2021 Wireless Signal Processing

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Academic unit or major
Graduate major in Information and Communications Engineering
Fukawa Kazuhiko 
Course component(s)
Lecture    (ZOOM)
Day/Period(Room No.)
Tue3-4()  Fri3-4()  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
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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.


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 and basics of space time coding.

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.


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.
Note that when the online lecture is held, the terminal exam can be replaced by several reports.

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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