ARTIFICIAL INTELLIGENCE ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
EEE5601	Digital Communication	Spring	3	0	3	12

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Non-Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Assoc. Prof. SAEID KARAMZADEH
Recommended Optional Program Components:	None
Course Objectives:	AWGN kanal için bazı modülasyon/demodülasyon tekniklerini, temel sezim kuramını ve performans analizinin metodlarını detaylarıyla anlamak.

Learning Outcomes

The students who have succeeded in this course;
1. Describe digital communications,

2. Explain signal space representation,

3. Describe digital modulation schemes,

4. Gain knowledge noise calculations,

5. Understand single-user detection theory.

Course Content

This course starts with reviewing concepts of sampling, quantization and encoding. Then, it moves to source and channel coding, signal space representation, and digital modulation schemes. Upon visiting digital demodulation schemes, performance analysis of different schemes are carried out. In the second half of the course, basic estimation and detection techniques are introduced. Finally, the course ends with fading channel analysis.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	General model for a digital communication system
2)	Source and channel coding
3)	Signal Space Representation
4)	Digital modulation schemes, M-QAM
5)	Performance considerations, Bandwidth considerations, Practical considerations
6)	(Phase) noncoherent detection principles
7)	Differential detection, System constraints and trade-offs
8)	Comparison and discussion of previously mentioned methods. Midterm exam.
9)	General Concepts of Detection Theory, Bayesian Decision Theory
10)	The Likelihood Ratio Test and Its applications
11)	Optimal binary detection for the Gaussian vector channel
12)	Optimal detection for M-ary hypothesis tests
13)	BER calculations
14)	Introduction to fading channels

Sources

Course Notes / Textbooks:	Proakis, Digital Communications, Fourth Edition, McGraw Hill
References:

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Project	1	% 30
Midterms	1	% 30
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 30
PERCENTAGE OF FINAL WORK		% 70
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Workload
Course Hours	14	42
Project	4	50
Midterms	9	60
Final	4	48
Total Workload		200

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Have sufficient background in mathematics, science and artificial intelligence engineering.
2)	Use theoretical and applied knowledge in the fields of mathematics, science and artificial intelligence engineering together for engineering solutions.
3)	Identify, define, formulate and solve engineering problems, select and apply appropriate analytical methods and modeling techniques for this purpose.
4)	Analyse a system, system component or process and design it under realistic constraints to meet desired requirements; apply modern design methods in this direction.
5)	Select and use modern techniques and tools necessary for engineering applications.
6)	Design and conduct experiments, collect data, and analyse and interpret results.
7)	Work effectively both as an individual and as a multi-disciplinary team member.
8)	Access information via conducting literature research, using databases and other resources
9)	Follow the developments in science and technology and constantly update themself with an awareness of the necessity of lifelong learning.
10)	Use information and communication technologies together with computer software with at least the European Computer License Advanced Level required by their field.
11)	Communicate effectively, both verbal and written; know a foreign language at least at the European Language Portfolio B1 General Level.
12)	Have an awareness of the universal and social impacts of engineering solutions and applications; know about entrepreneurship and innovation; and have an awareness of the problems of the age.
13)	Have a sense of professional and ethical responsibility.
14)	Have an awareness of project management, workplace practices, employee health, environment and work safety; know the legal consequences of engineering practices.