课程大纲

COURSE SYLLABUS

1.

课程代码/名称

Course Code/Title

现代信号处理

Modern signal processing

2.

课程性质

Compulsory/Elective

专业核心课

3.

课程学分/学时

Course Credit/Hours

3/48

4.

授课语言

Teaching Language

英文

5.

授课教师

Instructor(s)

唐晓颖

6.

是否面向本科生开放

Open to undergraduates

or not

否

7.

先修要求

Pre-requisites

（ 如 面 向 本 科 生 开 放 ， 请 注 明 区 分 内 容 。 If the course is open to

undergraduates, please indicate the difference.）

研究生先修要求：Digital Signal Processing，Random Processes

8.

教学目标

Course Objectives

（如 面 向 本 科 生开 放 ， 请 注 明 区 分 内容 。 If the course is open to undergraduates, please indicate the

difference.）

Students are expected to be able to :

1. Determine if a minimum variance unbiased estimator (MVUE) exists & compute it

2. Determine if an efficient estimator exists and compute it

3. Compute the Cramer-Rao lower bound for scalar and vector cases

4. Compute MVUE for linear models

5. Compute maximum likelihood estimator

6. Compute the linear least-squares estimator

7. Compute various Bayesian estimators including the minimum mean square error estimator and

the maximum aposteriori estimator

8. Apply theory and estimation algorithms learned in class to real-world examples

9.

教学方法

Teaching Methods

（如面向本科生 开放， 请注 明区分 内容。 If the course is open to undergraduates, please indicate the

difference.）

Considering that I have many years of industry experience in designing adaptive equalizers and

carrier and symbol timing recovery tracking algorithms at the receiver, and building the nonlinear

polynomial model (or Volterra series) of the power amplifiers at the transmitter, I present a proposal to

this course by adding some practical algorithms that are used and developed in modern digital

communications, especially in Chapters 7 & 10 of maximum likelihood and least squares estimations.

The purpose for adding these practical algorithms is to tell students why and how the ML and LS

estimation methods are used in modern digital communication systems in order to enhance their interest

in this course. The contents of the proposal for these two Chapters are described as follows:

1.Maximum Likelihood (ML) Estimation (Ch.7)

A typical application of ML estimation is the optimum detector in digital communications. A new

teaching method that I would plan in this chapter is to apply the ML estimation to the optimum detector

at the receiver of digital communication systems.

The maximum of likelihood function over the transmitted nth signal symbol is equivalent to

minimize the Euclidean distance when the transmitted M signal symbols are equally probably a priori

probability. Euclidean means to find one of the transmitted signal symbols, which is closest in distance to

the currently received signal symbols. Furthermore, the minimum of the Euclidean distance is equivalent

to the largest correlation metric at the receiver, called the correlation based optimum receiver that is

widely used in digital communications.

Next content would be added in this Chapter is carrier frequency offset (CFO) estimation in OFDM

systems by using ML estimation based on training symbols in the received frame. This ML based

estimation is widely used in the IEEE 802.11 Wi-Fi OFDM signal reception to achieve the fast and

accurate CFO estimation.

2.Least Squares (LS) Estimation (Ch. 8)

Besides teaching the content required in this topic, I also plan to add one practical design example

by using the LS estimation to solve the practical problem of the nonlinear behavioral modelling for the

power amplifier. This practical example is to extract the coefficients of the Volterra series that is used to

approximate to the power amplifier’s characteristics from the measured (or collected) data at the input

and output of the power amplifier. This behavioral model is widely used for linearizing power amplifier

in order to compensate for the power amplifier’s nonlinear property by inserting a predistorter prior to

the power amplifier. Thus, students could obtain the latest knowledge how to model a nonlinear device in

practice by using LS algorithm. Actually, Chapter 5 of my book describes the LS algorithm in more

detail.

Note: Teaching new contents above in these two Chapters will be limited within the required time

period or the total of 3 weeks if extra time is not allowed.

10.

教学内容

Course Contents

（如面向本科生开放，请注明区分内容。 If the course is open to undergraduates, please indicate the

difference.）

Section 1

Minimum Variance Unbiased Estimation (MVU) (Ch.2) : ~ 1 week

Section 2

Cramer-Rao Lower Bound (scalar & vector cases) (Ch.3) : ~ 2 weeks

Section 3

Linear Models (Ch.4) : ~ 1 week

Section 4

Sufficient Statistics, Neyman-Fisher Factorization Theorem (Ch.5) : ~1

week

Section 5

Sufficient Statistics, Neyman-Fisher Factorization Theorem (Ch.5) : ~1

week

Section 6

Maximum Likelihood Estimation (Ch.7) : ~ 2 weeks

Section 7

Least Squares Estimation

–

Geometrical Interpretations (Ch.8) : ~ 1

week

Section 8

Bayesian Estimation (Ch.10) : ~ 1 week

Section 9

Maximum Aposteriori (MAP) Estimation (Ch.11) : ~ 1 week

Section 10

Linear Minimum Mean Square Error (LMMSE) Estimation (Ch.12) : ~ 2

weeks

Section 11

Overview of Detection Techniques : ~ 1 week

11.

课程考核

Course Assessment

（

○

1

考核形式 Form of examination；

○

2

.分数构成 grading policy；

○

3

如面向本科生开放，请注明区分内容。

If the course is open to undergraduates, please indicate the difference.）

20% Homework

20% Midterm

20% Project

40% Final Exam

12.

教材及其它参考资料

Textbook and Supplementary Readings

Fundamentals of Statistical Signal Processing : Estimation Theory by Steven Kay. Volume I, Prentice Hall,

1993

I suggest one another reference textbook to this course, which was revised a few years ago.

“Detection, Estimation, and Modulation Theory”, Part 1- detection, estimation, and filtering theory. Harry

L. Van Trees, Wiley, 2013