COOPERATIVE COMMUNICATIONS COMBINATION DIVERSITY TECHNIQUES AND OPTIMAL POWER ALLOCATION EXPRESSION

International Journal of Applied Control, Electrical and Electronics Engineering (IJACEEE) Vol 3, No.4, November 2015
DOI: 10.5121/ijaceee.2015.3401 1
COOPERATIVE COMMUNICATIONS
COMBINATION DIVERSITY TECHNIQUES
AND OPTIMAL POWER ALLOCATION
EXPRESSION
Ahmed Hassan Mohammed Hassan1
, Ahmed M.Alhassan2
Electrical Engineering Dept., Faculty of Engineering1
, University of Blue Nile, Sudan
Communication Dept., Faculty of Engineering2
, Neelain University2
, Sudan
ABSTRACT
The main task of this article is to focus on the performance of cooperative MIMO relaying in terms of data
rate and Power. Furthermore, compare these performances when using Maximum Ratio Combining (MRC)
and equal gain combining (EGC).The average SNR improvement of MRC is typically about 5 dB better
than with EGC and direct link.The preciseness of the derived closed form expression of optimum power
allocation of the DF-based relaying system is demonstrated by simulation results.
KEY WORDS
Cooperative Communication, Amplify and forward, Decode and forward, optimum power allocation
1. INTRODUCTION
The idea of cooperative communication for wireless networks can be traced back to utilize of the
relay channel. Cover and El Gamal [1], studied a three-node network with a source, a destination
and a relay on the information theoretic properties. By the use of an additional relay node the
network capacity was well observed and three theorems were established. With the current
growth in cellular networks, sensor networks, and wireless ad hoc networks, the opinion of
cooperative communication has attracted tremendous attention. However, recent studies generally
focus on the diversity, spectral efficiency and power efficiency achieved by cooperative
transmission to combat channel fading instead of the information theoretic perspective.
Furthermore, with higher demand on the size of handset and sensors, current research develops a
scenario where single-antenna mobiles share their antennas in a virtual multiple-antenna system.
Each node could perform both as a source or a relay of other nodes which are symmetrical to each
other as shown in Fig.1

2
Fig.1: Symmetrical Transmission System
The two different emphases in recent work make cooperative communication and networking a
promising technology with significant capacity and multiplexing gain increase in wireless
networks. Although a number of researchers demonstrated the understanding of different
cooperative communication schemes in recent years, many more issues such as the cooperative
system architecture, outage probability, optimization, resource allocation, cross-layer design are
open to discuss to make these techniques practical and effective. In this paper, we consider a
three-node scenario consisting of one source, one relay and one destination which is the simplest
form in cooperative system as in fig-2.
Speedy, stable and good quality communication is significant in the information age. Citizens
want improved voice or video quality, wider coverage while smaller and more power and
bandwidth efficient handsets from every corner in the world. Overcoming the effects of fading,
outages, and circuit failures is always a large alarm in wireless communications contrast to fiber,
cable and other medium transmissions. One solution is to use send out diversity where identical
information-bearing signals are transmitted from independent sources through independent
channels. The more independent the fading characteristics between channels the more diversity
gain are achieved at the receiver. Transmit diversity is practical, effective and economical in
mitigating multipath fading compared with other techniques such as transmitter power control,
time and frequency diversity and receive diversity approaches [2].
A number of schemes for transmit diversity are recommended in cellular systems. Coding, time
and frequency are employed by the transmitters to produce diversity gain [2], [3]. Although the
pro of transmit diversity on a cellular base station is evident, it may not apply for other
approaches where the system cannot support various transmit antennas due to size, charge, or
hardware restriction.
To overcome this obstacle cooperative transmission is proposed. The technique allows a only one
antenna user to gain diversity similar to conventional transmit diversity systems to conflict slow
fading. The essential structure block in cooperative systems is the relay channel, whereby a
source transmits a message to object (destination) with the aid of a relay. Thus, the destination
receives two messages with the equivalent source but through independent fading channels. By
joining these signals with combination techniques like a MRC or EGC, the diversity gain can be
obtained without using additional antennas, power or bandwidths, and thus cost-effective[4]. Our
purpose is to quantify the advantages of using cooperative transmissions in expand the network
lifetime of the energy-constrained wireless network [5].

3
The reset of this article is arranged as follow: section-2 provides the general description of
signaling method of cooperative communication. The system model is presented in section-3. In
section-4 the power allocation methods are provided. The system simulation and results are
presented in section-5.
2. RECENT COOPERATIVE SIGNALING SCHEMES
In the essential category of cooperative transmission, the whole transmission time can be
separated into two time slots. In the initial/first time slot, the source transmits a signal to the relay
and the source. In the second time slot, the relay makes a decision, how to reply to the received
signal. Numerous cooperative signaling methods are developed with diverse signal processing
strategies at the relay [6-8].
i .Decode-and-forward
In the decode-and-forward (DF) mode, the relay decodes, re-encodes and retransmits the full
information to provide the destination edition of identical information-bearing signal as the
source to achieve the second order diversity. This signaling is similar to the traditional sense of a
relay channel and has the adaptability to channel conditions. However, when the detection by the
relay is failed, the information from the relay channel will be harmful to the detection at the
destination, which is called error propagation.
ii. Amplify-and-forward
Another straightforward cooperative technique is the amplify-and-forward (AF) relaying. In this
method the relay simply amplifies and forwards the noisy version of the signal transmitted by the
source. Interestingly, it has been shown in [9] that by optimally combining the signals received
from the source and relay with the necessary CSI available at the destination, this method
achieves second order diversity, which is full diversity for the two-user case. Amplify-and-
forward does not require the relay to decode the source's transmission, which is a major
advantage over decode-and-forward. Souryal in [10] proposed a new hybrid AF/DF relaying
protocol in which the relay uses CRC to detects whether the decoding is successful. If so, it re-
encodes and transmits the message as in the fixed DF method.
3. SYSTEM MODEL
A source node-A transmits information to the destination node-B with the help of a relay node R
as shown in Fig.2. For the transmission, time division multiplexing is assumed, in the first time
slot the source broadcasts the information to both relay and destination. In the second time slot
the relay decodes and forwards the received information to the destination. At the destination, the
signal from the relay path and the direct path are combined to reduce the fading of the resultant
signal. Hard-decision-decoding is processed for any decoding process.

4
Fig.2: System Model
The two stages of the transmission process are:
Stage-1: The signal received at destinationfrom the source is given by: (i.e, direct link)
( )D s sd sdy P h x n= + (1)
y is the received symbol, sP is the Power at the transmitter, h is complex scaling factor
corresponding to Rayleigh multipath channel, x is the transmitted symbol (taking values +1’s and
-1’s) and n is the Additive White Gaussian Noise (AWGN).The noise n has the Gaussian
probability density function with ( )
2
2
( )
2 2
( ) 1 2
n
p n e
µ
σ
πσ
− −
= with zero mean ( µ =0) and 2
σ as its
variance.
The channel h is known at the receiver and Equalization is performed at the receiver by dividing
the received symbol y by the apriori known channel
s SD SD SD
D
s SD s SD
P h x n n
y x
P h P h
+
= = +
)
x n= +
)
(2)
Where ˆn is the additive noise scaled by the channel coefficient.
Stage-2: The signal received at the relay from the source is given as:
. .r s sr s r
y P h x n= + (3)
The equation of yr is equalized at the relay to generate a new signal . srn P hr ss
x +

5
Stage-3:This signal is sent by relay to the destination, the received signal by:
Re _
)
.
P . .(SR
SR
D R
lay Signal
n
R
s DP hs
y h x n+= +
14243
(4)
With the SNR given by:
1
2
2
2
2
P .
P
(1 )
R RD
R RD
S SR
h
h
P h
γ
σ
=
+
(5)
As the source broadcasts the signal in the first time slot, the signal from the direct path is also
received at the destination with the SNR equal to 2 2
2 P .s sdhγ σ= .
We assume that full CSI at
both paths is available at the destination, so coherent combining is possible in such way the
overall SNR at the destination can be written as 1 2Dγ γ γ= +
1
2
2
2
2
2P .P .
2
P
(1 )
2
hh RS SD RD
D hR RD
PS hSR
γ
γ
σ
σ
γ +
+
=
14243
14243
(6)
4. POWER ALLOCATION METHOD
This section provides the formulation problem which will be examined through simulation
studies. We consider the approach of a system in which individual node has upper bound of
energy (i.e. limited power wireless terminals).
T S RP P P= + (7)
Where PS is the Power allocated to the source and PR the relay Power.
These workstations works in network of cellular type or works in unlicensed band (To prevent
interfering with other networks in the same band, the total power radiated by network in this band
should not exceed a specified level/threshold). Such types of network have upper limitation on
total power transmission. So transmitted power by source is given by:
, 0 1S TP Pλ λ= < < (8)

6
Transmitted power by supporting relay is given by rP (1 ) TPλ= − .
Ergodic capacity for cooperative communication is given as 2
1
.log (1 )
2
DC B γ= + . In the
equation, the right hand side is multiplied by a factor of 1/2, this is due to the fact that proposed
system model works in two time slots and utilize only half channel degree of freedom.
4.1 Optimum Power Allocation
This is a centralized power allocation method in which source should have full information
knowledge or CSI between all nodes prior to transmission. In practice, the channels are estimated
by sending guidance sequence before the actual message transmission, when each node operates
in time mode or with TDMA technique. When the source transmits the training bits, relay node
can simultaneously estimate their source-to-relay CSI due to the broadcast nature of the wireless
medium. Similarly, when relay (R) transmits the training bits, the CSI of source-to-relay and
relay-to-destination can be estimated at the source and destination respectively (we assume that
forward and backward channels between the relay and destination are the same due to
reciprocity). These transmissions occur on the same frequency band and same coherence interval.
However, CSI of relay-to-destination can be obtainable at the source only through channel
feedback.
In a slow fading setting, frequent training is not necessary. Hence, in this case, we can neglect
training period as compared to actual data transmission period. On the basis of CSI, S distributes
the available power (PT) between S and R. So our main objective is efficiently utilizing the
available power to improve Dγ at destination. Maximizing the equation of Dγ can be written as:
2
2
P ,
2 2
2
(1 )
2
to
max
R S
D
P
T S R
P h P hS RSD RD
hPR RD
PS hSR
subject P P P
σ
σ
γ +
+
=
= +
(9)
Via Lagrange multiplier maximization scenario, the modified objective function can be written as
2 2
2
2
2 2P
(P )
P
(1 )RD
SR
P h hS RSD RD P P
R S ThR
PS h
j λ
σ
σ
+ + −
+
= (10)
Here λ is a constant. Taking partial derivative of jwith respectively PS, PR, and λ equated to zero
gives optimal solution for PS and PR.

7
Then, optimal solution for PS and PRcan be given as
2
1
2
2
2
1
2
2
( [ ] )
P (1 [ [ ] ])
S T
R T
W W N
P P
Q Q Q
W W N
P
Q Q Q

= − − 


= − − −

(11)
where,
2 2 2
2 2 2
2 2 2 2 2 2 2
( )
( ) 2
RD SD SR
RD SR SD
RD SR SD RD SR RD SD
N h h h
W h N h h
Q h N h h h h h h
= +


 = −  

 = − − −  
(12)
5. SIMULATION ANALYSIS RESULTS
The simulation analysis conveys BER and power allocation. To evaluate the true performance of
the proposed scheme, computer simulation is conducted. The results confirm the advantages and
disadvantages of the proposed mathematical expression through the following analysis of the bit
error rate (BER) curves.
Cooperation between nodes can have a mutual positive pro for both nodes. However, there are
cases where only one of the partners takes all the advantages of cooperation. Hence, it would be
of attention to examine and estimate the mutual outlook of cooperation and explore its benefits to
each of the partners. The purpose would be to see when it is useful for nodes to cooperate and
when it is not. For a given total power, we have analyzed and provided regions (between source-
relay and relay-destination) in which cooperation enhances the performance. Suppose that
propagation attenuation exponent is 4. We fix the location of the source and the destination node
and analyze regions where cooperation can be useful and the quantity of improvement that can be
achieved when two nodes are assisting.
In all the simulation it’s assumed that the variance of the noise is unit (i.e. No =1), White
Gaussian Noise (AWGN) and Rayleigh Channel. Where the decoding process used is called
equalization,this led to a full knowledge of the CSI.
Fig.3 compares the BER versus the SNR for our theoretical analysis when Maximum Ratio
Combining (MRC) and Equal Gain Combining (EGC) are considered at the destination. For both
channels, BPSK modulation is considered; equalization and hard-decision-decoding are
performed at any receiver as the decoding process.

8
Fig.3: BER versus SNR for EGC, MRC
Fig.3 shows that cooperative communication allows single antenna users to gain diversity similar
as in conventional transmits diversity systems to combat slow fading. By combining the signals
from both relayed and direct path with combination techniques like a MRC or an EGC, the
diversity gain can be obtained without using extra antennas, power or bandwidths, and thus a
cost-effective solution. As both EGC and MRC achieve the same asymptotic diversity, it is clear
that EGC, which does not need channel gain information, offers a superior tradeoff between
difficulty and performance.
From Fig. 3, we notice that MRC outperforms the EGC for this decoding technique when the
average SNR less than 30 dB. The average SNR improvement of MRC is typically about 5 dB
better than with EGC and direct link.
In Fig.4 and Fig.5, we simulate different ratios of Ps and Pr with two unlike total power available
(PT=100dB and PT=300dB). We can observe that, BER versus power distribution, under different
total power constraints in the single relay node case, attain unique minima. Furthermore, it can be
concluded that under different total power constraints, the optimal transmission power schemes
are dissimilar. In case of the total transmission power is small; the BER is not very sensitive to
the source and the relay power distribution. When the total transmission power is relatively larger,
which means that the BER at the destination could be somewhat small, the BER performance is
aware to the power distribution. The source should spend significantly more power than the relay.
Alternatively, the relay should keep a lot of power. Although PR is small, it provides cooperative
diversity at the destination.
The ratio PS/PT gives thought on the relay position, we assume that a source node needs more
power when the destination is getting far away from it (i.e., a node require extra power when
extended transmission distance).
0 5 10 15 20 25 30
10
-5
10
-4
10
-3
10
-2
10
-1
10
0
Eb/No, dB
BitErrorRate
Direct Link
MRC
EGC

9
We can see that under different available power (in dB), the performance of the communication
depends on the power allocated to both source and relay; because the less the BER, the less the
noise will be. So we can expect a better communication link
When the power ratio /s TP P is less than 0.4 the relay is far away from the destination, more
power needs to be allocated to it to assure that the transmission from relay to destination is
successful. In case of the relay node in the middle position between the source and destination the
power ration is between 0.5 and 0.6. The BER becomes lower and we can expect a better
communication as the noise is reduced.
When the power ratio is greater than 0.6 the relay is far away from the source, the source needs
more power to transmit the information to the relay. The BER gets higher, the noise power is
increased. In this situation a reliable communication link may not be feasible. Furthermore, we
observe that we have two situations:Whether the relay is closer to the source or destination and
the situation where it’s in the middle of them. In the first situation the power allocation is called
Optimum Power Allocation method (OPA) and the other called Equal Power Allocation method
(EPA).
Fig.4 and Fig.5 are illustrates that the OPA outperforms the EPA methods when the total power
available is small. Their BER are almost the same when the power ratio is between 0.6 and 0.8
with a greater total power. When the power increase, the OPA method can really bring about BER
performance improvement when the relay is close to the destination.
Fig.4: BER as a function of the source power ratio with PT=100dB
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0.25
0.3
0.35
0.4
0.45
0.5
Ps/PT
BER

10
Fig.5: BER as a function of the source power ratio with PT=300dB
6. CONCLUSION
We have presented in this paper a power allocation method and comparison of BER between
EGC and MRC. By introducing cooperation protocol among nodes, both energy pro and location
advantage can be explored such that the device lifetime is improved and diversity is achieved.
First, decode-and-forward cooperation protocol is employed among nodes. We discuss at which
position the node should cooperate and how much power should be allocated for cooperation. An
optimization issue is formulated with an aim to maximize the SNR device lifetime under a total
power constraint.
The cooperation scheme is proposed as follow: an optimum power should be given to the source
according to the relay position; the relay regenerates the received signal by equalization and hard-
decision-decoding and forwards it to the receiver which will do the same process to decode the
information. The power given to the relay will also be optimized in order to get a reliable
communication link.
It can be observed that the performance differs according to the total power available, and
according to the combining technique used. The power allocation method is resumed into two
methods; the OPA method and the EPA method. According to the position of the relay each one
performs better than the other, better transmission link, long communication time may be
expectable if the optimum method is chosen and may also be helpful for the resource
management.
Future work may include solving the optimization in particular scenarios, development of a
selective strategy to circumvent limitations due to link source-relay, extension to multi-hop
transmission, in particular assessing how diversity can improve performances.
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0.25
0.3
0.35
0.4
0.45
0.5
Ps/PT
BER

11
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Vehicular Technology, IEEE Transactions on, vol. 47, pp. 119-123, 1998.
[3] G. W. Wornell and M. D. Trott, "Efficient signal processing techniques for exploiting transmit
antenna diversity on fading channels," Signal Processing, IEEE Transactions on, vol. 45, pp. 191-205,
1997.
[4] F. Gomez-Cuba, R. Asorey-Cacheda, and F. J. Gonzalez-Castano, "A survey on cooperative diversity
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[7] A. Meier and J. S. Thompson, "Cooperative diversity in wireless networks," in 3G and Beyond, 2005
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[8] Y.-W. P. Hong, W.-J. Huang, and C.-C. J. Kuo, Cooperative communications and networking:
technologies and system design: Springer Science & Business Media, 2010.
[9] J. N. Laneman, G. W. Wornell, and D. N. Tse, "An efficient protocol for realizing cooperative
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