Course Objectives and Expected Learning Outcomes

This is the second course in a two-course series covering the principles of digital transmission of information. ECE 537 extends the concepts learned in ECE 535, and introduces five new topics: transmission over band-limited and ISI channels, constrained coding, equalization, and iterative decoding. We build analytical and simulation models for bandlimited systems in presence of noise, and define the performances of digital communication systems through a probability of reliable transmission of information.

The successful student will be able to:

1. Design, analyze analytically and through simulations a receiver for an inter-symbol interference channel.
2. Design and analyze a capacity approaching code for given channel constraints.
3. Analyze convergence of iterative inference, decoding and detection algorithms.

Relationship to Student Outcomes:

ECE 537 contributes directly to the following specific Electrical Engineering and Computer Engineering Student Outcomes of the ECE Department:

• an ability to apply knowledge of mathematics, science, and engineering (High)
• an ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability (High)
• an ability to identify, formulate, and solve engineering problems. (High)
• an ability to use the techniques, skills, and modern engineering tools necessary for engineering practice. (Medium)

Class Location and Times	Zoom https://arizona.zoom.us/j/6380981976 , with cameras on unless otherwise approved by the Instructor. Mo-We-Fr, 2-2:50PM
Instructor:	Bane Vasić, Professor Department of Electrical and Computer Engineering, 1230 East Speedway Boulevard, Room: 456P
Phone:	(520) 626-5550
Email:	vasic@ece.arizona.edu
Website:	http://www.ece.arizona.edu/~vasic
Office Hours	TBA, and by appointment.
Textbook:	None required
References:	BOOKS J. G. Proakis, Digital Communications, 4th Edition, McGraw-Hill, 2000. R. Blahut, Digital Transmission of Information, Addison-Wesley, 1990. E. Lee and D. Messerschmitt, Digital Communications, 2nd ed., Kluwer‐Academic, 1994. S. Lin and D. J. Costello, Jr., Error Control Coding: Fundamentals and Application, Prentice‐Hall, 1989. D. A. Lind and B. Marcus , An Introduction to Symbolic Dynamics and Coding, Cambridge Univ Press, 1995. D. Koller and N. Friedman, Probabilistic Graphical Models Principles and Techniques, MIT press, 2009. T. Richardson and R. Urbanke, Modern Coding Theory, Cambridge Univ Press, 2008. W. E. Ryan, and S. Lin, Channel Codes Classical And Modern, Cambridge University Press, 2009. JOURNAL PAPERS Sofic Systems B. Marcus, P. Siegel and J. K. Wolf, "Finite-state modulation codes for data storage," IEEE J. Select. Areas Commun., vol. 10, no. 1, pp. 5-37, January 1992. K. A. S. Immink, "Runlength-limited sequences," Proc. IEEE, vol. 78, pp. 1745-1759, Nov. 1990. Partial Response Channels Paul H. Siegel, Jack Keil Wolf, "Modulation and Coding for Information Storage", IEEE Communications Magazine, no. 12, December 1991 pp. 68-86. P. Kabal and S. Pasupathy, "Partial-response signaling," IEEE Trans. Commun., vol. COM-23, pp. 921-934, Sept. 1975. ISI Channel Detection L. R. Bahl, J. Cocke, F. Jelinek, and J. Raviv, "Optimal decoding of linear codes for minimizing symbol error rate," IEEE Trans. Inform. Theory, vol. IT-20, pp. 284-287, 1974. Codes on Graphs and Iterative Decoding F. R. Kschischang, B. J. Frey, and H.-A. Loeliger, "Factor graphs and the sum-product algorithm," IEEE Trans. on Inform. Theory, vol. 47, no. 2, pp. 498 -519, Feb. 2001. R. G. Gallager, Low-Density Parity-Check Codes. Cambridge, MA: MIT Press, 1963. R. M. Tanner, "A recursive approach to low complexity codes," IEEE Trans. Inform. Theory, vol. IT-27, pp. 533-547, Sept. 1981. Amin Shokrollahi, LDPC Codes: An Introduction, [Online Available]. J. Yedidia, W. T. Freeman, Y. Weiss, "Understanding belief propagation and its generalizations," [Online Available]
Credits:	ECE 537 is a three-unit, A-E based graduate course.
TA/Grader:	Xin Xiao (7xinxiao7@email.arizona.edu)

Prerequisites:	1. ECE 503 (Random Processes for Engineering Applications) 2. ECE 435A/535A (Digital Communication Systems I)
Participation:	The UA’s policy concerning Class Attendance, Participation, and Administrative Drops is available at: here. The UA policy regarding absences for any sincerely held religious belief, observance or practice will be accommodated where reasonable, here . Absences pre-approved by the UA Dean of Students (or Dean Designee) will be honored. See: here. Accommodations due to the current pandemic will follow guidelines found here. Capturing video, voice, screen, and any other content of lectures, discussions, students, participants, etc. in any form is not permitted
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 at most four medium size computer projects instead.
Exams:	There will be two mid-term exams.       midterm1 September 25       midterm2 November 13 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. Requests for incompletes (I) and withdrawal (W) must be made in accordance with University policies which are available at here and here respectively. The exams will be over zoom, using the same link as regular classes. The cameras must be turned on.
Computer Problems:	These will be integrated with your regular homework. Students may use C, C++ or Matlab.
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:       Percentage Grade       >90% A       >75% B       >60% C       >50% D The weights below will be used to determine your point total and your final grade:       Projects 30%       Midterms 40%       Final 30% Problems and corresponding number of points dedicated to graduate students will be marked on the question papers and in projects descriptions. The exams and project scores for graduate/undergraduate students will be calculated based on the total number of points on problems dedicated for graduate/undergraduate students.
Academic Integrity:	Students are encouraged to share intellectual views and discuss freely the principles and applications of course materials. However, graded work/exercises must be the product of independent effort unless otherwise instructed. Students are expected to adhere to the UA Code of Academic Integrity as described in the UA General Catalog. See: here. The University Libraries have some excellent tips for avoiding plagiarism available at: here. Selling class notes and/or other course materials to other students or to a third party for resale is not permitted without the instructor’s express written consent. Violations to this and other course rules are subject to the Code of Academic Integrity and may result in course sanctions. Additionally, students who use D2L or UA email to sell or buy these copyrighted materials are subject to Code of Conduct Violations for misuse of student email addresses. This conduct may also constitute copyright infringement.
UA Nondiscrimination and Anti-Harassment Policy	The University is committed to creating and maintaining an environment free of discrimination, here.
Subject to Change Statement:	Information contained in the course syllabus, other than the grade and absence policy, may be subject to change with advance notice, as deemed appropriate by the instructor.

Fundamentals of iterative decoding: Bayesian inference, Factor graphs, Message passing, Belief propagation, Linear programming decoder.
Partial response channels: Controlled ISI, Partial response equalization, Data detection for controlled ISI channels, Optimum maximum‐likelihood receiver.
Constrained (modulation) coding: Symbolic dynamics basics, Modulation codes for spectrum shaping, Shannon noiseless capacity, Sofic shifts of finite type, Sliding window decoders, State‐splitting algorithm.
Iterative receivers for ISI channels: Iterative decoding principles, Combined equalization and coding, BCJR algorithm, Message‐passing algorithm.