1. Singular Value Decomposition
Speaker : 詹承洲
Advisor : Prof. Andy Wu
Date : 2008/01/22

2. Outline Introduction to Singular Value Decomposition Problem Statement
What You Will Learn
Expected Results

3. Motivation

4. Singular Value Decomposition
Noise is not noise only anymore
Collect the desired signals respectively instead of eliminating them

5. SVD for MIMO Systems
SVD
Mess!

6. Applications
OFDM MIMO systems
IEEE n (Wi-Fi)
Antenna arrays

7. Problem Statement Algorithm domain: Architecture domain :
Too complex computations
Theoretical convergence problem
No uniform solution
Architecture domain :
Large hardware complexity
High-speed issue
High power consumption

8. What You Will Learn Matrix computations
Various SVD processing algorithms
Evaluation of the performance of SVD algorithms

9. Expected Results Paper survey and acquaintance with SVD process
Simulation for SVD algorithms
Propose or modify existing SVD algorithms

10. Background Needed Linear algebra C or Matlab programming
Probability (Optional)
DSP or Communication Systems (Optional)
Enthusiasm