2019 Wireless Signal Processing

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Academic unit or major
Graduate major in Information and Communications Engineering
Instructor(s)
Fukawa Kazuhiko 
Course component(s)
Lecture     
Day/Period(Room No.)
Tue3-4(S322)  Fri3-4(S322)  
Group
-
Course number
ICT.S407
Credits
2
Academic year
2019
Offered quarter
2Q
Syllabus updated
2019/5/9
Lecture notes updated
-
Language used
English
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Course description and aims

 The course focuses on applications of signal processing to wireless digital communications such as equalization, adaptive array antennas, and interference cancellation. In addition, adaptive algorithms, which estimate parameters of these items, are detailed.
 A major aim of the lecture is to help students gain a deep understanding of important transmission techniques for wireless digital communications.

Student learning outcomes

Major aims of this course are as follows:
1) To be able to deeply understand principles of equalization, adaptive array antennas, and interference cancellation as signal processing techniques.
2) To be able to deeply comprehend principles of adaptive algorithms that estimate parameters of the above items.

Keywords

Wireless Communications, Signal Models, Wireless Channels, Wiener Filters, LMS, RLS, Kalman Filters, Adaptive Equalizers, Frequency Domain Equalization, Diversity Techniques, Turbo Code, Turbo Equalization, Adaptive Array Antenna, MIMO, Space Time Coding, Precoding

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

After the instructor's explanations using handouts, students are required to solve problems.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction for Wireless Communications and Review of Fourier Transform, Laplace Transform and z Transform. Completely understand Fourier transform, Laplace transform and z transform.
Class 2 Signal Models for Wireless Communications Learn signal models for wireless communications.
Class 3 Wireless Channels and their Statistical Properties Learn wireless channels and their statistical properties.
Class 4 Wiener Filters and LMS Algorithm Understand principles of Wiener filters and the LMS algorithm.
Class 5 RLS Algorithm Completely understand the RLS algorithm.
Class 6 Kalman Filter Understand principles of Kalman filters.
Class 7 Adaptive Equalizer Understand principles of adaptive equalizers.
Class 8 Blind Equalization Understand principles of blind equalization.
Class 9 Frequency Domain Equalization Learn principles of frequency domain equalization.
Class 10 Turbo Code and Turbo Equalization Learn principles of Turbo codes and Turbo equalization.
Class 11 Diversity Techniques Learn principles of diversity techniques.
Class 12 Adaptive Array Antenna Techniques Learn basics of adaptive array antenna techniques.
Class 13 Nonlinear Interference Canceller Learn principles of nonlinear interference cancellers.
Class 14 MIMO Techniques Learn principles of MIMO techniques.
Class 15 Space Time Coding and Precoding for MIMO Channels Learn basics of both space time coding and precoding for MIMO channels.

Textbook(s)

Any text books are not specified. Documents for the classes are distributed.

Reference books, course materials, etc.

J. G. Proakis, Digital Communications, McGraw-Hill
S. Haykin, Adaptive Filter Theory, Prentice-Hall

Assessment criteria and methods

Marks are based on a terminal exam.

Related courses

  • ZUS.M303 : Digital Communications
  • ZUS.C301 : Signal Processing
  • ICT.C511 : Advanced Topics in Mobile Communications

Prerequisites (i.e., required knowledge, skills, courses, etc.)

To complete the lecture "Digital Communications" is recommended.

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