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Using LMS weighting value as the CSI for soft decision Viterbi decoder

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Using LMS weighting value as the CSI for soft decision Viterbi decoder. Advisor : Yung-An Kao Student : Chi-Ting Wu 2005.01.28. Outline. Introduction Block diagram Formula computation Simulation results Conclusion. Introduction.

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### Using LMS weighting value as the CSI for soft decision Viterbi decoder

Advisor : Yung-An Kao

Student : Chi-Ting Wu

2005.01.28

Outline
• Introduction
• Block diagram
• Formula computation
• Simulation results
• Conclusion
Introduction
• For Viterbi decoder, we view different sub-carriers in the same channel condition
• Actually, different sub-carrier suffers different channel condition
• Using the CSI for each sub-carriers
• long train symbol? What else?
• equalizer weighting values !!
Formula computation
• According to the Central Limit Theorem, after we transmit lots of symbols, they all seems like Gaussian distribution
• The likelihood function

will become

Formula computation

And we know that the weighting value is

The received signal after phase compensation is

Formula computation

We want the same weighting value for

Therefore, we use the weighting value :

And we take the expected value

Simulation ~ interleaver

500 symbols

100 times average

1:1:15 dB

CFO=0.01

No SFO

Trms=50ns

4 bit quantization

No weighting value

Simulation ~ quantization

100 symbols

100 times average

1:1:15 dB

CFO=0.01

No SFO

Trms=50ns

No weighting value

Simulation ~ weighted CSI

500 symbols

100 times average

1:1:15 dB

CFO=0.01

No SFO

Trms=50ns

4 bit quantization

With interleaver

Simulation ~ weighted CSI

1000 symbols

100 times average

1:1:15 dB

CFO=0.01

No SFO

Trms=50ns

4 bit quantization

With interleaver

Conclusion
• Weighting values added should has better performance
• Some dimension problems should take notice