1. Channel Estimation for Wired MIMO Communication Systems
05/05/2005
Channel Estimation for Wired MIMO Communication Systems
Final Presentation
Daifeng Wang
Dept. of Electrical and Computer Engineering The University of Texas at Austin

2. Introduction Review Today 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?
11/30/2018

3. 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
11/30/2018

4. [Fragouli, Dhahir & Turin, 2003]
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)
Aperiodic
[Tella, Guo & Barton, 1998] No
Medium(N2)
L-Perfect (MIMO)
[Fragouli, Dhahir & Turin, 2003]
Almost
Low(Nlog2N)
Sometimes
Frequency
[Tella, Guo & Barton, 1999]
* impulse-like autocorrelation and zero crosscorrelation
11/30/2018

5. Training-Based MIMO Channel Model
2 X 2 MIMO Model
TX 1
RX 1
h11
h21
h12
TX 2
RX 2
h22
11/30/2018

6. 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
11/30/2018