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City University of Hong Kong · Course

EE6617Detection and Estimation - Theory and Applications in Communications

2026/27 · Postgraduate

Course code
EE6617
Credit units
3
Academic unit
Department of Electrical Engineering
Readable study materials
20 items

Course overview and requirements

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2026/27 · Official catalogue edition

The effective term printed in this PDF is Semester B 2024/25。

This outline is still being checked. Before registering, confirm entry and assessment requirements against the official document linked below.

Offering details for 2026/27 have not been checked. Confirm available sections in AIMS before registering.

The PDF Effective Term is separate from the 2026/27 catalogue year and current offering observation.

What you will study

Course overview
The course provides students with principles in three areas of Estimation and Detection: - Estimation and Detection theory - Statistical signal processing and optimization - Applications in Communications.

Official PDF, page 2 · Abstract

Course content
Fundamentals Vector spaces, linear subspaces, random variables, probability density function, cumulative distribution function, statistical signal representation. Detection and estimation theory Hypothesis testing, Neyman-Pearson criterion, minimum probability error detector, non-coherent detection, parameter estimation, minimum variance estimation, Cramer-Rao bound, maximum likelihood estimation, least squares, Bayesian estimator Applications to communications and signal processing The mini-projects are designed to supplement the lecture aspects of the course, and will provide practical learning experience on how various detection and estimation techniques are applied to design communication and signal processing systems.

Official PDF, pages 5–6 · Keyword Syllabus

Intended learning outcomes
View official learning outcomes and conditions
CILOs                                                 Weighting (if DEC-A1       DEC-A2          DEC-A3
                                                                     app.)
1              Engage in exploring and discussing the general                       x           x
               frameworks in detection and estimation through
               interactive learning activities, enhancing
               conceptual understanding.
2              Collaboratively identify detection and                               x           x
               estimation problems, applying mathematical
               formulations, statistical signal processing skills,
               and optimization tools in group problem-solving
               scenarios.
3              Apply detection and estimation techniques                            x           x               x
               to practical problems in communications
               and signal processing through hands-on
               applications, fostering critical thinking and
               practical skills.

Official PDF, page 2 · Course Intended Learning Outcomes (CILOs)

Before you take this course

Check prerequisites, precursors and corequisites separately, together with equivalence and exclusion conditions. “and” means conditions must be met together; “or” marks alternatives. Read combined conditions in the groupings shown in the original text below.

Prerequisites
Nil

Official PDF, page 1 · Prerequisites

Precursors
EE3210 Signals and Systems; or EE3008 Principles of Communications; or Courses in Signal Processing and Communications

Official PDF, page 1 · Precursors

Corequisites
This edition of the official syllabus does not state this item.
Additional course requirements
This edition of the official syllabus does not state this item.
Equivalent courses and conditions
Nil

Official PDF, page 1 · Equivalent Courses

Exclusive courses and conditions
Nil

Official PDF, page 1 · Exclusive Courses

Assessment and passing the course

Assessment
Continuous Assessment (%): 50; Examination (%): 50; Examination Duration (Hours): 2
View the original assessment table and instructions
ATs                          CILO No.                     Weighting (%)   Remarks (e.g. Parameter
                                                                                         for GenAI use)
1              Tests (min.: 2)              1, 2, 3                      30
2              #Assignments (min.: 3)       1, 2, 3                      20

Official PDF, page 3 · Continuous Assessment (%),Examination (%),Examination Duration (Hours)

Passing requirements
Remark: To pass the course, students are required to achieve at least 30% in course work and 30% in the examination. # may include homework, tutorial exercise, project/mini-project, presentation

Official PDF, page 4 · Additional Information for ATs

Credits, duration and edition details
Title in this edition
Detection and Estimation - Theory and Applications in Communications

Official PDF, page 1 · Course Title

Credits in this edition
3

Official PDF, page 1 · Credit Units

Course duration
One Semester

Official PDF, page 1 · Course Duration

Syllabus effective term
Semester B 2024/25
Levels printed in the syllabus
P5, P6 - Postgraduate Degree

Official PDF, page 1 · Level

Check the official document

Read or download this edition through the university link below. EE6617 · Open the original university PDF ↗

Source · University course information ↗ · Checked 2026-10-20T13:47:01.004Z

Study materials

20 readable items

Browse by the material’s own year and type. Historical materials are study references.

20 / 20 items
  • 01 MVU

    Lecture slides · Year unknown Term unknown · Local review material · 193 KB

    Material type follows its original classification.

  • 02 LMVU

    Lecture slides · Year unknown Term unknown · Local review material · 259 KB

    Material type follows its original classification.

  • 03 GMVU_BLUE

    Lecture slides · Year unknown Term unknown · Local review material · 241 KB

    Material type follows its original classification.

  • 05 LS

    Lecture slides · Year unknown Term unknown · Local review material · 775 KB

    Material type follows its original classification.

  • 06 Bayesian

    Lecture slides · Year unknown Term unknown · Local review material · 251 KB

    Material type follows its original classification.

  • 07 Detection Theory 1

    Lecture slides · Year unknown Term unknown · Local review material · 394 KB

    Material type follows its original classification.

  • 08 Detection Theory 2

    Lecture slides · Year unknown Term unknown · Local review material · 412 KB

    Material type follows its original classification.

  • 09 Composit Hypothesis Testing

    Quizzes · Year unknown Term unknown · Local review material · 248 KB

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  • Assignment 1_sol

    Assignments · Year unknown Term unknown · Local review material · 192 KB

  • Assignment 2_sol

    Assignments · Year unknown Term unknown · Local review material · 192 KB

  • chapter 1 Review of linear algebra

    Lecture slides · Year unknown Term unknown · Local review material · 87 KB

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  • chapter 2 Review of random signals

    Lecture slides · Year unknown Term unknown · Local review material · 46 KB

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  • Tutorial1__内容不同_f4ac43d6c8

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    Tutorials · Year unknown Term unknown · Local review material · 119 KB

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    Tutorials · Year unknown Term unknown · Local review material · 133 KB

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