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Multiple Input Multiple Output (MIMO) Communications System Madhup Khatiwada (M.E. Student) University of Canterbury, Christchurch, New Zealand Supervisor – Dr. P. J. Smith

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Multiple Input Multiple Output (MIMO) Communications System. Madhup Khatiwada (M.E. Student) University of Canterbury, Christchurch, New Zealand Supervisor – Dr. P. J. Smith. Channel H. N TXers. M RXers. Coding Modulation. Demodulation Decoding. MIMO Wireless System Introduction I. - PowerPoint PPT Presentation

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Page 1: Multiple Input Multiple Output (MIMO) Communications System

Multiple Input Multiple Output (MIMO) Communications System

Madhup Khatiwada(M.E. Student)

University of Canterbury, Christchurch, New ZealandSupervisor – Dr. P. J. Smith

Page 2: Multiple Input Multiple Output (MIMO) Communications System

MIMO Wireless SystemIntroduction I

• Multiple Input Multiple Output (MIMO)– Multiple antennas at source and destination.

• Motivation : Current wireless systems– Capacity constrained networks.– Issues related to quality and coverage

Coding Modulation

DemodulationDecoding

N TXers M RXersChannel

H

Page 3: Multiple Input Multiple Output (MIMO) Communications System

Introduction IIMIMO increases capacity

– MIMO uses independent channel fading due to multipath propagation to increase capacity.

– No extra expen$ive bandwidth required !!

MIMO gives reliable communication – Multiple independent samples of the same signal at

the receiver give rise to “diversity”.

C NT log2(1 + SNR)

Page 4: Multiple Input Multiple Output (MIMO) Communications System

Introduction IIIDiversity exhibited :

Spatial diversity– spacing between antennas

Transmit diversity– space – time coding

Receive diversity– receive antennas

Page 5: Multiple Input Multiple Output (MIMO) Communications System

System Model I

Mr

r:1

MNM

N

hh

hh

....::

....

1

111

O

Ns

s:1

Mn

n:1

nsHR R : received vectorH : quasi-static channel matrixs : transmitted vectorn : white Gaussian noise vector

MIMO system with N transmit and M receive antennas

Page 6: Multiple Input Multiple Output (MIMO) Communications System

System Model II

Rayleigh channel model : multi-path

– Channel between any two pair of antennas is independent

– Each hik is complex Gaussian with unit variance

Ricean channel model : line of sight (K = 0dB)

Page 7: Multiple Input Multiple Output (MIMO) Communications System

What led us to MIMO?

Courtesy- AT&T Labs

Page 8: Multiple Input Multiple Output (MIMO) Communications System

Shortcoming of SISO channels

SISO Channel Equationr(t) = H(t)*s(t)+n(t)

Problems with SISO channels:– High Pathloss.– Interference of multipaths.– Inefficient bandwidth utilisation.– Low gain.

Capacity of the SISO channel:

)1(log 22 hC Where ρ is the SNR at any RX antenna

Page 9: Multiple Input Multiple Output (MIMO) Communications System

Exploring Ideas on Diversity

• Make use of Multipath propagation, than mitigate it.• Improves the performance in fading.

– Frequency Diversity: Signal transmitted in several frequency bands (coherence BW).

– Time Diversity: Signal is transmitted in different time slots.

– Polarization Diversity: Two antennas with different polarisation for reception/transmission.

– Space Diversity: Multiple antennas to receive signal.

Page 10: Multiple Input Multiple Output (MIMO) Communications System

Receive Diversity

)1(log2

12

M

iihC

Use of well separated multiple receive antennas to generate independent reception of symbols.

Selection Diversity: Choose signal with largest received power or SNR.Switched Diversity: Choose alternate antenna if signal falls below certain threshold.Linear Combining: Linearly combine weight replica of all received signal.

Capacity increases logarithmically with number of receive antennas

SIMO channel

H11

H21

H = [ H11 H21]

Page 11: Multiple Input Multiple Output (MIMO) Communications System

Transmit Diversity

1211HH

HH11

H12

MISO channel

Improves signal quality at Rx by simple processing across two transmit antennas. •Tx Diversity order = Rx Diversity order (MRRC).(Alamouti scheme)•No feedback from Tx to Rx.•No bandwidth expansion needed.•Improves error performance, data rate or capacity.•Allows to use higher level modulation schemes.

Capacity increases logarithmically with number of transmit antenna

)/1(log2

12

N

iihNC

Page 12: Multiple Input Multiple Output (MIMO) Communications System

Multiple Input Multiple Output systemH11

H22

H12H21

2221

1211

HHHH

H

))/(det(log *2 HHNIC MEP MIMO channel

Transmitting Tx and receiving Rx ends equipped with multiple antenna system.•Two dimensional channel: Spatial and Temporal•Often described as independent channels.•Multiple independent samples of the same signal at the receiver gives rise to “diversity”.MIMO channels transfer function is a complex matrix H for N Tx and M Rx antennas.

MIMO uses independent channel fading due to multipath propagation to increase capacity.

Page 13: Multiple Input Multiple Output (MIMO) Communications System

Comparison of Capacity

Probability (capacity > abscissa)

Capacity (bits/sec/Hz)

Increased MIMO channel capacity compared to SISO, SIMO or MISO.

Page 14: Multiple Input Multiple Output (MIMO) Communications System

Space- Time Coding I• Space- Time Coding scheme allows for the adjusting and

optimization of joint encoding across space and time in order to maximize the reliability of a wireless link.

• Space- Time Coding provides diversity gain and coding gain by introducing spatial and temporal correlation into the signals.

• Space- Time Coding also• Improves Downlink performance.• Does not require CSI at the Tx.• Robust again non-ideal connections.

Page 15: Multiple Input Multiple Output (MIMO) Communications System

Space- Time Coding II

• For each input symbol, space time encoder chooses the constellation points to simultaneously transmit from each antenna, achieving coding gain and diversity

• A Typical ST Coding Model may include– N Tx and M Rx antennas.– Overall channel made up of N*M slowly varying subchannels.– Each sub-channel is modelled as Rayleigh fading.– At any time N signals are transmitted simultaneously one from each Tx

antenna.– The sub-channels undergo independent fading.– Fading coefficients are assumed to be fixed and independent.

V.Tarokh, H.Jafarkhani, A.R.Calderbank Space-time block codes from orthogonal designs, IEEE Trans. On Information Theory June 1999

Page 16: Multiple Input Multiple Output (MIMO) Communications System

Space- Time Coding IIISpace-Time Block Codes:

These codes are transmitted using an orthogonal block structure which enables simple decoding at the receiver.

– diversity gain (use outer code to get coding gain)– simple detection

Space-Time Trellis Codes: These are convolutional codes extended to the case of multiple transmit and receive antennas. – coding and diversity gain – require Viterbi detector, which is complex

Page 17: Multiple Input Multiple Output (MIMO) Communications System

Space- Time Block Code – Alamouti Scheme

Decoding:– Linearly combine received symbols.– Perform Maximum Likelihood (ML) detection.

011000 )( nshshtrr

1*01

*101 )( nshshTtrr

Alamouti, S. M., “A simple transmit diversity technique for wireless communications” Selected Areas in Communications, IEEE Journal,16(8):1451–1458, 1998

Page 18: Multiple Input Multiple Output (MIMO) Communications System

Simulation Results of Alamouti Scheme

Increase in number of antennas leads to increase in diversity which consequently leads to better system performance.

Simulation Parameters Number of Antennas – 1x1, 2x1,

3x1, 4x1. Modulation Scheme – BPSK Channel Model – Flat Rayleigh

Fading

Courtesy- Hoo-Jin Lee, Shailesh Patil, and Raghu G. Raj, Univ. of Texas

Page 19: Multiple Input Multiple Output (MIMO) Communications System

Spatial Multiplexing Technique – An Overview

• Multiple data streams are transmitted simultaneously and on the same frequency using a transmit array – Different data sub-streams are transmitted from

different antennas• The transmitter needs no channel state information

– No need for fast feedback links.

Page 20: Multiple Input Multiple Output (MIMO) Communications System

Spatial Multiplexing Detection• Maximum Likelihood (ML): optimum and most

time consuming detection method • Linear detection

– Zero-Forcing (ZF): pseudo inverse of the channel, simple

– Minimum mean-squared error (MMSE) : simple detection with intermediate performance

rHHHI HHNSNR

s 1)1(

rHHH HHs 1)(ˆ

2

ˆˆminargˆ sHrs

ss

Page 21: Multiple Input Multiple Output (MIMO) Communications System

Comparison of Detection Methods

-15 -10 -5 0 5 10 15 20 25 30-5

-4.5

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0 Comparision of BER for ZF, MMSE and ML

SNR db( 10*log10(1/q))

Log1

0BE

R

ZFMMSEML

Simulation Parameters• Number of Antennas – 4 x 4• Modulation Scheme – BPSK• Channel Model – Flat Rayleigh Fading

Page 22: Multiple Input Multiple Output (MIMO) Communications System

Comparison for different systems

Simulation Parameters Number of Antennas – 2 x 2, 6 x 6, 8 x 8 Modulation Scheme – BPSK Channel Model – Flat Rayleigh Fading

-15 -10 -5 0 5 10 15 20 25 30-5

-4.5

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0 Comparision of BER for 2-2 MMSE and 2-2 ML

SNR db( 10*log10(1/q))

Log1

0BE

R

MMSE22ML22

-15 -10 -5 0 5 10 15 20 25 30-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0 Comparision of BER for 6-6 MMSE and 6-6 ML

SNR db( 10*log10(1/q))

Log1

0BE

R

MMSE66ML66

-15 -10 -5 0 5 10 15 20 25 30-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0 Comparision of BER for 8-8 MMSE and 8-8 ML

SNR db( 10*log10(1/q))

Log1

0BE

R

MMSE88ML88

Page 23: Multiple Input Multiple Output (MIMO) Communications System

V-BLASTV-BLAST (Vertical Bell Labs Layered Space-Time)

– extracts data streams by ZF or MMSE filter with ordered successive interference cancellation (SIC)

– Steps for V-BLAST detection1.Ordering: choosing the “best” channel2.Interference Nulling/ Reduction : using ZF or

MMSE3.Slicing: making a symbol decision 4.Canceling: subtracting the detected symbol5.Iteration: going to the first step to detect the next symbol

G.J.Foschini, Bell Labs Technical Journal 1996

Page 24: Multiple Input Multiple Output (MIMO) Communications System

Performance of V-BLAST

-15 -10 -5 0 5 10 15 20 25 30-5

-4.5

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0 Comparision of BER for MMSE, V-BLAST MMSE and ML

SNR db( 10*log10(1/q))

Log1

0BE

R

MMSEV- BLAST MMSEML

Simulation Parameters• Number of Antennas – 4 x 4• Modulation Scheme – BPSK• Channel Model – Flat Rayleigh Fading

Page 25: Multiple Input Multiple Output (MIMO) Communications System

Comparison among Spatial Multiplexing Receivers in Rayleigh Channel

Performance and Complexity:ML receiver > MMSE V-BLAST (SIC) receiver > ZF V-BLAST (SIC) receiver > MMSE receiver > ZF receiver

Simulation Parameters• Number of Antennas – 2 x 3• Modulation Scheme – 4 QAM• Channel Model – Flat Rayleigh Fading

Courtesy- Hoo-Jin Lee, Shailesh Patil, and Raghu G. Raj, Univ. of Texas

Page 26: Multiple Input Multiple Output (MIMO) Communications System

Issues of Ordering and Speed I

Ordering• Optimal ordering plays a significant role in achieving high

performance In V-BLAST, the best channel is picked based on

highest SINR, but picking up channels based on highest SINR value doesn’t seem to be the optimal way of ordering.[ In the initial V-BLAST paper, Foschini et al. have proposed a optimal method for ordering for ZF detection. But for MMSE detection (which is proved to be better than ZF), no such optimal ordering scheme has been proposed. So it has been a practice to order based on highest SINR in MMSE detection ]

Page 27: Multiple Input Multiple Output (MIMO) Communications System

Significance of Optimal Ordering

Picking the first best channel can effect significantly in the whole system performance

-15 -10 -5 0 5 10 15 20 25 30-5

-4.5

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0 Comparision of BER for MMSE, V-BLAST One Stage, Best Channel and ML

SNR db( 10*log10(1/q))

Log1

0BE

R

MMSEV-BLAST One StageBest Channel One StageML

Simulation Parameters• Number of Antennas – 4 x 4• Modulation Scheme – BPSK• Channel Model – Flat Rayleigh

Fading

Page 28: Multiple Input Multiple Output (MIMO) Communications System

Issues of Ordering and Speed IISwitching • Switching based on SINR or Eigenvalues of the channel helps

in reducing the computational time by adding very little complexity to the system.

Switching is a scheme in which the receiver switches its detection technique from V-BLAST MMSE to simple MMSE depending upon the channel condition (either SINR values or eigenvalues of the channel).

Switching allows us to trade off performance with time

Page 29: Multiple Input Multiple Output (MIMO) Communications System

Significance of Switching

The position of the blue curve may vary anywhere between the black and red curves depending upon performance we require trading off with the processing time.

Simulation Parameters• Number of Antennas – 4 x 4• Modulation Scheme – BPSK• Channel Model – Flat Rayleigh

Fading

-15 -10 -5 0 5 10 15 20 25 30-5

-4.5

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0 Comparision of BER for MMSE, Switching ,V-BLAST MMSE and ML

SNR db( 10*log10(1/q))

Log1

0BE

R

MMSESwitching technique appliedV-BLAST MMSEML

Simulation time reduced by nearly 60% at 10 db SNR case

Page 30: Multiple Input Multiple Output (MIMO) Communications System

Significance of Ordering and Switching in Overloaded system

For systems with less receive antennas, optimal ordering can be a crucial issue for system performance

-15 -10 -5 0 5 10 15 20 25 30-5

-4.5

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0 Comparision of BER for MMSE, V-BLAST MMSE, Switching, Best Channel and ML

SNR db( 10*log10(1/q))

Log1

0BE

R

MMSEDetecting First Best ChannelSwitching Technique AppliedV- BLAST MMSEMLV-BLAST ML

Simulation Parameters• Number of Antennas – 4 x 2• Modulation Scheme – BPSK• Channel Model – Flat Rayleigh

Fading

Page 31: Multiple Input Multiple Output (MIMO) Communications System

• Channel capacity increases linearly with min(M, N) antennas.

• Optimal ordering plays a vital role in increasing the performance of V-BLAST systems with MMSE.

• Switching techniques may be useful in reducing processing time while adding very little complexity to the system.

This research project is still on progress……………

“Take- home Message”

Page 32: Multiple Input Multiple Output (MIMO) Communications System

Key references I• Foschini, G. J. and Gans, M. J., “ On limits of wireless communications in

a fading environment when using multiple antennas,” Wireless Personal Communications, Vol. 6, pp. 311-335, 1998

• Golden, G. D., Foschini, G. J., Valenzuela, R. A., and Wolniansky, P. W., “ Detection algorithm and initial laboratory results using V-BLAST space-time communication architecture,” IEE Lett., Vol. 35, No. 1, pp. 14-16, January 1999

• Benjebbour, A., Murata, H. and Yoshida, S., “Comparison of ordered successive receivers for space-time transmission,” Vehicular Technology Conference, 2001. VTC 2001 Fall. IEEE VTS 54th , Vol. 4 , pp. 2053 - 2057 , 7-11 Oct. 2001

• Tarokh, V., Jafarkhani, H., and Calderbank, A. R., “Space-time Codes for High Data Rate Wireless Communication: Performance Criterion and Code Construction,” IEEE Trans. Inform. Theory, Vol. 44, No. 2, pp. 744-765, July 1998

• Hassell, C.Z.W., Thompson, J., Mulgrew, B. and Grant, P.M., “A comparison of detection algorithms including BLAST for wireless communication using multiple antennas” The 11th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Vol. 1 , pp. 698 – 703, 18-21 Sept. 2000

Page 33: Multiple Input Multiple Output (MIMO) Communications System

Key references II• Alamouti, S. M., “A simple transmit diversity technique for wireless

communications,” Selected Areas in Communications, IEEE Journal,16(8):1451–1458, 1998

• Gore, D. A., Heath, R. W. Jr., and Paulraj, A. J., “Performance Analysis of Spatial Multiplexing in Correlated Channels,” submitted to Communications, IEEE Transactions March 2002

• Golden, G. D., Foschini, C. J., Valenzuela, R. A., and Wolniansky, P. W., “ Detection algorithm and initial laboratory results using V-BLAST space-time communication architecture,” IEEE Lett., Vol. 35, No. 1, pp. 14-16, January 1999

• Gesbert, D.; Shafi, M.; Da-shan Shiu; Smith, P.J.; Naguib, A .,“From theory to practice: an overview of MIMO space-time coded wireless systems”,Selected Areas in Communications, IEEE Journal on , Vol. 21 , Issue. 3,pp.281 – 302, April 2003