老師：高永安 學生：蔡育修

1. ICI Mitigation for Pilot-Aided OFDM Mobile SystemsYasamin Mostofi, Member, IEEE and Donald C. Cox, Fellow, IEEEIEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 4, NO.2, MARCH 2005 老師：高永安 學生：蔡育修

2. Outline • Introduction • System model • Piece-Wise Linear Approximation Method I Method II • Mathematical Analysis and Simulation Result • Noise/Interference Reduction • Simulation Results and Conclusion

3. Introduction • Transmission in a mobile communication environment is impaired by both delay and Doppler spread. • As delay spread increases, symbol duration should also increase. reasons---1.near-constant channel in each frequency subband. 2.prevent ISI. • OFDM system become more susceptible to time-variations as symbol length increases. Time-variations introduce ICI. be mitigated to improve the performance.

4. We introduce two new methods to mitigate ICI. Both methods use a piece-wise linear model to approximate channel time-variations.

5. Assume perfect timing synchronizaton System model

7. The channel output y

8. The FFT of sequence y

9. Furthermore,

10. An estimate of Hi,0 can then be acquired at pilot： Pilot Extraction

11. In the absence of mobility, L pilots would have been enough to estimate the channel. • However, in the presence of Doppler, due to the ICI term, using them for data detection results in poor perfor-mance. • This motivates the need to mitigate the resultant ICI.

12. Piece-Wise Linear Approximation • We approximate channel time-variations with a piece-wise linear model with a constant slope over the time duration T.

13. For normalized Doppler of up to 20%, linear approxi- mation is a good estimate of channel time-variations. We will derive the frequency domain relationship. Therefore, we approximate

14. Then, we will have

15. Futhermore,

16. An FFT of y：

17. To solve for X, both Hmid and Hslope should be estimated. • Matrix C is fixed matrix and Hmid is readily available. • So we show how to estimate Hslope with our two methods.

18. The output prefix vector Method I：ICI Mitigation Using Cyclic Prefix

19. Then,

20. Equations (9) and (11) provide enough information to solve for X. • We use a simpler iterative approach to solve for X.

21. Method II：ICI Mitigation Utilizing Adjacent Symbols • This can be done by utilizing either the previous symbol or both adjacent symbols. • A constant slope is assumed over the time duration of T+(N/2)*Ts for the former and T for the latter.

22. Estimate of the slopes in region 2：

23. Utilizing two slopes introduces a minor change in (8).

24. It can be easily shown the frequency domain relationship

26. Method I and Method II can handle considerably higher delay and Doppler spread at the price of higher compu- tation complexity.

27. Mathematical Analysis and Simulation Result • We define SIRave as the ratio of average signal power to the average interference power. • Our goal is to calculate SIRave when ICI is mitigated and compare it to the that of the “no mitigation” case.

28. Estimated channel taps are compared with a Threshold. Let MAV represent the tap with maximum absolute value. All the estimated taps with absolute values smaller than MAV/γ for some γ>=1 will be zeros. Noise/Interference Reduction

29. Simulation Results • System parameters

30. The power-delay profile of channel#1 has two main taps that are separated by 20μs. • The power-delay profile of channel#2 has two main clus- ters with total delay of 36.5μs.

31. Each channel tap is generated as Jakes model. • To see how ICI mitigation methods reduce the error floor. in the absence of noise for both channels.

32. To see the effect of noise for fd,norm = 6.5%

33. To see how ICI mitigation methods reduce the required received SNR for achieving a Pb = 0.2.

34. Conclusion • Both methods used a piece-wise linear approximation to estimate channel time-variations in each OFDM symbol. • These methods would reduce average Pb or the required received SNR to a value close to that of the case with no Doppler. • The power savings become considerable as fd,norm incre- ases.