead. | ||||||||||||||||
| Exams: | There will be two mid-term exams, one final written examination. The final exam schedule can be found here. Exams may include material/topics not contained in the text, but which are discussed in class. The final exam is mandatory. | ||||||||||||||||
| Computer problems: | These will be integrated with your regular homework. Students may use any convenient math software. | ||||||||||||||||
| Grading policy: | Graded work includes exams and projects. Final grades will be determined by your total number of points compared to an absolute scale. The course grade will be percentage based and I guarantee the following minimum cutoffs for grades:
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| Academic Integrity: | All submitted work must be original. The minimum penalty for plagiarism and cheating on exams and quizzes is an E grade of failing. |
Course Outline
Review of mathematical tools: Orthogonal functions, probability theory, random processes, Markov processes.
Information theory: Information measures (self information, mutual information, channel capacity), looseless source coding, Huffman codes, channel coding, Shannon coding theorems.
Representation of band-pass signals and systems: Band-pass signals and noise representation (Hilbert transform); signal space representation.
Digital modulation schemes: Memoryless digital modulation methods (ASK, PSK, FSK, QPSK), modulation with memory (base-band and band-pass), spectra of digitally modulated signals.
Optimum receivers for additive white Gaussian noise (AWGN) channel: Maximum a posteriori and maximum likelihood detection, matched filter demodulation, sequence detectors, symbol by symbol MAP detector for channels with memory, receiver performance.
Error control coding fundamentals: Finite fields, generator and parity check matrices block and convolutional codes, Hamming codes, syndrome decoding.
Codes on graphs and iterative decoding: Probabilistic graphical models, exact and approximate inference, belief propagation, low-density parity check (LDPC) codes, iterative decoding, linear programming decoding.
Viterbi algorithm.
Gallager algorithms for decoding of LDPC codes.
Belief propagation.
LDPC code construction.