Channel%20Estimation%20for%20Wired%20MIMO%20Communication%20Systems

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Channel Estimation for Wired MIMO Communication
Systems
05/05/2005

• Final Presentation
• Daifeng Wang
• Dept. of Electrical and Computer EngineeringThe
  University of Texas at Austin
• wang_at_ece.utexas.edu

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Introduction

• Review
• Wired MIMO Communication Systems
• Multicarrier Modulation
• Training-Based Channel Estimation
• Today
• Which channel estimation strategy for wired
  communication systems?
• How to design the training sequence?
• What is the channel model?
• How to estimate
• the wired MIMO
• channel?

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Training-Based Channel Estimation Strategy

• Block-Type
• All subcarriers Period
• Least Square (LS), Minimum Mean-Square (MMSE)
• Slow Fading/Varying Channels
• Decision Feedback Equalizer
• Comb-Type
• Selective subcarriers Interpolation
• LS, MMSE, Least Mean-Square (LMS)
• Fast Fading/Varying Channels
• Interpolation
• Linear
• Second order
• Low-pass
• Spline Cubic
• Time domain

Tradeoff between performance and complexity
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Training Sequences

• y S h n
• h L-tap channel
• S transmitted N x L Toeplitz matrix made up of N
  training symbols
• n AWGN

Domain Method Minimum MSE Complexity Optimal Sequence
Time Periodic Chen Mitra, 2000 Yes High(2N) Yes
Time Aperiodic Tella, Guo Barton, 1998 No Medium(N2) Yes
Time L-Perfect (MIMO) Fragouli, Dhahir Turin, 2003 Almost Low(Nlog2N) Sometimes
Frequency Periodic Tella, Guo Barton, 1999 No Low(Nlog2N) Sometimes
impulse-like autocorrelation and zero
crosscorrelation
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Training-Based MIMO Channel Model

• 2 X 2 MIMO Model

TX 1
RX 1
h11
h12
h21
TX 2
RX 2
h22
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Training-Based Channel Estimation for MIMO

• Least Square (LS)
• Assumes S has full column rank
• Mean-Square (MSE)
• zero-mean and white Gaussian noise
• Sequences satisfy above are optimal sequences
• Optimal sequences impulse-like autocorrelation
  and zero crosscorrelation