Research Journal of Applied Sciences, Engineering and Technology 4(20): 3958-3964, 2012
ISSN: 2040-7467
© Maxwell Scientific Organization, 2012
Submitted: December 20, 2011 Accepted: April 20, 2012 Published: October 15, 2012
Abstract: The analysis the outage performance of suggested Cognitive Wireless Relay Network (CWRN) which consists of a source, a destination and M cognitive relay nodes and M primary user nodes. In this study, we mainly study that the cognitive relay nodes impact on the outage performance of proposed CWRN over
Rayleigh channels under both the peak and average interference power constraints. Firstly, considering a scenario with a single cognitive relay node, the optimal power control policies of the cognitive relay nodes are derived in order to achieve the minimum system outage probability under different interference power constraints. Then, to further improve outage performance, a scenario with multi-relay is also studied. Finally, the numerical simulations are presented. Numerical results show that substantial performance improves with the increase of the number of cognitive relay nodes and the power control policy under the average interference power constraint is more effective than that under the peak interference power constraint.
Keywords: Cognitive radio, cognitive relay networks, outage performance, power control
INTRODUCTION
To deal with the conflicts between spectrum congestion and spectrum under-utilization, cognitive radio has been considered as an efficient approach to improve the spectrum utilization by spectrum sharing between
Primary Users (PUs) and cognitive radio users Mitola and
Maguire (1999). This fundamental requirement of sharing is to avoid harmful interference to potential PUs. To implement without interference to PUs, cognitive user needs to control its transmission power so as not to cause any harmful interference to the active primary users
(Wang and Liu, 2011; Akyildiz et al ., 2011).
On the other hand, relay wireless communications have received more and more attentions due to outperformance over conventional point-to-point transmissions (Louie et al ., 2009; Laneman et al ., 2004).
Inspired by cognitive radios and relay networks, we suggest a cognitive wireless relay network, combining the idea of cognitive radio with the relay networks, which is defined by a source node, a destination node and M cognitive relay nodes and M primary user nodes. A source is assisted by cognitive relay nodes which allow coexisting with primary user nodes subject to interference power constrains so that they do not cause harmful interference to PUs. In this study, we mainly focus on understanding that the cognitive relay nodes impact on the system outage performance over Rayleigh environments.
Firstly, considering a scenario with a single relay node, to achieve the minimum outage probability under both the peak and average interference power constraints, the optimal power control strategies of the cognitive relay node are derived. To further improve outage performance, we also derive the relationship between the interference power constraints and the outage probabilities under a scenario with multi-relay, which exploits the spatial diversity to improve performance.
The study of outage performance under an interference power constraint has attracted attentions in cognitive radio networks recently (Kang et al ., 2008; Lee and Yener, 2006; Suraweera et al ., 2008). Kang et al .,
(2008) derived the optimal power allocation strategies to achieve the minimum outage probability in a spectrumsharing model which consists of one PU and one SU.
However, the extension of these algorithms to cognitive relay network is not obvious. Lee and Yener (2006) studied the approximate outage probability of a repetitionbased cognitive relay network in the high Signal-to-Noise
Ratio (SNR) regime under overlay fashion. The cognitive nodes relay information for the source node in an opportunistic fashion by acquiring spectrum unused by a primary node. Their main emphasis was to analyze the achievable diversity order of the system subject to cognitive relays being able to successfully acquire spectrum. In contrast to the standard relay scenario, cognitive relays will only transmit if they are successful
in obtaining spectrum hole. Suraweera et al . (2008) considered the same cognitive relay network architecture described in Lee and Yener (2006) and derived
exact expressions for the outage probability of the
Corresponding Author: Wenjing Yue, Key Lab of Broadband Wireless Communication and Sensor Network Technology,
Nanjing University of Posts and Telecommunication, Nanjng, China
3958

repetition-based scheme and selection cooperation.
Additionally, cognitive relays could also be deployed as a means of minimizing the interference caused by secondary transmissions to the primary licensee, while guaranteeing reliable communications for the secondary users (Hamdi and Letaief, 2007). An ad-hoc cognitive radio concept, in which transceivers with small power and multi-hop communications are used for expanding the service area has been proposed in Fujii and Suzuki
(2005). However, recent research on the combination of relaying and cognitive radio techniques mainly focus on opportunistic cognitive relay network which cognitive nodes will only relay information for the source node if they are successful in obtaining spectrum unused by a primary node. We propose cognitive relay networks which a cognitive relay node is designed to allow the coexistence with a PU and study power control policies of this system to minimize the outage probability subject to interference constraints of primary users. In this study we
C
Propose a cognitive relay network model which a cognitive relay node is designed to allow the coexistence with a primary user node.
C Include the impact of the cognitive relay nodes on the outage performance of proposed system model and provide a thorough performance analysis under both the peak and average interference power constraints over Rayleigh fading channels.
In this study, we suggest a cognitive wireless relay network model. We mainly focus on understanding that the effect of the cognitive relay node on the proposed model has been studied by evaluating the outage probability in Rayleigh environments. For both the peak and average interference power constraints, we derived closed-form expressions for outage probability under M relay nodes, respectively. Finally, numerical performance results show that substantial performance improves with the increase of the number of cognitive relay nodes and the power control policy under the average interference
Source
Relay 1
Primary node 1
Res. J. Appl. Sci. Eng. Technol., 4(20): 3958-3964, 2012
Relay M
Primary node
Desti nation
Fig. 1: Proposed cognitive wireless relay network power constraint is more effective than that under the peak interference power constraint.
METHODOLOGY
System model: In this study, proposed cognitive wireless relay network consists of a source, a destination, M cognitive relay nodes and M primary user nodes, as shown in Fig. 1. The cognitive relay nodes are cognitive
(secondary) users.
The cognitive node relays information for the source node in underlay approach which allows the coexistence with a primary user node by imposing severe constraints on the transmission power so that it operates below the noise floor of primary user node. Assume that the transmission of PUs is orthogonal, either through time or frequency division. A cognitive relay is only allowed to coexist with a primary user node, as shown dashed circle in Fig. 1. Therefore, the transmission of the relay nodes to the destination is also orthogonal because the acquired time slots or spectra from different primary nodes are non-overlapping. All M relays help the source and (M+1) orthogonal channels are used for transmission. For orthogonal channels, the destination receives (M+1) copied of the source symbol from the source and the relay nodes with no interference among each other. Different relay schemes have been investigated in literatures
(Laneman et al ., 2004; Sadek et al ., 2007). Here, we consider a Decode-and-Forward (DF) relaying scheme, in which the relay decodes the received signal, re-encodes it and transmits the signals to the destination. Let us assume that the wireless links between any two nodes are modeled as flat fading channels and the channel fade coefficients for different links are statistically independent.
The communication consists of two transmission stages. In the first stage, the source broadcasts to its destination and relay nodes, as depicted real line in Fig. 1.
During the second stage, the relay node decodes the received signal, re-encodes and transmits it to the destination node, as depicted dashed line in Fig. 1.
Therefore, during the first stage, the destination and received signals at the ith relay are respectively given by:
Y sd
= h sd
X s
+Z sd
, Y sri
= h sri
X s
+Z sri
(1) where, X s is the transmitted signal from the source node.
During the second stage, the received signal at the destination from the ith relay node is:
Y rid
= h rid
X ri
+Z rid
(2) where, X ri is the transmitted signal from the ith relay node.
Z sri
, Z sd and Z rid are modeled as mutually independent, circularly symmetric complex Gaussian random variables
3959

Res. J. Appl. Sci. Eng. Technol., 4(20): 3958-3964, 2012 with zero-mean and variance N
0
. h sri
, h sd
, h rip and are the annel fading coefficients for source-relay links, sourcedestination links, relay-destination links and relay-primary links respectively. In the following discussions, for simplicity we denote the channel power gains are g0-
|h sd
| 2 ,gsri = |h sri
| 2 , grid|h rid
| 2 and grip = |h rip
| 2 exponentially distributed with parameters
8 which follow
0
,
8 sr
,
8 rd
and
8 rp in Rayleigh fading channels, respectively. Assume that
Channel Side Information (CSI) is available at both the cognitive relay nodes and the destination. We then denote the instantaneous power adaption at the i th cognitive relay node as P i
(g rip
, g rid
) and the source transmits with power
P s
. Then, we define the instantaneous received SNR at the i th relay and the destination as:
γ sr i
= g P i s
N
0
,
γ sd
= g P s
N
0
,
( rip
g
)
(3)
N
0
Outage performance analysis: Outage probability is one of the most commonly used performance measures in wireless communication systems. For a general communication system, the outage occurs when the mutual information ( I ) falls below a target value R.
Therefore, for a given transmission rate R, the outage probability is defined as:
P out
= Pr{I<R} (4) where Pr{.} denotes the probability.
The mutual information between the source and the i th relay node, i = 1, …, M , is given by:
I i
=
1
2 log
(
1
+
sr i
)
(5) where all logarithms are base 2. The availability of a relay node to assist the source to destination communication, depends on the reliability of that source to relay transmission link. However, in this study, we mainly focus on understanding that the power control policies of the cognitive relay nodes impact on the proposed cognitive relay network model. Hence, we assume that the mutual information of a source-relay link, I i
, is greater than the target rate, R . That is, the relay can reliably decode the source message.
Then, the average mutual information of the DF relaying scheme between the source and the destination becomes:
I
=
M
1
+
1 log
⎜
⎛
⎝
1
+ γ sd
+ i
M
=
1
γ ⎟
⎞
⎠
(6)
Minimum outage probability is a concept closely related to the outage capacity. The outage capacity is defined as the maximum rate that can be maintained throughput with some outage probability. Mathematically, the problem is defined to find the optimal power control policy to achieve the maximum rate for given outage probability. This is equivalent to obtain the optimal power control policy that minimizes the outage probability. That is, for a given R, we can adapt the transmitted power of the cognitive relay node to minimize the outage probability in M -relay case, which is written as:
⎧
⎨
⎩ M
1
+
1
⎛
⎝
⎜
1
+ γ sd
+ i
M
=
1
γ
⎞
⎠
⎟ <
R
⎫
⎬
⎭
In the following, we will consider the above problem under the peak interference power constraint and the average interference power constraint, respectively.
Peak interference power constraint under a single relay node: If M = 1, there is a single relay node. In this scenario, the minimization problem is formulated as:
⎩⎪
Minimize Pr
⎧
⎨
⎩⎪
1
2 log
⎜
⎛
⎝
1
+ g P s
N
0
+ g P
1
N
0
⎞
⎠
⎟ <
R
⎪
⎭⎪
{ subject to g P
1
≤
Q pk
(8)
(9) where, Q pk
denotes the peak interference power limitation at the primary node and we have dropped the (g rip
, g rid
) in the above equations for description simplicity.
In order to minimize the outage probability, the problem is converted to find the maximum value of P
1 under the peak interference power constraint. Clearly, the optimal power control is obtained as by substituting P
1
=
Q pk
/g rip into (8):
Pr
⎩⎪ g s
Q pk
+ g P s
<
N
0
( 2 2 R
−
1 )
⎭⎪ g
Pr
⎩⎪ g
<
N
0
( 2 2 R
−
1 )
−
Q pk g P s
⎭⎪
(10)
Further we assume that CSI between the source and the destination is available at the source. Thus, the received power g
0
P s
at the destination from the sourcedestination link can be guaranteed a constant. Thus, (10) can be rewritten as:
(11) where, M is the number of cognitive relay nodes.
Since g rid
and g rip are mutually independent, the probability density function (pdf) of g rid
/g rip is given (12):
3960

Res. J. Appl. Sci. Eng. Technol., 4(20): 3958-3964, 2012 f g
( )
= g
(
λ rd
λ λ rd rp x
+ λ rp
2
) , x
≥
0 (12) g g
<
N
0
( 2
2 R a
β
−
1 )
− g P s
Since the outage will occur when (11) is satisfied, the minimum outage probability for DF is given by:
Pout
0
N
0
( 2
2 R
Q pk g P s f gr
1 d
g
rp
Q pk
rp
Q pk
rd
N
0
2 R
g P s
(13)
(17) where,
"$
is determined by substituting (17) into E[g rid
P
= Q av
. Since the pdf of g rid
/g rid
is the same as g rid
/g rip
1
]
for
Rayleigh fading channels, which has given in (12). Then, we get:
0
N
0
( 2
2 R
αβ g P s xf g g
=
N
0
( 2
2 R
Q av
−
1 )
− g P s
(18) where, x denotes g rip
/g rid
, and (f grip
/g r1d)
(x) denotes the pdf of g rip
/g rid
.
Since the outage will not occur only when (17) is satisfied, the outage probability can be calculated as:
Average interference power constraint under a singel relay node: In this subsection, we consider a more interesting and challenging problem, the outage probability under the average interference power constraint.
In such scenario, the problem can be formulated as:
P out
= −
⎢
⎢
⎣
⎡
1 Pr
⎜⎜
⎛
⎝ g g
<
N
0
( 2
2 R −
1 )
− g P s
⎟⎟
⎞
⎠
⎥
⎤
⎦
⎥
(19)
⎩⎪
Minimize Pr
⎧
⎨
⎩⎪
1
2 log
⎜
⎛
⎝
1
+ g P s
N
0
+ g P
1
N
0
⎞
⎠
⎟ <
R
⎭⎪
(14)
{ subject to E g P
1
Q av
(15) where Q av
denotes the average interference power limitation at the primary node and E[.] denotes expectation operator.
Suppose there is no power constraint, the minimum power required to support a given transmission rate R is given by P
1
= [N
0
(2 2R -1) !
g
0
P s
]/ grid
. Since the total average interference power is constrained, relay has to control its transmission power to support R without causing too much interference to PU. Thus, relay should make better use of the average interference power that PU can tolerate. From (15), if P
1
is very big, cognitive relay node will cause interference to primary user node. Therefore, we should turn off transmission when P threshold
α
. Besides, if g rid
1
is larger than a
is very large (i.e. the channel between relay node and primary node is very good), cognitive relay node will also cause interference to primary user node. Thus, we should also turn off the transmission when g rip
is larger than a threshold
$
.
Therefore, the optimal power control policy for above problem becomes:
P
1
=
N
0
( 2 2 R
−
1 )
− g g P s
, 0
<
P
1
<
,
< β
(16)
[N
0
Note that 0<P
1
<a can be rewritten as g rid
>
(2 2R !
1)
!
g
0
P s
]/a and since g rid
<
$
, we have:
"$
/[N
0
"$
Note that, in order to get P out
(2 2R -1)-g
0
P s
. Thus, we can denote parameter n
.
, what we need is
]. Thus we need not bother to solve for
"$
/[N
0
(2 2R -1)-g
0
P s
] as a new
Substituting the pdf of g rip
/g rid
into (18), we get: ϕ = −
λ
λ rd rp
⎪
⎩
⎧
⎪
⎨
⎢
⎣
⎢
⎢
⎡
W
⎜
⎝
⎜
⎜
⎛
− e
λ rp
λ rd
N
0
( 2
2 R
Q av
−
1 )
− g P s ⎟
⎠
⎟
⎟
⎞
⎥
⎦
⎥
⎥
⎤ −
1
+
1
⎫
⎪
⎬
⎪
⎭
(20) where W(x) is the Lambert-W function, and it is defined as the inverse function of f(w) = we w .
Thus, the minimum outage probability is obtained as:
P out
=
⎢
⎣ ⎢
⎡
⎢
1
−
0 ϕ
(
λ rp
λ λ rd x
+ λ rd
)
2 dx ⎥
⎦ ⎥
⎥
⎤
=
λ rd
+ rd
(21)
Peak interference power constraint under multi-relay:
Up to this point we have discussed outage performance for our suggested system model under a single relay node.
To compare the spatial diversity gains, we consider a scenario with multi-relay, i.e., M
≥
2.
Thus, the minimum outage probability is written as in such scenario:
⎧
⎪
⎨
⎪
Minimize Pr
⎨
⎪
⎧
⎪
M
1
+
1 log
⎜
⎜
⎛
1
⎜
⎝
⎜
+ g P
N
+
.
K +
0 s + g P
N
0 g P
M
N
0
1
⎟
⎟
⎟
⎞
⎟
⎠
<
R
⎬
⎪
⎫
⎪
(22)
3961

i
M ∑ g
>
=
1 g max i g g for given the peak interference power limitation Q pk and the transmission rate R, an approximation to the outage probability in this case becomes:
P out
=
⎛
1
−
⎝
⎜⎜
λ rp
λ rp z
0
+ λ rp
⎟⎟
⎞
⎠
M z
0
[ N
0
(
Res. J. Appl. Sci. Eng. Technol., 4(20): 3958-3964, 2012 pk
1 pk
M
(23) where, P(i = 1, 2, …, M) denotes transmission power for the ith relay node and Qpk i denotes peak interference power limitation at the ith primary node.
Obviously, the objective function is minimized when
P i
= Q pki we get:
/g rip
(i = 1, 2, …, M). Substituting these into (22),
Pr
⎩⎪ g g
Q pk
1 g g
Q pk
M
<
N
0
( 2
( M
+
1 ) R g P s
⎭⎪
(24)
Here, for simplicity we assume that Q pki
Also, g rid
/g rip
and g rid
/g rip
= Q pkM
= Q pk
.
(i
… j) are mutually independent. It is extremely difficult to directly compute the exact outage probability because the pdf of
M i
=
1 g g r p i
However, since:
is hard to obtain.
−
1 )
− g P s
] / Q pk
(25)
(26)
Average interference power constraint under multirelay: Let us firstly consider a scenario with two relay nodes, i.e., M = 2.
In this scenario, the problem is formulated as:
{
⎩⎪
Minimize Pr
⎧
⎨
⎩
1
3
⎜
⎛
⎝
1
+ g P s +
N
0 g rid
P
1 +
N
0 g rid
P
2
N
0
⎞
⎠
⎟ <
R
⎫
⎬
⎭
(27)
[ P
1
]
≤
Q av
1
, [ P
2
]
≤
Q av
2
(28) where, Q avi
(i = 1, 2) stands for the limit of the average interference power constraint at the ith primary node.
We will follow the same philosophy as used in calculating the minimum outage probability for a single relay under average interference power constraint. Thus, the optimal power control for above problem is expressed as:
⎧
⎪
P
1
=
⎨
⎪
P
2
=
N
0
( 2
3 R
N
0
( 2
3 R
1
)
) g g P s
− g P
2
0
<
P
1
< α
1
, g g g P s
− g P
1
0
<
P
2
< α
2
, g
< β
1
< β
2
(29) g rid g rid
P
1
Note that 0<P
$ [N
0
]/a
2
(2 3R -1)-g
0
P
1 s
-g
<a
1 and 0<P
2
<
"
2 rid
P
2
]/
"
1
respectively and since g rip
<
$
1 can be written as
and grid $ [N
and g
0
(2 rip
3R
<
$
2
-1)-g
0
P s
-
, we get:
⎨
⎪
⎧
⎪ g g g g
<
<
α β
1 1
N
0
( 2 3 R −
1 )
− g P s
− g P
2
α β
2 2
N
0
( 2 3 R −
1 )
− g P s
− g P
1
(30)
In order to hold (30), g r1p
/g r1d satisfy: and g r2p
/g r2d
should
⎪
⎨
⎧
⎪
⎪
⎪
⎩ g g g g
<
< min
⎪
⎩⎪
⎧
⎨ min
⎪
⎩⎪
⎧
⎨
N
0
( 2 3 R
N
0
( 2 3 R
α β
1 1
−
1 )
− g P s
− g P
2
⎭⎪
α β
−
1 )
−
2 2 g P s
− g P
1
⎭⎪
(31)
Thus, the above problem defined by (31) can be expressed as:
⎨
⎪
⎧
⎪ g g g g
<
<
N
0
( 2
2 R
α β
−
1
1
)
−
N
0
(
α β
2
2
2 R −
1 )
− g P s g P s
(32) where,
"
1
$
1
and
"
2
$
2
are determined by substituting (32) into E[g rip
P
1
] = Q av1 and E[g r2p
P
2
] = Q av2
, respectively.
Since the outage at the destination will not occur when at least one of two Eq. in (32) is satisfied, the corresponding outage probability is expressed as:
Pout
=
⎝
⎜
⎜
⎛
1
−
Pr
⎜⎜
⎛
⎝ g g
×
⎝
⎜
⎜
⎛
1
−
Pr
⎜⎜
⎛
⎝ g g
<
<
N
0
(
α β
1
2
2 R −
1 )
− g P s
⎟
⎞
⎠
⎟
⎟⎟
⎞
⎠
N
0
(
α β
2
2
2 R −
1 )
− g P s
⎟
⎞
⎠
⎟
⎟⎟
⎞
⎠
(33)
Assuming that Q av1
= Q av2
= Q av
, then
"
1
$
1
=
"
2
$
2
.
Substituting the pdf of g rp
/g rd
into (33), we have:
P out
=
⎢
⎣ ⎢
⎡
⎢
1
−
0 ϕ
(
λ rp x
λ λ rp rd
+ λ rd
)
2 dx ⎥
⎦ ⎥
⎥
⎤ 2
=
1
⎜⎜
⎛
⎝
λ rd
λ rp ϕ +
1
⎟⎟
⎞
⎠
2 where, n
has given in (20).
(34)
3962

As can be seen from (34), if ϕ goes to infinity, the outage probability becomes zero. However, from (20), n equals to infinity only when Qav = 4 .
Likewise, a simple approximation of the outage probability for M relay nodes can be obtained
P out
=
⎢
⎣ ⎢
⎡
⎢
1
−
Res. J. Appl. Sci. Eng. Technol., 4(20): 3958-3964, 2012
0 ϕ
(
rp
rd x
+
rd
)
2 dx ⎥
⎦ ⎥
⎥
⎤ M
= ⎜⎜
⎛
⎝
rd
rp
NUMERICAL RESULTS
+
1
⎟⎟
⎞
⎠
−
M
(35)
M = 1
M = 2
M = 4
10
0
10
-1
10
-2
10
-3
10
-4
10
-5
10
-6
-20 -15 -10 -5 0 5 10 15
In this section, we will give the numerical results to validate the derived power control strategies. In the following simulations, we assume that the transmission rater R is 3 bits/sec/Hz and all curves shown in Fig. 2-4 correspond to
8 rd
= 1 and
8 rp
= 2. Meanwhile, N
0
= 1is used throughout this study.
Figure 2 illustrates the outage probability versus g
0
P for the case M = 1. It is seen from the figure that the s
20 25
Fig. 4: Outage probability under the average interference power constraint
0.95
0.85
0.75
0.65
0.55
0.45
-10 -5 0 5 g P (dB)
0 s
10 15
Fig. 2: The relationship between outage probability and g
0
P s
M = 1
M = 2
M = 4
10
0
10
-1
10
-2
10
-3
10
-4
10
-5
10
-6
-15 -10 -5 0 5 10 15 20 25 30 35
Fig. 3: Outage probability under the peak interference power constraint outage probability decreases with the increase of g
0
P s
.
Moreover, for a given Q pk
(Q av
) , the outage probability for different g
0
P s do not vary much. Thus, in the following examples, we set g
0
P s
= 10 dB for simplicity.
Figure 3 shows the outage probability of suggested cognitive relay network versus the peak interference power constrain Q pk for M = 1, 2 and 4. It is evident that when Q pk
is small, the curves almost overlap with one another. This reveals the fact that Q pk
is the bottleneck that restricts the performance of the system when Q pk
is small. However, with the increase of Q pk
, the outage probability for different Q pk
gradually become different.
More specifically, for a given Q pk
, the outage probability decreases with the increase of the number of M . This means that a spatial diversity gain of M is achieved.
Figure 4 depicts the outage probability of suggested system model versus the average interference power constraint Q av
for M = 1, 2 and 4. It is seen from the figure that with the increase of Q av
, the outage probability for all curves decreases. As expected, we also see that the outage performance of multi-relay is better than that of a single relay node. Comparing with Fig. 3, it is evident that the outage probability under the average interference constraint drop sharply when Q av
reaches a certain value.
This demonstrates when Q av
becomes large enough, there will be no outage. This is consistent with the results we analyzed in Section 3. Additionally, we observe that, for a given M, the outage probability under average interference power constraint is much less than that under peak interference power constraint when Q av
= Q pk
, meaning that the power control policy under the average interference power constraint is more effective. This is because the cognitive relay node turns off the transmission to save the power when the channel is unfavorable, and concentrates the power to transmit when the channel is favorable in this scenario.
3963

This study was supported in part by the National
Natural Science Foundation of China (No. 60905040,
60972039, 60971129, 61001077 and 61071092), Basic
Research Program of Jiangsu Province (Natural Science
Foundation) (No. BK2011756), Natural Science Fund for
Higher Education of Jiangsu Province (No. 11KJB510018 and 11KJB510016), Jiangsu Planned Projects for
Postdoctoral Research Funds (No. 1101006B), Scientific
Research Foundation of Nanjing University of Posts and
Telecommunications (No. NY211009, NY210006 and
NY210072), the scientific project of Jiangsu Entry-Exit
Inspection and Quarantine Bureau (No. 2011KJ19), the project of the science and technology bureau of Suzhou municipal government (No. SYJG0925, No. SWG0913) and the open research fund of National Mobile
Communications Research Laboratory, Southeast Univ.
(No. 2011D05).
Res. J. Appl. Sci. Eng. Technol., 4(20): 3958-3964, 2012
CONCLUSION
A cognitive wireless relay network model is suggested in this study. We mainly focus on understanding that the effect of the cognitive relay node on the proposed model has been studied by evaluating the outage probability in Rayleigh environments. For both the peak and average interference power constraints, we derived closed-form expressions for outage probability under M relay nodes, respectively. Finally, numerical performance results show that substantial performance improves with the increase of the number of cognitive relay nodes and the power control policy under the average interference power constraint is more effective than that under the peak interference power constraint.
ACKNOWLEDGMENT
REFERENCES
Akyildiz I.F., F.L. Brandon and R. Balakrishnan, 2011.
Cooperative spectrum sensing in cognitive radio networks: A survey. Phys. Commun., 4: 40-62.
Fujii, T. and Y. Suzuki, 2005. Ad-hoc cognitive radio-
Development to frequency sharing system by using multi-hop network. 1st IEEE International
Symposium on New Frontiers in Dynamic Spectrum
Access Networks, DySPAN , 8-11 Nov., pp: 589-
592.
Hamdi, K. and K.B. Letaief, 2007. Cooperative communications for cognitive radio networks. PGNet
2007, the 8th Annual Postgraduate Symposium,
Liverpool John Moores University, Liverpool, UK,
June 28-29.
Kang, X., Y.C. Liang and A. Nallanathan, 2008. Optimal power allocation for fading channels in cognitive radio networks: Delay-limited capacity and outage capacity. Proceeding of IEEE Vehicular Technology
Conference (VTC Spring 2008), pp: 1544-1548.
Laneman, J.N., D.N.C. Tse and G.W. Wornell, 2004.
Cooperative diversity in wireless networks: Efficient protocols and outage behavior. IEEE T. Inf. Theor.,
50(12): 3062-3080.
Lee, K. and A. Yener, 2006. Outage performance of cognitive wireless relay networks. Proceeding of
IEEE Global Telecommunications Conference
(GLOBECOM06), pp: 1-5.
Louie, R.H.Y., Y. Li, H.A. Suraweera and B. Vucetic,
2009. Performance analysis of beam forming in two hop amplify and forward relay networks with antenna correlation. IEEE T. Wireless Commun., 8(6): 3132-
3141.
Mitola, J. and G.Q. Maguire, 1999. Cognitive radio:
Making software radios more personal.
IEEE Pers.
Commun., 6: 13-18.
Sadek, A.K., W.F. Su and K.J. Ray Liu, 2007. Multinode cooperative communications in wireless networks.
IEEE T. Signal Proc., 55(1): 341-355.
Suraweera, H.A., P.J. Smith and N.A. Surobhi, 2008.
Exact outage probability of cooperative diversity with opportunistic spectrum access. Proceeding of
IEEE International Conference on Communications
(ICC), pp: 79-84.
Wang, B. and K.J. Liu Ray, 2011. Advances in cognitive radio networks: A survey. IEEE J. Select. Top. Signal
Proc., 5: 5-23.
3964