cooperative diversity for wireless networks. dr. noha ossama el-ganainy lecturer, arab academy of...

Cooperative Diversity for Wireless Networks.

Dr. Noha Ossama El-GanainyLecturer, Arab Academy of Science and

TechnologyAlexandria, Egypt.

BiographyPhD degree of Electrical Communications,

Faculty of Engineering, Alexandria University, Alexandria, Egypt, 2010.

Worked for different institutions in Egypt.

More than 15 publications in international journals and conferences.

Won the young scientist awards 2011 from URSI GA 2011 “Union Radio Scientific Internationale”

Presentation Outlines.

Developments of cellular systems.

Next generation systems requirements.

Cooperative diversity: the smart

solution.

Discussions and conclusions.

Developments of Cellular Systems.

2G 2.5G 3G 4G.

3G Services: Mobile TV Video on demand Video conferencing Location-based services

2G and 2.5G Services Voice Messaging Image Transmission

4G Services Mobile Internet Ultra Connectivity Adaptive and Smart systems

Next Generation Systems Requirements.

Next generation systems are challenged with the growing demand for high-rate, high-quality wireless services.

Advanced algorithms are recommended to increase the data rate and to guarantee the quality-of-service QOS desired by each media class.

It is also essential to efficiently allocate the network resources to improve the transmission rate and capacity.

Advanced signal processing, adaptive techniques, and using various forms of diversity are highly recommended.

Spatial Diversity Provided independently faded versions of the

same signals at the receiver which enhances the detection.

It combats the channel deteriorations and the deep fades

Results in more efficient performance compared to any other signal processing tool.

MIMO TransmissionsThey provided the spatial diversity but hard to

implement for single terminals .

Widely used and served in the development of a number of communication systems.

Cooperative Communications

Allows single-antenna mobiles to share their antennas in a manner that creates a virtual MIMO systems.

Gain the benefits of MIMO transmissions with no additional cost to the network.

Numerous theoretical models of cooperative signaling were proposed.

Can serve, in aware transmissions, to efficiently use the available network resources.

We are concerned in wireless networks, of cellular or ad-hoc variety, where the wireless terminal increase their quality of service via cooperation.

Historical Background Is a development of the classical concept of Relay

channels introduced by T. A.Cover and El-Gamal in 1979.

Was a model of a three-node networks consisting of a source, a destination, and a relay.

The Relay unique role is to help the source. The capacity was studied under AWGN channel.

While in a cooperative environment the users act as both information sources as well as relays.

The studies are interested in transmission in a fading channel.

Cooperative Communications

USER 1

USER 2

Destination

Independent fading paths

Cooperative CommunicationsCooperative communication provides

independently faded versions of the transmitted signal at the ultimate receiver.

Single-antenna mobiles in a multi-user framework are allowed to share their antennas and generate a virtual multiple- antenna transmitter.

Cooperative Communications Requirements

The base station ties-up a number of users as user-partner, pairs are highlighted.

The base station must separately receive the original and relayed data.

In cellular systems, hardware requirements are essential at the terminals as they receive down-link and up-link transmissions.

Half-Duplex and Full-Duplex.

Different Cooperative Signaling Amplify-and-Forward:

o Each user receives, amplifies, and retransmits a noisy version of the partner’s signal.

o The destination combines the information sent by the user and partner to make a final decision on the transmitted bit.

o The destination must have efficient estimation process to equalize the effect of the inter-user channel. Amplify-and-

Forward

Different Cooperative Signaling

Coded Cooperation:o Integrates cooperation into channel coding,

different portions of each user’s codeword is sent via two independent fading path (users).

o Requires efficient code design.

Different Cooperative Signaling

Decode-and-Forward:oThe partner is assigned to detect/estimate

the user’s signal and forward it to the destination after encoding it.

oThe destination must have access to the inter-user channel coefficient to do optimal decoding.

oAdaptive signaling is possible, at low SNR the partner can switch to non-cooperative mode.

Different Cooperative Signaling

Different Cooperative Signaling

Compress-and-Forward: The partner is allowed to compress

the user’s signal and forward it to the destination without decoding the signal.

Decode-and-Forward Algorithm.

During odd intervals, the user and partner send their information to each other and to the destination. Also, they are assigned to

detect/estimate the partner’s information.

During even intervals, all user’s transmitted signal is a combination of its own data and the partner’s information estimate each spread

by the appropriate code.

Inter-User Channel

The value of Pe12 affects the estimation of the partner’s data which has the potential to control the efficiency of the

cooperation process.

User 1 User 2

b1 1̂bPe12Pe12

Decode-and-Forward Algorithm.

Period Time4321

User 1 Tx

User 2 Tx

11b

31b

31̂b

41b 4

1b

41̂b

42b̂

32b̂

12b

32b

42b 4

2b22b

31b

32b

5 6

21b

Non-Cooperative Cooperative Periods

Odd DurationThe received signal at the destination

during the odd interval is

While the received signal at the partner is

odd

oddoddodd

ZCbaK

ZXKY

111112

1122

odd

oddoddoddodd

ZCbaKCbaK

ZXKXKY

222220111210

220110

Partner detector

During the odd intervals the partner’s estimate and the Pe of the transmitted bit are

1111212

ce

NaKQP

011112011

1ˆ nbaKsignYcN

signb T

c

Even Duration

The received signal at the destination during the even interval is

even

eveneveneveneven

ZCbaKCbaKCbaKCbaK

ZXKXKY

112320222320221310111310

220110

2223

222

221

1213

212

211

1

1

PaaaL

PaaaL

The Receiver ModelThe destination begins by calculating

the soft decision statistics for both intervals

,

which results in

oddT

codd Yc

Nsigny 01

1

evenT

ceven Yc

Nsigny 01

1

eveneven

oddodd

nbaKbaKy

nbaKy

1232011310

11210

The Receiver ModelThe destination combines the information

extracted during both intervals to obtain the transmitted bit

The MAP detector is used to extract b1 given y

The probability of detecting b1 given y is

o

CN

even

odd

y

yy

11

maxarg1̂ bybpb

1

1

1

1 11

byby pp

The Optimal Detector

yve

yve

yve

yve

TTTT

AePeAPAePeAP 2

12

1

12

2

12

1

12

1

1

1

1 11

02220121011101 CNTaKaKaKv

02220121011102 CNTaKaKaKv

The optimal detector is found to be

32exp A

12102 aK 22203 aK

The Sub-Optimum Detector Model

The optimal detector is complex and doesn't have a closed-form expression for the resulting probability of bit error.

A sub-optimal detector ‘modified λ-MRC’ is proposed instead.

The information received during the even duration is waited by .

yaKaKaKsignb 2320131012101̂

Optimum vs Sub-Optimal Detector

For perfect inter-user Pe12 , the optimal detector reduces to the sub-optimal model.

The -MRC is simple and computationally undemanding.

It has a closed form expression which provides a simulation-free analysis.

The -MRC may run in a blind mode, and is may be calculated blindly.

Optimum vs Sub-Optimal Detector

As Pe12 increases, the equivalence between the two models disappears.

For some transmissions conditions, a performance loss will take place.

The Sub-Optimum Detector Model

Matched Filter

Y

oddX~

evenX~

Decision

Channel Estimation

MRC

The Weighting Factor Is used to weight the information received

from the partner before the combining stage.

Is a measure of the destination confidence of the partner’s transmitted bit.

Ranging from 0 to 1 and is dependent on the inter-user channel error Pe12 .

Controls the efficiency of cooperatrion.

The Weighting Factor

The value of Pe12 affects the estimation of the partner’s data which is reflected on the value of the proper .

User 1 User 2

b1 1̂bPe12Pe12

0 1

The Probability of Error

The Pe is given by;

vv

vvQP

vv

vvQPP

T

T

eT

T

ee2

121

121

0

2320131012101 CNTaKaKaKv

0

2320131012102 CNTaKaKaKv

0232013101210 CNTaKaKaKv

The Probability of Error

The destination wants to use the value of that minimizes Pe for given transmission conditions.

The destination may not have access to the value of Pe12 , an adaptive estimation and feedback from the users is essential.

For given transmission conditions, the maximum possible performance is found by making use of an “optimal” value of (found) numerically.

Pe vs Pe12

The performance analysis of the cooperative algorithm in terms of the probability of error for different values of inter-user channel

-2 0 2 4 6 8 1010

-4

10-3

10-2

10-1

Pro

babi

lity

of E

rror

- P

e

Theoritical Probability of Error Performance

Pe12=0.5- Theoritical Performance

Pe12=0.1

Pe12=0.05Pe12=0.005

Pe12=0.0001

To Cooperate or Not to Cooperate?

0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.02

10-1

100

Threshold

Pr

of

Ou

tag

e

Coop

Non-Coop

To Cooperate or Not to Cooperate?

Power Tradeoff More power is may be needed to provide

cooperation? The baseline power will be reduced due to diversity. Smart power allocation is used to efficiently utilize

the power resources. Rate Tradeoff Is cooperation causing losses of rate in the system? Due to the spectral efficiency improvement, the

channel code rates is may be increased.Cost Is positively approved by several studies.

Discussions and Conclusions

The cooperative communications concept provides the benefits of MIMO transmission at no additional cost to the network.

It provides higher capacity and enhanced throughput compared to non-cooperative transmissions.

It efficiently allocates the network resources which improves the network capabilities and enhances the overall performance.

Discussions and Conclusions

Decreased sensitivity to channel variations.

Security the user’s data has to be encrypted before

transmission, the partner can detect the user’s data without understanding it.

Complexity of Mobile Receiver Increased security, signal separation.

How to decide the partnership? Partners assignments and

reassignments

References

A. Nosratinia, T. Hunter, and A. Hedayat, “Cooperation Communication in Wireless

Networks,” IEEE Communication Magazine, October 2004, pp. 74–80.

Noha O. El-Ganainy and Said E. El-Khamy, “A New Practical Receiver for a Decode-and-

Forward Cooperative CDMA Systems based on a Blind λ-Combiner,”

Progress in Electromagnetic Research Letters PIERL, Issue #28, page 23-36, 2012.

Thank You