1. Noise Cancelation for MIMO System Prepared by: Heba Hamad Rawia Zaid Rua Zaid Supervisor: Dr.Yousef Dama

2. Outline Aim and objectives 2 Interference Cancellation Techniques  SIC  Optimal ordering with SIC  ML  Cancel the effect of the transmitted power using a feedback signal process  2*1 MIMO Using STC  HIPERLAN/2 Simulation and Results SWOT Recommendation for Future Works

3. 3 Aims and Objectives Present a method to cancel the interference that is caused by the transmitting antennas closely spaced to the receive antennas of the MIMO system.

4. 4 Interference Cancellation Techniques 4 Cancel the effect of the transmitted power using a feedback signal process ML Optimal ordering SIC MMSEZF HIPERLAN/2 2*1 MIMO Using STC

5. Generate random binary sequence of +1′s and -1′s. Group them into pair of two symbols and send two symbols in one time slot Multiply the symbols with the channel and then add white Gaussian noise. Equalize the received symbols with Zero Forcing criterion Find the power of received symbol from both the spatial dimensions Take the symbol having higher power, subtract from the received symbol Perform Maximal Ratio Combining for equalizing the new received symbol Perform hard decision decoding and count the bit errors Type of method Take the symbol from the second spatial dimension, subtract from the received symbol ZF-SIC with optimal ordering ZF-SIC Zero Forcing

6. 6 Generate random binary sequence of +1′s and -1′s. Group them into pair of two symbols and send two symbols in one time slot Multiply the symbols with the channel that add with and then add white Gaussian noise. Equalize the received symbols with MMSE criterion Find the power of received symbol from both the spatial dimensions Take the symbol having higher power, subtract from the received symbol Perform Maximal Ratio Combining for equalizing the new received symbol Perform hard decision decoding and count the bit errors Type of method Take the symbol from the second spatial dimension, subtract from the received symbol MMSE-SIC with optimal ordering MMSE-SIC MMSE

7. Noise ZF equalization MMSE equalization +

8. Generate random binary sequence of +1′s and -1′s. Group them into pair of two symbols and send two symbols in one time slot Multiply the symbols with the channel and then add white Gaussian noise. Find the minimum among the four possible transmit symbol combinations Based on the minimum chose the estimate of the transmit symbol Maximum Likelihood

9.  Cancel the effect of the transmitted power using a feedback signal process o 2*1 MIMO Using STC o HIPERLAN/2

10. 2*1 MIMO Using STC Get the channel information of the users Modulating the data of users and sending it by using Alamouti method Multiplying the send symbols by the channel information Receiving the signal of both users during two time slots according to Alamouti Receiving the feedback from user 1 Subtracting the feedback signal from the receive signal Feedback signal Decoding the new signal to get the symbols of user 2 End

11. HIPERLAN/2 System

12. Simulation and Results

13. ZF _SIC with MMSE_SIC 13

14. ZF _SIC,MMSE_SIC with optimal ordering 14

15. Maximum likelihood 15

16. Cancel the effect of the transmitted power using a feedback signal process  2*1 MIMO Using STC BER versus SNR when the transmitted power is changing: 16

17. Cont… 17 BER versus SNR when the received power is changing :

18. Cont… 18 BER versus SNR when the feedback mismatch is changing:

19. 19  HIPERLAN/2 HIPERLAN/2 using16-QAM with different distributions of antennas:

20. Cont… 20 HIPERLAN/2 performance when nTx=2 and nRx=1 for different modulation schemes:

21. Cont… 21 BER versus SNR when the transmitted power is changing

22. Cont… 22 BER versus SNR when the received power is changing

23. Cont… 23 BER versus SNR when the feedback mismatch is changing

24. Cont… 24 BER versus SNR with and without noise cancelation: BER versus SNR

25. ws TO In practice its difficult to estimate the response of the channel, but in our project the channel is assumed to be known. The proposed methodology has not been implemented in reality. Increasing the capacity. Enhancing the reliability. Improving the signal-to-noise ratio. Increasing the data rate of the wireless systems. WiFi – 802.11n WiMAX 3G 4G 25

26. Recommendation for Future Works The suggested methodology can be implemented in reality then measuring the results and comparing it with the simulated results. Studying the performance of the system with other types of channels and other type of diversity code. studying the other types of antennas distributions in both transmitting and receiving sides. 26

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