DIRTY PAPER CODE DESIGN FOR MULTIUSER MIMO BROADCAST CHANNEL

MSICE Thesis

on

DIRTY PAPER CODE DESIGN

FOR

MULTIUSER MIMO BROADCAST

CHANNEL

By:

Krishna Prasad Phelu

(Exam Roll No. 102204)

Supervisor:

Daya Sagar Baral

Asst. Professor, IOE

Date: November 2012

• Motivation

• Problem definition

• Objectives

• Dirty paper code (DPC)

• Multiuser MIMO Broadcast channel

• Methodology

• Results and Discussion

• Conclusion

• Limitation and Future work

Presentation Outline

2

Motivation

• Dirty paper code (DPC) can recover capacity loss due to interference.

• Multiuser MIMO has lots of advantages over ordinary point to point MIMO system.

• DPC is capacity achieving code for MU-MIMO broadcast scenario.

3

Problem Definition

• Dirty paper code (DPC) design is a combined source-channel code design problem.

• In Multiuser MIMO BC base station should perform pre-equalization.

4

• To study on Dirty Paper Code (DPC).

• To design Dirty Paper Code based on nested trellis using trellis coded quantization /trellis coded modulation (TCQ/TCM) scheme.

• To implement Dirty Paper Code for MU- MIMO Broadcast Channel.

Objectives

5

Dirty Paper Code (DPC)

)1ln(2

1

SZ

X

PP

PC

Capacity from Shannon’s capacity formula

So, no capacity loss due to interference known to the transmitter non-casually

6

Figure: General DPC Channel.

Capacity from DPC capacity formula )1ln(2

1

Z

X

P

PC

• BC is downlink MU-MIMO channel.

• Lack of cooperation between the receivers.

• pre-coding is performed at base station

Multiuser MIMO Broadcast Channel

Figure: MU-MIMO Broadcast Channel.

7

• In MIMO system transmitting and receiving terminals have multiple antennas

• In MU-MIMO antennas at one of the ends of the communication link are no longer co-located.

Base

Station

User 1

User r

1

2

t

Methodology: DPC using TCQ/TCM scheme

8

Figure: TCQ/TCM based DPC implantation.

C1

Rate=k/n

H-1

Channel

Code

(TCM)

Source code (rate k/m TCQ)

PSKn

αS

k bits

(n-k) bits

w n bits

C2

Rate n/mm

u x

-

Methodology: Code C1 (1)

• Rate ½ convolutional code with constraint length 3

9

D Di/p

y1

y2

00

11

11

00

10

01

01

10

00

01

10

11

00

01

10

11

Present State Next State

0 i/p

1 i/p

]11[ 22 DDD G

Figure: rate 1/2 code C1 of DPC encoder.

Figure : Trellis diagram of code C1.

10

1H)(H

0H

1

TT

TG •G is generator matrix

•HT is Syndrome Former (SF) •(H-1)T is inverse syndrome former

DD

DD

D

T

T

1)(H

1

1H

1

2

2

D D

D D

y1

y2S

DS

y1

y2

Methodology: Code C1 (2)

Figure: Syndrome FormerFigure: Inverse Syndrome Former

• For Syndrome former and inverse syndrome former of convolutional code

• Rate 2/3 Trellis coded modulation (TCM)

11

D Dx0

x1

y0

y1

y2

Methodology: Code C2

0

4

2

6

2

60

4

1

5

37

3

3

7

1

5

00

01

10

11

00

01

10

11

Present

state

Next

state

Figure: Rate 2/3 TCM for code C2 of DPC encoder.

Figure: Trellis structure of code C2

• Presence of uncoded bit causes parallel transition in the trellis structure of TCM

• It is maximum likelihood decoding algorithm

• Viterbi decoder is used to decode trellis code.

12

Methodology: Viterbi decoding

BMU ACS SMU

PPM

Figure: Viterbi decoder architecture.

13

D DState i/p

D D

y0

y1

y2

H-1

w

d1

d2

Data inputData bits

Figure: Structure of DPC encoder based on 16 state encoder TCQ/TCM.

Data bits = 01

State i/p = 0

Data bits = 10

State i/p = 0

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Figure : Trellis diagram for DPC encoder for state input ‘0’ and different data bits.

Methodology: DPC encoder(1)

14

Pro

gra

mm

ab

le in

terc

on

ne

ctio

n

State 0

State 15

BMU8 - PSK

BMU8 - PSK

ACS State 0

BMU8 - PSK

BMU8 - PSK

ACS State 15

Interference sequence

Data bits

Figure: Architecture of DPC encoder.

Methodology: DPC encoder (2)

• Performs forward recursion on the trellis, i.e. computing matrices along the trellis

• Once forward recursion is completed final output sequence is obtained by trace back operation

15

Pre

vio

us s

tate

ge

ne

ratio

n u

nit

Output of

survivor path

Data bits

Current state

Survivor

memory

Figure: Architecture for the Trace-Back operation.


• MATLAB ‘cell array’ is used to implement survivor memory.

• Each cell is a 3x1 array

16

3x1

array

3x1

array

3x1

array

3x1

array

3x1

array

3x1

array

3x1

array

3x1

array

3x1

array

0

1

15

Sta

tes

0 1

Stages

(No. of symbols)

Path metric

Survivor path

Data bits

Cell array

A Cell

Figure: Survivor memory Organization.


17

α

Decoder

For C2HY W’

Figure: DPC decoder.

Methodology: DPC decoder

Methodology: Implementation of DPC

for MU-MIMO (1)

18

Figure: MU-MIMO with base station having 3 antennas and 3 users each having single antenna.

TCM

encoder

User 1 data

w1

Pre-coding

B

Channel

H

u1 x1

x2

TCM decoder

(User 1)

DPC decoder

(User 2)

y2

W1'

W2'

z2

Outer coder Inner coder

Transmitter Channel Receiver

DPC

encoder

User 2 data

w2

u2

DPC

encoder

User 3 data

w3

u3 x3

y1

z1

y3

z3

DPC decoder

(User 3)

W3'

19


for MU-MIMO (2)

333232131

323222121

313212111

3

2

1

333231

232221

131211

.

ububub

ububub

ububub

u

u

u

bbb

bbb

bbb

Bux• Transmitted signal

)()()(

)()()(

)()()(

.

333232131333232221213231321211131

333232131233232221212231321211121

333232131133232221211231321211111

333232131

323222121

313212111

333231

232221

131211

ubububhubububhubububh



ububub

ububub

ububub

hhh

hhh

hhh

Hxy

• Received signal

20

For user 1

For user 2


for MU-MIMO (3)

noiseadditive

33313231213112321322121211

signalrequired

13113211211111 )()()( ubhbhbhubhbhbhubhbhbhy

noiseadditive

3332323221321

signalrequired

2322322221221

ceinterferenknown


For user 3

signalrequired

3333323321331

ceinterferenknown


• Received signal can be simplified

Methodology: Pre-coding matrix (1)

• Use LQ-decomposition of channel matrix.

21

LQH

• Select precoding matrix as HQB

Lu

IQQuLQQ

u)(LQ)(Q

Hxy

HH

H

)(

• Then

22

Methodology: Pre-coding matrix (2)

•So

3

2

1

333231

2221

11

3

2

1

.0

00

u

u

u

lll

ll

l

y

y

y

and

signalrequired

333

ceinterferenknown

2321313

signalrequired

222

ceinterferenknown

1212

signalrequired

1111

ulululy

ululy

uly

23

Figure: Gain provided by using DPC.

RESULTS AND DISCUSSION (1)

24


Figure: Comparison of gain provided by 16-state DPC and 64-state DPC.

D DState i/p

D D

y0

y1

y2

H-1

w

d1

d2

Data inputData bits

Figure: 16-state DPC.

D D DD

H-1

D D

y0

y1

y2

d1

d2

u

w

Figure: 64-state DPC.

25

Figure: SNR Vs BER of DPC with full interference presubtraction and PIP.


1

SNR

SNR

PP

P

ZX

X

Total noise power can be reduced by by subtracting only partial interference, αS

26


Figure: SNR vs BER curve for two users MU-MIMO BC.

27


Figure: SNR vs BER curve for three user MU-MIMO BC.

28


Figure: SNR vs BER curve for four user MU-MIMO BC.

CONCLUSION

• DPC based on TCQ/TCM scheme is designed.

• DPC cancels the effect of interference that is known to the encoder.

• Gain provided by DPC increases by using stronger source code and channel code.

• DPC is implemented for MU-MIMO broadcast system.

• In MU-MIMO BC, DPC presubtracts known inter-user interference

• Precoding forces unknown inter-user interference in MU-MIMO to zero.

29

LIMITATION and FUTURE WORK

• This thesis considers MU-MIMO broadcast channel with multiple receivers each having single antenna and base station with number of antennas equal to number of receivers.

• This thesis work can be extended to the MU-MIMO broadcast system with each receiver having multiple antennas.

• Performance can be evaluated when number of antennas at base station is more or less than sum of antennas on all user terminals.

30

REFERENCES (1)[1] M. Costa, "Writing on dirty paper," IEEE Transactions on Information

Theory, vol. 29, no. 3, pp. 439-441, May 1983.

[2] J. Chou, S. Pradhan, and K. Ramchandran, "Turbo coded trellis-based constructions for data embedding: channel coding with side information," Proc. 35th Asilomar Conf. Signals, Syst., Computers, vol. 1, pp. 305 - 309, Nov. 2001.

[3] M. Carrasco, "Design and implementation of multi-user MIMO precodingalgorithms," Department of Electronics and Computer Science, University of Mondragon, P.hd. Thesis November 2011.

[4] R. Zamir, S. Shamai, and U. Erez, "Nested linear/lattice codes for structured multiterminal binning," IEEE Trans. Inform. Theory, vol. 48, no. 6, pp. 1250–1276, June 2002.

[5] W. Yu, D. Varodayan, and J. Cioffi, "Trellis and convolutional precodingfor transmitter-based interference pre-subtraction," IEEE Trans. Commun., vol. 53, no. 7, pp. 1220–1230, July 2005.

[6] Y. Sun et al., "Nested Turbo Codes for the Costa Problem," IEEE transaction on communications, vol. 56, no. 1, Jan. 2008.

[7] P. Bhagawat et al., "An FPGA Implementation of Dirty Paper Precoder," reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings., 2007.

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[8] G. Caire and S. Shamai, "On achievable Throughput of a MultiantennaGaussian Broadcast Channel," IEEE transaction on information theory, vol. 49, no. 7, pp. 1691-1706, July 2003.

[9] S. Pai and B. Rajan, "A Practical Dirty Paper Coding Applicable for Broadcast Channel," Coding and Modulation Lab, Dept of ECE, Indian Institute of Science,Bangalore, Jan 2010.

[10] Dabbagh and D. Love, "Precoding for Multiple Antenna Gaussian Broadcast Channels With Successive Zero-Forcing.," IEEE transaction on signal processing, vol. 55, no. 7, pp. 3837-3850, July 2007.

[11] G. Khani, S. Lasaulce, and J. Dumont, "About the performance of practical dirty paper coding schemes in gaussina MIMO broadcast channels,".

[12] M. UPPAL, "Code design for multiple input multiple output broadcast channels," Office of Graduate Studies of Texas A&M University, M. Sc. Thesis August 2006.

[13] http://www. radio-electornics.com/MIMO Technology Tutorial.

[14] T. Li, "MIMO Broadcast Channel," WAND Lab, Department of Electrical Engineering, University of Notre Dame, April 2002.

[15] M. Hong, "Analysis of the Bit Error Rate of Trellis-coded Modulation.," School of Electrical Engineering, Department of Signals and Systems, Chalmers university of technology, M.Sc. Thesis 2002.

REFERENCES (2)

DIRTY PAPER CODE DESIGN FOR MULTIUSER MIMO BROADCAST CHANNEL

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