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Noise Cancelation for MIMO System

Noise Cancelation for MIMO System. Prepared by : Heba Hamad Rawia Zaid Rua Zaid Supervisor : Dr.Yousef Dama. Outline. Aim and objectives. Interference Cancellation Techniques. SIC. Optimal ordering with SIC. ML

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Noise Cancelation for MIMO System

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

  2. Outline • Aim and objectives • 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. 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. Interference Cancellation Techniques MMSE ZF HIPERLAN/2 2*1 MIMO Using STC 4

  5. Zero Forcing 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 ZF-SIC with optimal ordering Type of method ZF-SIC Take the symbol from the second spatial dimension, subtract from the received symbol 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

  6. MMSE 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 MMSE-SIC with optimal ordering Type of method MMSE-SIC Take the symbol from the second spatial dimension, subtract from the received symbol 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

  7. + ZF equalization Noise MMSE equalization

  8. Maximum Likelihood 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

  9. Cancel the effect of the transmitted power using a feedback signal process • 2*1 MIMO Using STC • 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 Feedback signal • 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 • 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

  14. ZF _SIC ,MMSE_SIC with optimal ordering

  15. Maximum likelihood

  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:

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

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

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

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

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

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

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

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

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

  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.

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