Course Objectives and Expected Learning Outcomes

The purpose of the course is to give students a comprehensive introduction to digital communication principles. The major part 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 in the presence of noise and intersymbol interference (ISI). Various digital modulation schemes are discussed through the concept of signal space. Analytical and simulation models for digital modulation systems are designed and implemented in the presence of noise and ISI. Optimal receiver models for digital base-band and band-pass modulation schemes are covered in detail. Basic concepts of channel (error correction) codes and decoding algorithms will be studied. May be convened with ECE 535A.

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 435A/535A 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)

Prerequisites:	1. ECE 340 (Engineering Systems Analysis) (signal characterization in frequency domain, Fourier transform, discrete-time systems) 2. ECE 503 (Random Processes for Engineering Applications) or any introductory course on probability is not a strict prerequisite, but a desired background for the course
Participation:	Class Attendance, Participation, and Administrative Drops shall be in accordance with the UA’s policy. The UA policy regarding absences for any sincerely held religious belief, observance or practice will be accommodated where reasonable. Absences pre-approved by the UA Dean of Students (or Dean Designee) will be honored.
Student Questions:	The instructor encourages student visits during the office hours (TBA in class).
Projects and Homework	There will be no graded homework for this class, i.e., homework problem set and the solution will be posted on the instructor’s course web page periodically, but will not be considered towards the final grade. There will be at most four medium size computer projects instead.
Exams:	There will be two mid-term exams.       midterm1 March 2       midterm2 April 6 The final exam schedule follows the schedule as in UA Office of Registrar website. 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.
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. Following is the relative 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. The University Libraries have some excellent tips for avoiding plagiarism. 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.
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.

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 and their decoders, Hamming codes, syndrome decoding, iterative decoders.