• Lecture （授课）
  

• Experiments （实验）

• Projects （课程小项目）

Required Textbook:

1. 李晓峰、李在铭、周宁、傅志中. 随机信号分析（第四版）. 电子工业出版社，北京。

2. Lathi. Modern digital and analog communicationsystems. (4^th ) chapters 8 and 9. (We will follow closely the structure of the two chapters in this classical textbook, but will also add more materials when needed.)
   

Reference Textbooks:

1. S. Miller, D. G. Childers. Probability and Random Processes, with Applications to Signal Processing and Communications. Elsevier, 2004.

2. Steven Kay. Fundamentals of Statistical Signal Processing, vol 1: Estimation Theory, Vol 2: Detection Theory.

3. Steven Kay. Intuitive Probability and Random Processes using MATLAB. Springer, 2006.

4. Robert M. Gray and Lee D. Davisson. An Introduction to Statistical Signal Processing. (1st Edition), 2010, Cambridge University Press.

5. P. Peebles. Probability, Random Variables, and Random Signal Principles. (4th edition). 2000, McGraw-Hill.

6. J. A. Gubner. Probability and Random Processes for Electrical and Computer Engineers. 2006, Cambridge University Press.

7. W. Woyczynski. A First Course in Statistics for Signal Analysis. 2011, Birkhäuser. 

8. Stanford EE 278: Introduction to statistical signal processing. URL: http://web.stanford.edu/class/ee278/

9. University of California Santa Cruz. Statistical signal processing. URL： https://itunes.apple.com/us/itunes-u/statistical-signal-processing/id547397278?mt=10

10. Rice University. ELEC531, Statistical signal processing. Video lectures: https://mediacosmos.rice.edu/Playlist/Be5b8R7E

Lectures
                                                                                                                                               

Aim and Purpose

"Random Signal Processing (RSP)” is a fundamental course for the major Electronic and Communication. This lab is an inseparable part of this course.

As a fundamental course for students majored in communication and electronics, RSP deals with modelling of random signals, parameter estimation, signal estimation and signal detection problems. All these problems have strong engineering background and areextracted from realistic engineering applications.

Through study of this course and related labs, students should be able to get a basic understanding of information extraction from random signals. Furthermore, they will also get familiar with recent advances in communication and signal processing. Through the “learning via practicing” approach, we anticipate that our students can get the basic training needed for their future engineering work, being rigorous and also innovative.

 A series of experiments are designed in this lab, coordinating closely with the discussion of theoretical topics in class. In addition, we also considered the course design and latter courses when designing our experiments. These experiments will help our students to digest what they have learned in class; they will help students to think creatively by doing some innovation-based projects. Realization of signal estimation and detection is extensively used in this lab, which is proved to be very useful for practical works.

In addition to Matlab based experiments,some experiments are designed to use the hardware platform in signal processing lab. This lab will lay a solid foundation for later courses, including course project and practices in multimedia communication and network security.

List of experiments

We need totally 8 hours on these experiments, including4 hours of basic experiments and 4 hours of more complicated and comprehensive experiments.

There are also two innovation-based experiments which are optional. A detailed introduction to all experiments is listed in the following table.