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Singular Value Decomposition

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

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  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 802.11n (Wi-Fi) • Antenna arrays

  7. Problem Statement • Algorithm 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

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