Noise Cancelation for MIMO System

Prepared by: Heba Hamad Rawia Zaid

Rua Zaid Supervisor: Dr.Yousef Dama

Outline• Aim and objectives

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•Interference Cancellation TechniquesSIC Optimal ordering with SIC

• Simulation and Results

• SWOT• Recommendation for Future Works

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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.

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Interference Cancellation Techniques

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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

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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

Noise ZF equalization

MMSE equalization+

 

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

Cancel the effect of the transmitted power using a feedback signal process

o2*1 MIMO Using STC

oHIPERLAN/2

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

HIPERLAN/2 System

Simulation and Results

ZF _SIC with MMSE_SIC

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0 5 10 15 20 2510

-5

10-4

10-3

10-2

10-1

Average Eb/No,dB

Bit E

rror

Rate

BER for BPSK modulation with 2x2 MIMO and MMSE-SIC equalizer (Rayleigh channel)

theory (nTx=2,nRx=2, ZF)

theory (nTx=1,nRx=2, MRC)sim (nTx=2, nRx=2, MMSE-SIC)

sim (nTx=2, nRx=2, ZF-SIC)

ZF _SIC ,MMSE_SIC with optimal ordering

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0 5 10 15 20 2510

-5

10-4

10-3

10-2

10-1

Average Eb/No,dB

Bit E

rror

Rate

BER for BPSK modulation with 2x2 MIMO and MMSE-SIC equalizer (Rayleigh channel)

theory (nTx=2,nRx=2, ZF)

theory (nTx=1,nRx=2, MRC)sim (nTx=2, nRx=2, MMSE-SIC-Sort)

sim (nTx=2, nRx=2, ZF-SIC-Sort)

Maximum likelihood

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0 5 10 15 20 2510

-5

10-4

10-3

10-2

10-1

Average Eb/No,dB

Bit E

rror

Rate

BER for BPSK modulation with 2x2 MIMO and ML equalizer (Rayleigh channel)

sim (nTx=2, nRx=2, ML)

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 :

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Cont…

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BER versus SNR when the received power is changing :

0 5 10 15 20 25 3010

-4

10-3

10-2

10-1

100

Plot of symbol error rates using16-QAM Tx=2, Rx=1

SNR(dB)

Sym

bol E

rror R

ate

perfect feedback

Rx=-30 dBm

Rx=-35 dBm

Rx=-40 dBm

Cont…

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BER versus SNR when the feedback mismatch is changing :

0 5 10 15 20 25 3010

-4

10-3

10-2

10-1

100

Plot of symbol error rates using16-QAM Tx=2, Rx=1

SNR(dB)

Symb

ol Er

ror R

ate

perfect feedback

feedback=-40dBmfeedback=-45dBm

feedback=-50dBm

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

-6 -4 -2 0 2 4 6 8 10 1210

-6

10-5

10-4

10-3

10-2

10-1

100

Tx=2 Rx=1

Tx=2 Rx=2

Tx=3 Rx=1Tx=3 Rx=2

Tx=3 Rx=3

Cont…

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HIPERLAN/2 performance when nTx=2 and nRx=1 for different modulation schemes:

-5 0 5 10 15 2010

-6

10-5

10-4

10-3

10-2

10-1

100

16QAM

4QAM 32QAM

64QAM

Cont…

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BER versus SNR when the transmitted power is changing

-5 0 5 10 15 2010

-6

10-5

10-4

10-3

10-2

10-1

100

Tx power=35 dB

Tx power=40 dB

Tx power=50 dB

Cont…

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BER versus SNR when the received power is changing

-5 0 5 10 15 2010

-6

10-5

10-4

10-3

10-2

10-1

100

Rx power=-30 dB

Rx power=-35 dB

Rx power=-40 dB

Cont…

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BER versus SNR when the feedback mismatch is changing

-5 0 5 10 15 2010

-6

10-5

10-4

10-3

10-2

10-1

100

feedback=-50 dB

feedback=-45 dB

feedback=-40 dB

Cont…

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BER versus SNR with and without noise cancelation:

BER versus SNR

-5 0 5 10 15 2010

-6

10-5

10-4

10-3

10-2

10-1

100

with noise cancelation

without noise cancelation

s w

O T•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

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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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