Cooperative Diversity for Wireless Networks. Dr. Noha Ossama El-Ganainy Lecturer, Arab Academy of...
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Transcript of 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