5.2 Simulation Results
5.2.2 Simulations of On-Line Blind Equalization
In this subsection, we use the adaptive channel structure method to illustrate its perfor-mance. In Fig. 5.12 we show equalizer estimation this algorithm for 25dB SNR and with 500 symbols and in Fig. 5.13 and 5.14 show that our algorithm have better performance than the linear prediction algorithm. For the channel of TXK, the performance of our algorithm for SNR below 25dB still remains an acceptable ISI and SINR.
−4 −3 −2 −1 0 1 2 3 4
−3
−2
−1 0 1 2 3 4
Received Data
Real
Imaginary
−4 −3 −2 −1 0 1 2 3 4
−4
−3
−2
−1 0 1 2 3 4
Channel Output Whitening Method (Howard Fan)
Real
Imaginary
Fig. 5.2: Signal constellations for 500 symbols at SNR=25dB. (a) Without equalization.
(b) After equalization for Fan’s method.
−4 −3 −2 −1 0 1 2 3 4
−4
−3
−2
−1 0 1 2 3 4
Channel Structure Method
Real
Imaginary
−4 −3 −2 −1 0 2 4 3 4
−4
−3
−2
−1 0 1 2 3 4
Iterative Least Square Method
Real
Imaginary
Fig. 5.3: After equalization for Channel Structure and Iterative Least Square method.
0 10 20 30 40 10−6
10−4 10−2 100 102
SNR (dB)
ISI
ISI Comparison
Channel Structure Method Fan
Iterative Least Square Method TXK
MRE
0 10 20 30 40
10−6 10−4 10−2 100 102
SNR (dB)
ISI
ISI Comparison
Channel Structure Method Fan
Iterative Least Square Method TXK
MRE
Fig. 5.4: Performance comparison versus different SNR for ISI. ( (a) 250 and (b) 750 symbols)
0 10 20 30 40
SINR of Equalizer Output
Channel Structure Method Fan
Iterative Least Square Method TXK
SINR of Equalizer Output Channel Structure Method
Fan
Iterative Least Square Method TXK
MRE
Fig. 5.5: Performance comparison versus different SNR for SINR. ( (a) 250 and (b) 750 symbols)
0 500 1000 1500 2000 10−3
10−2 10−1 100
Data Length
ISI
ISI Comparison
Channel Structure Method Fan
Iterative Least Square Method TXK
MRE
0 250 500 750 1,000 1,250 1,500 1750 2000 10−3
10−2 10−1 100
Data Length
ISI
ISI Comparison
Channel Structure Method Fan
Iterative Least Square Method TXK
MRE
Fig. 5.6: Performance comparison versus different symbol number for ISI. ( (a) 20dB and (b) 25dB)
0 250 500 750 1,000 1,250 1,500 1,750 2000
SINR of Equalizer Output
Channel Structure Method Fan
Iterative Least Square Method TXK
MRE
0 500 1000 1500 2000
5
SINR of Equalizer Output
Channel Structure Method Fan
Iterative Least Square Method TXK
MRE
Fig. 5.7: Performance comparison versus different symbol number for SINR. ( (a) 20dB and (b) 25dB)
0 5 10 15 20 25 30 35 40 10−6
10−4 10−2 100 102
SNR (dB)
ISI
ISI Comparison
Channel Structure Method Fan
Iterative Least Square Method TXK
MRE
0 5 10 15 20 25 30 35 40
10−6 10−4 10−2 100 102
SNR (dB)
ISI
ISI Comparison
Channel Structure Method Fan
Iterative Least Square Method TXK
MRE
Fig. 5.8: Using Channel of TXK to performance comparison versus different SNR for ISI with 500 symbols. ( (a) N=5 and (b) N=7)
0 5 10 15 20 25 30 35 40
SINR of Equalizer Output
Channel Structure Method Fan
Iterative Least Square Method TXK
SINR of Equalizer Output Channel Structure Method
Fan
Iterative Least Square Method TXK
MRE
Fig. 5.9: Using Channel of TXK to performance comparison versus different SNR for SINR with 500 symbols. ( (a) N=5 and (b) N=7)
0 500 1000 1500 2000 10−3
10−2 10−1 100 101
Data Length
ISI
ISI Comparison
Channel Structure Method Fan
Iterative Least Square Method TXK
MRE
0 250 500 750 1000 1250 1500 1750 2000
10−4 10−3 10−2 10−1 100 101
Data Length
ISI
ISI Comparison
Channel Structure Method Fan
Iterative Least Square Method TXK
MRE
Fig. 5.10: Using Channel of TXK to performance comparison versus different symbol number for ISI with SNR=25dB. ( (a)N = 5 and (b) N = 7)
0 250 500 750 1000 1250 1500 1750 2000
SINR of Equalizer Output
Channel Structure Method Fan
Iterative Least Square Method TXK
SINR of Equalizer Output
Channel Structure Method Fan
Iterative Least Square Method TXK
MRE
Fig. 5.11: Using Channel of TXK to performance comparison versus different symbol number for SINR with SNR=25dB. ( (a)N = 5 and (b) N = 7)
−4 −3 −2 −1 0 1 2 3 4
−4
−3
−2
−1 0 1 2 3 4
Received Data
Real
Imaginary
−0.06 −0.04 −0.02 0 0.02 0.04 0.06
−0.06
−0.04
−0.02 0 0.02 0.04 0.06
Adaptive Channel Structure Method
Real
Imaginary
Fig. 5.12: Performance comparison versus different SNR for ISI and SINR with 20dB.
0 500 1000 1500 2000 2500 3000 10−3
10−2 10−1 100
Time
ISI
ISI Comparison
Adaptive Channel Structure Method Linear Prediction
0 500 1000 1500 2000 2500 3000
8 10 12 14 16 18 20
Time
SINR (dB)
SINR of Equalizer Output
Adaptive Channel Structure Method Linear Prediction
Fig. 5.13: Performance comparison versus different SNR for ISI and SINR with 20dB.
0 10 20 30 40 10−3
10−2 10−1 100
SNR (dB)
ISI
ISI Comparison
Adaptive Channel Structure Method Linear Prediction
0 10 20 30 40
0 5 10 15 20 25 30
SNR (dB)
SINR (dB)
SINR of Equalizer Output
Adaptive Channel Structure Method Linear Prediction
Fig. 5.14: Performance comparison versus different SNR for ISI and SINR withN=4 and 1500 symbol.
0 500 1000 1500 2000 2500 3000 10−3
10−2 10−1 100 101
SNR (dB)
ISI
ISI Comparison
Adaptive Channel Structure Method Linear Prediction
0 500 1000 1500 2000 2500 3000
−10
−5 0 5 10 15 20 25
SNR (dB)
SINR (dB)
SINR of Equalizer Output
Adaptive Channel Structure Method Linear Prediction
Fig. 5.15: Using channel of TXK to performance comparison versus different symbol number for ISI and SINR withN=7 and 30dB.
0 10 20 30 40 10−3
10−2 10−1 100 101
SNR (dB)
ISI
ISI Comparison
Adaptive_Channel Structure Method Linear Prediction
0 10 20 30 40
−5 0 5 10 15 20 25
SNR (dB)
SINR (dB)
SINR of Equalizer Output
Adaptive Channel Structure Method Linear Prediction
Fig. 5.16: Using channel of TXK to performance comparison versus different SNR for SINR withN=7 and 1500 symbols.
Chapter 6 Conclusion
This thesis presents two design algorithms for subspace blind equalization and an adaptive subspace blind equalizer. We use the property of Toeplitz structure of channel matrix and the concept of mutual-reference filters to develop a new blind equalizer and its adaptive algorithm. We also use the iterative least squares approach to design the blind equalizer.
Simulations are also performed to demonstrate that our algorithms yield better perfor-mance with respect to the measures of ISI and SINR.
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