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Singular Value Decomposition. Speaker : 詹承洲 Advisor : Prof. Andy Wu Date : 2008/01/22. Outline. Introduction to Singular Value Decomposition Problem Statement What You Will Learn Expected Results. Motivation. Singular Value Decomposition. Noise is not noise only anymore

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singular value decomposition

Singular Value Decomposition

Speaker : 詹承洲

Advisor : Prof. Andy Wu

Date : 2008/01/22

outline
Outline
  • Introduction to Singular Value Decomposition
  • Problem Statement
  • What You Will Learn
  • Expected Results
singular value decomposition1
Singular Value Decomposition
  • Noise is not noise only anymore
  • Collect the desired signals respectively instead of eliminating them
applications
Applications
  • OFDM MIMO systems
    • IEEE 802.11n (Wi-Fi)
  • Antenna arrays
problem statement
Problem Statement
  • Algorithm domain:
    • Too complex computations
    • Theoretical convergence problem
    • No uniform solution
  • Architecture domain :
    • Large hardware complexity
    • High-speed issue
    • High power consumption
what you will learn
What You Will Learn
  • Matrix computations
  • Various SVD processing algorithms
  • Evaluation of the performance of SVD algorithms
expected results
Expected Results
  • Paper survey and acquaintance with SVD process
  • Simulation for SVD algorithms
  • Propose or modify existing SVD algorithms
background needed
Background Needed
  • Linear algebra
  • C or Matlab programming
  • Probability (Optional)
  • DSP or Communication Systems (Optional)
  • Enthusiasm