# Optimal Power Allocation for Relay Assisted Cognitive Radio Networks

**Abstract**

In this paper, we study the optimal power allocation of wireless relay nodes which are used in the secondary user (SU) communication of a cognitive radio (CR) network. We consider the behavior of transmitting powers of SUs where those powers are limited to the tolerable interference as seen by the primary user (PU) communication. To improve the performance of the secondary communication based on minimizing the outage probability, we re-formulate the power allocation problem with a new set of constraints. These are obtained by considering the co-channel interference generated by the SU communication to the PU communication. The power allocation problem is solved for both regenerative and non regenerative relay models under Rayleigh fading conditions. SU communication with N number of relays is discussed and compared. The outage probability of SU communication is limited by the interference power threshold (IPT) constraints of PUs and is affected significantly by the IPT levels of PUs.

Optimal Power Allocation for Relay Assisted

Cognitive Radio Networks

L.K. Saliya Jayasinghe

∗

, Nandana Rajatheva

†

Telecommunications Field of Study, School of Engineering and Technology,

Asian Institute of Technology, Klong Luang, Pathumthani 12120, Thailand

keeth.saliya@yahoo.com

∗

, rajath@ait.ac.th

†

Abstract—In this paper, we study the optimal power allocation

of wireless relay nodes which are used in the secondary user

(SU) communication of a cognitive radio (CR) network. We

consider the behavior of transmitting powers of SUs where those

powers are limited to the tolerable interference as seen by the

primary user (PU) communication. To improve the performance

of the secondary communication based on minimizing the outage

probability, we re-formulate the power allocation problem with

a new set of constraints. These are obtained by considering the

co-channel interference generated by the SU communication to

the PU communication. The power allocation problem is solved

for both regenerative and non regenerative relay models under

Rayleigh fading conditions. SU communication with N number

of relays is discussed and compared. The outage probability of

SU communication is limited by the interference power threshold

(IPT) constraints of PUs and is affected signiﬁcantly by the IPT

levels of PUs.

I. INTRODUCTION

Cognitive radio (CR) is a promising technology that enables

solutions for complicated problems in wireless communication

which are believed to be infeasible in the past. Demand for

data rates gradually increase due to the rapid development

of services needed which makes the efﬁcient use of available

spectrum, a sine qua non, in wireless system implementations.

Recent surveys from the Federal Communications Commis-

sion (FCC) indicate that in about 90 percent of the time, many

licensed frequency bands remain idle [1]. The concept of CR

introduced as a method to improve the spectrum utilization

by allowing a secondary user (SU) to utilize a licensed band

when the primary user (PU) is absent.

SU transmission is possible with the same spectrum via

different types of strategies. A common approach is to dynam-

ically sense a frequency hole vacated by a PU and use that

frequency slot for communication [2]. Additionally, interesting

techniques are introduced to use the same frequency band by

controlling the power levels of the SU transmissions to limit

the interferences to PU communication [3].

In some cases PUs utilize different licensed spectrum bands

in multiple geographic areas and hence the SU communication

between those geographical areas [3] is made possible by

maintaining a certain level of interference to the PUs. How-

ever, the required SU transmitting power can be much higher

in direct communication and thus causing severe interference

to the PUs. The circumstances where the SUs limit their power

and use the same spectrum as the PUs simultaneously are

discussed in [3] where the use of a cooperative relay scheme

between the SUs to reduce SU transmitting power is proven

to be an effective way of ensuring lower interference to the

PUs [4]. The relay based transmission is introduced to the sec-

ondary communication in [4] and the positive improvements

of that method also are discussed in their research work.

It is found that cooperative relay communication enhances

the system performance, reduces unwanted interference and

saves power at the source [5]. The performance of cooperative

relay networks is discussed with different parameters and

channel environments in [6, 7] where improvements in the

overall transmission rate and the diversity are obtained. The

power allocation schemes for relays to achieve better perfor-

mance are discussed for the regenerative, non regenerative, etc.

cases in [8–10].

In cognitive radio with the relay based secondary communi-

cation, it is essential to improve the performance of secondary

system subject to pre-determined interference levels to PUs.We

should consider optimum power allocation to achieve this

objective.

To the best of authors knowledge the power allocations

problem where a relay is used in the secondary transmission is

not previously considered in the open literature. Therefore, we

study this issue in Rayleigh fading channels. To improve the

secondary communication, the power has to be distributed in

an optimal way among the source and relay nodes to minimize

the outage probability while maintaining a reliable primary

link. We formulate an optimization problem for power allo-

cation in the relays which are used in the SU communication

where we also introduce additional IPT constraints for the

SU which guarantee predetermined interference levels at the

PUs. Both regenerative and non regenerative relay models are

considered.

The rest of the paper is organized as follows. In section II,

we describe the system and the channel model. In addition to

that, the basic formulation of optimization problem is given

there. In section III, we study the case of a single relay

node for both regenerative and non regenerative cases. N relay

nodes scheme for the secondary communication is discussed in

section IV and the Numerical results are presented in section

V. Finally, conclusions are given in section VI.

978-1-4244-3574-6/10/$25.00 ©2010 IEEE

II. SYSTEM MODEL AND PROBLEM

FORMULATION

In the problem formulation, we consider a communication

system with N relay nodes and it is shown in Fig.1.

.

...

.

.

U

a

U

b

s

d

a

b

m n

r

N

r

2

r

1

Secondary transmission using Cooperative scheme

r

3

Intereferences for PUs

I

a-b

I

b-a

I

b-a

I

a-b

Fig. 1. N relay nodes used in secondary communication. Source node and

relay nodes are transmit signal with different frequency sets.

U

a

and U

b

are PUs and their coverage areas are shown as

a and b. s and d are two SUs which use relays r

1

to r

N

for

their communication. Frequency sets are selected as shown in

Fig.1 to satisfy the condition that s or d does not interfere

with their nearest PU.

I

a

: Available spectrum for s

I

b

: Available spectrum for d

I

a−b

= I

a

- I

b

: Idle channel set in a, but busy in b

s to r

1

transmission uses I

a−b

frequency set and r

1

to r

2

,..,

r

N−1

to r

N

, r

N

to d transmissions use I

b−a

frequency set.

In addition to this, we assume that the SUs can sense the

spectrum and distribute their channel state information (CSI)

to each other. SU source and destination nodes have the

capability to sense and locate the most affected PUs in their

PU coverage areas.

m and n are primary users of U

a

and U

b

. n is the most

affected user at U

b

due to the transmission of source s and m

be the most affected user in U

a

. IPT level is the highest that

can be tolerated by a PU. The transmit power of source s is

denoted as p

s

and that of relay node r

n

by p

n

. The link gain

between node i and node j is h

ij

. The IPT levels on m and n

are T

a

and T

b

. Total power allocated for source and relays is

denoted as P

T

.

Interferences from nodes which are used in the secondary

communication should fulﬁll the following requirements to

have stable PU communication.

p

s

h

sn

≤ T

b

(1)

N

n=1

p

n

h

r

n

m

≤ T

a

(2)

The signal envelopes of the links are assumed to have Rayleigh

distributions. Therefore, the Signal-to-noise ratio (SNR) of the

channel is exponentially distributed. In general, the average

SNR of the links

γ

n

can be written as γ

n

= G

n

p

n

. G

n

contains parameters such as the antenna gains, path loss,

shadowing, noise power, and similar parameters [11], and p

n

is the transmitted power of the n

th

relay.

we select the outage probability of secondary communica-

tion as our objective function and formulate an optimization

problem by considering total power constraint and IPT con-

straints.

min P

out

subject to

⎧

⎪

⎪

⎪

⎨

⎪

⎪

⎪

⎩

p

s

+

N

n=1

p

n

= P

T

p

n

≤ P

max

: n =1, 2, ..., N

p

s

≤ P

max

p

s

h

sn

≤ T

b

N

n=1

p

n

h

r

n

m

≤ T

a

Outage probabilities that we obtain for this are convex

functions and our constraints are linear. The convexity of the

feasibility set also satisﬁed due to linearity. Therefore, we

can solve this as a convex optimization problem and obtain

a unique solution.

III. SINGLE RELAY TRANSMISSION

Here, we consider the power allocation of the source and

the r elay node for both regenerative and non regenerative relay

systems.

A. Regenerative Relay scheme

Outage probability is given by the following equation [12],

[13],

P

out

=1− e

−γ

th

(

1

G

1

p

1

+

1

G

2

p

2

)

(3)

where p

1

and p

2

are source and relay node transmit powers.

Minimizing P

out

in this case is equivalent to minimizing

γ

th

(

1

G

1

p

1

+

1

G

2

p

2

) term. Therefore the problem reduces to,

min γ

th

(

1

G

1

p

1

+

1

G

2

p

2

)

subject to

⎧

⎪

⎨

⎪

⎩

2

n=1

p

n

= P

T

p

n

≤ P

max

: n =1, 2

p

1

h

sn

≤ T

b

p

2

h

rm

≤ T

a

Since this is a convex problem, Lagrangian function given as

Λ(p

1

,p

2

)=γ

th

(

1

G

1

p

1

+

1

G

2

p

2

)+μ(p

1

+ p

2

− P

T

)+λ

1

(p

1

−

T

b

h

sn

)+λ

2

(p

2

−

T

a

h

rm

)+λ

3

(p

1

− p

max

)+λ

4

(p

2

− p

max

).

(4)

By using Karush-Kuhn-Tucker (KKT) conditions, we obtain

the solutions as follows.

Neglecting all inequality constraints,we have the following

optimal solution.

p

∗

1

= P

T

[1 +

G1

G2

]

−1

and p

∗

2

= P

T

[1 +

G2

G1

]

−1

(5)

The inequality constraints are relevant at higher P

T

values, for

lower values there is no use of those to obtain the solution.

In general we can assume both

T

b

h

sn

and

T

a

h

rm

are always less

than p

max

. Therefore we neglect p

max

and by considering the

IPT constraints, we get

p

∗

1

=

⎧

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎨

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎩

P

T

[1 +

G1

G2

]

−1

: P

T

<min[

T

b

h

sn

(1 +

G

1

G

2

),

T

a

h

rm

(1 +

G

2

G

1

)])

T

b

h

sn

: if

T

b

h

sn

(1 +

G

1

G

2

)=min[

T

b

h

sn

(1 +

G

1

G

2

),

T

a

h

rm

(1 +

G

2

G

1

)] and

T

b

h

sn

(1 +

G

1

G

2

) <P

T

<

T

a

h

rm

+

T

b

h

sn

P

T

−

T

a

h

rm

: if

T

a

h

rm

(1 +

G

2

G

1

)=min[

T

b

h

sn

(1 +

G

1

G

2

),

T

a

h

rm

(1 +

G

2

G

1

)] and

T

a

h

rm

(1 +

G

2

G

1

) <P

T

<

T

a

h

rm

+

T

b

h

sn

T

b

h

sn

: P

T

≥

T

a

h

rm

+

T

b

h

sn

We can clearly see that with different P

T

values, the optimal

power allocation has different solutions. Outage probability

variation with P

T

is considered in the numerical results

section.

B. Non regenerative Relay scheme

Non regenerative relay receives the signal from source s

and ampliﬁes and forwards it to the destination d. Outage

probability for a dual hop systems i s given by [13],

P

out

=1−

2C

√

G

1

p

1

G

2

p

2

K

1

(

2C

√

G

1

p

1

G

2

p

2

)e

−γ

th

(

1

G

1

p

1

+

1

G

2

p

2

)

(6)

K

1

is ﬁrst order modiﬁed Bessel function and C = γ

th

or

C =

γ

th

2

+ γ

th

. Therefore the problem formulation for

non-regenerative relay is given by,

min 1 −

2C

√

G

1

p

1

G

2

p

2

K

1

(

2C

√

G

1

p

1

G

2

p

2

)e

−γ

th

(

1

G

1

p

1

+

1

G

2

p

2

)

subject to

⎧

⎪

⎨

⎪

⎩

2

n=1

p

n

= P

T

p

n

≤ P

max

: n =1, 2

p

1

h

sn

≤ T

b

p

2

h

rm

≤ T

a

We have use the following expression [14] of Bessel functions

to solve this using KKT conditions.

z

d

dz

K

v

(z)+vK

v

(z)=−zK

v−1

(z) (7)

We assume both

T

b

h

sn

and

T

a

h

rm

are always less than p

max

.

Therefore, we neglect the effect of p

max

constraints and the

optimal solution is given by,

p

∗

1

=

⎧

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎨

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎩

[

G1

G2

1

(P

T

−p

1

∗

)

2

+

C

γ

th

G1

G1

K

0

(

2C

√

G

1

p

1

∗

G

2

p

2

∗

)

K

1

(

2C

√

G

1

p

1

∗

G

2

p

2

∗

)

×

(

1

√

p

1

∗

(P

T

−p

1

∗

)

3

−

1

√

p

1

∗ 3

(P

T

−p

1

∗

)

)]

−1/2

T

b

h

sn

P

T

−

T

a

h

rm

K

0

(.) is the zeroth-order modiﬁed Bessel function of the

second kind and the ﬁrst expression for p

1

∗

is obtained when

all the constraints are relaxed. The ﬁrst solution is a transcen-

dental function of p

1

∗

and therefore numerical techniques have

to be used to solve it. Techniques such as Newton algorithm

can be considered.

Additionally, the closed form solution that we have for the

regenerative model can be approximated to the non regenera-

tive case. These details are discussed in the numerical results

section.

IV. N R

ELAY TRANSMISSION

Outage probability for regenerative relay scheme

in Rayleigh faded case is given as P

out

=

1 − e

−γ

th

G

s

p

s

N

n=1

e

−γ

th

G

n

p

n

.

Minimizing the outage probability is similar to maxi-

mizing e

−γ

th

G

s

p

s

N

n=1

e

−γ

th

G

n

p

n

and it reduces to minimizing

N

n=1

γ

th

G

n

p

n

+

γ

th

G

s

p

s

. P

max

constraint can be neglected without

causing much damage to original problem. This is possible,

since node powers are limited by the IPT values. Lagrangian

function is given as,

Λ(p

s

,p

n

)=

N

n=1

γ

th

G

n

p

n

+

γ

th

G

s

p

s

+ μ(p

s

+

N

n=1

p

n

− P

T

)+λ

s

(p

s

−

T

b

h

sn

)+λ

0

(

N

n=1

p

n

h

r

n

m

− T

a

)

(8)

Problem is solved using KKT conditions and the optimal

solutions have different expressions depending on P

T

and gain

parameters.

1) Case where P

T

has a lower value, both interference

inequalities do not have a contribution to the optimal so-

lution. Optimal solution given as(λ

s

∗

=0and λ

0

∗

=0),

p

n

∗

= P

T

[1+

√

G

n

N

k=1,k=n

1

√

G

k

+

G

n

G

s

]

−1

: n =1, 2, ..., N

(9)

p

s

∗

= p

n

∗

G

n

G

s

(10)

The following two inequalities are needed to be satisﬁed

by other parameters to obtain above optimal solution.

P

T

[1 +

√

G

n

N

k=1,k=n

1

√

G

k

+

G

n

G

s

]

−1

G

n

G

s

<

T

b

h

sn

(11)

N

n=1

h

r

n

m

P

T

[1+

√

G

n

N

k=1,k=n

1

√

G

k

+

G

n

G

s

]

−1

<T

a

(12)

2) Case where the p

s

is limited by the IPT threshold. Then

the source power reaches its maximum power

T

b

h

sn

and

node powers are given by (λ

s

∗

=0and λ

0

∗

=0),

p

n

∗

=(P

T

−

T

b

h

sn

)[1 +

√

G

n

N

k=1,k=n

1

√

G

k

]

−1

(13)

p

s

∗

=

T

b

h

sn

(14)

3) Both m and n users sense maximum interference from

the source and relay nodes, when both IPT constraints

are satisﬁed with equality (λ

s

∗

=0and λ

0

∗

=0). P

T

has no effect in the optimal solution.

p

s

∗

=

T

b

h

sn

(15)

p

n

∗

= T

a

[h

r

n

m

+

√

G

n

N

k=1,k=n

h

r

n

m

h

r

k

m

1

√

G

k

]

−1

(16)

4) Most probable case in a real environment is where all

relay nodes get power levels to interfere primary user m.

The p

s

does not reach its maximum. In other words, this

simply means λ

s

∗

=0and λ

0

∗

=0. The closed form

expressions are not available for this and the following

equations are obtained by simplifying KKT conditions.

p

n

∗

= p

1

∗

[

G

n

G

1

h

r

n

m

h

r

1

m

+

G

n

G

s

p

2

1

p

2

s

(1 −

h

r

n

m

h

r

1

m

)]

−1/2

(17)

The above can be simpliﬁed into p

n

∗

= K

n

p

1

∗

and K

n

is a function of p

1

∗

and p

s

∗

. Then the following two

equations are dependent only on p

1

∗

and p

s

∗

. Therefore

the optimal solutions can be obtained by carrying out a

numerical technique.

p

1

∗

[h

r

1

m

+

N

i=2

h

r

i

m

K

i

]=T

a

(18)

p

1

∗

[1 +

N

i=2

K

i

]+P

s

∗

= P

T

(19)

V. NUMERICAL RESULTS

Numerical results are obtained for three different instances.

Power allocation with and without IPT constraints and uniform

allocation are considered. Optimal outage probabilities for

these cases are plotted with total power P

T

.

In addition to that, numerical results are obtained for both

non regenerative and regenerative cases by assuming certain

parameters. We take γ

th

=3, G

1

=1, G

2

=10, T

b

=10,

h

sn

=0.2 , T

a

=20and h

rm

=0.2. Also in the equal

power allocation case, powers are equally divided between the

source and the relay node. Fig.2. shows the outage variation

with the total power f or regenerative relay based secondary

communication. The case without IPT constraints is also

plotted in t he same graph. Fig.3 describes the outage variation

of regenerative and non regenerative models.

0 5 10 15 20 25

10

−2

10

−1

10

0

Total power,[dB]

Outage Probability

Optimal − With IPT

Optimal − Without IPT

Equal power − Without IPT

Fig. 2. Single regenerative relay based secondary communication outage

probability behavior

0 5 10 15 20 25

10

−2

10

−1

10

0

Total power,[dB]

Outage Probability

Non Regenerative,no IPTs

Regenerative,no IPTs

Non Regenerative,IPTs

Regenerative,IPTs

Fig. 3. Outage probability comparison between non regenerative and

regenerative relay cases

0 5 10 15 20 25 30 35

10

−3

10

−2

10

−1

P

total

dB

Bit Error Rate

With IPT constraints

Without IPT constraints

Fig. 4. Average BER of a regenerative relay system with BPSK modulation.

With and without IPT constraints cases are considered.

Outage probability reduces with the total power P

T

in each

case that we consider here. Interestingly, in the cognitive case,

the outage probability does not reduce below a certain level

with P

T

. This happens due to the IPT constraints that we

introduce into the optimization problem. When the secondary

communication reaches its minimum outage probability, PUs

experience the highest interference from the secondary trans-

mission. Therefore, there is a limit in the outage probability

that the s econdary transmission can achieve. Also by referring

to Fig.3, we can see that the regenerative model can be

approximated to the non regenerative case. Plots obtained for

both are very much related.

Fig. 4 compares the average bit-error rate (BER) for the

regenerative relay with the binary phase shift keying (BPSK)

modulation. Here we have consider the optimal power allo-

cation cases with and without IPT constraints. It is clear that

have BER also behaves similar to the outage probability as

seen in Fig.4.

Fig. 5 shows the multi-hop transmission comparison up

0 5 10 15 20 25

10

−2

10

−1

10

0

Total power,[dB]

Outage Probability

Four relays

Three relays

Two relays

Single relay

Fig. 5. Outage probability comparison between N realy model with 1,2,3

and 4 relays

0 5 10 15 20 25

10

0

Total power,[dB]

Outage Probability

Optimal power allocation

Equal power allocation

Optimal power allocation − no IPTs

Fig. 6. Outage probability of comparison with 3 series relays. Optimal

power allocation with IPT constraints, equal power allocation and optimal

power allocation without considering IPT constraints compared

to four relays. Relays are in series and the regenerative

relay model with Rayleigh fading channel environment is

selected for the comparison. When the number of relays are

increased, outage probability becomes higher. Fig. 6 compares

the outage behavior in the three relay nodes model. Optimal

power allocation s hows much better outage performance when

compared to equal power allocation and the IPT constraints

effect is visible as in the previous case.

VI. CONCLUSIONS

In this paper, we consider the optimum power allocation

for relay based secondary transmission schemes. Cooperative

relay network cases are reformulated by introducing additional

interference power threshold constraints. These are convex

optimization problems and we consider the outage probability

of the secondary communication as the objective function.

Then the problem is solved for the single relay model. Both

regenerative and non regenerative cases are considered where

they behave in a similar manner even though the objective

function has different expressions.

It is observed from the numerical results that the outage

probability cannot be reduced after a certain level, even

though there is sufﬁcient total power. Optimal solutions for

the general case with N relay nodes are obtained. They have

different expressions depending on the other parameters like

channels gains and total power of relay nodes. Performance

of the secondary user communication improves with the stable

primary user transmission at the same time.

Finally, by considering results that we have, the optimal

power allocation guarantees that the primary user will not be

affected due to the secondary transmission while the secondary

user achieves its maximum possible performance under some

limitations.

R

EFERENCES

[1] FCC, “Spectrum Policy Task Force,” ET Docket 02-135, Nov. 2002.

[2] S. Haykin, “Cognitive radio: Brain-empowered wireless communica-

tions,” IEEE J. Select. Areas Commun., vol. 23, no. 2, pp. 201-220,

Feb. 2005.

[3] J. Jia and Q. Zhang, “A Non-Cooperative Power Control Game for Sec-

ondary Spectrum Sharing,” in Proc. of IEEE International Conference

on Communications (ICC) 2007, Jun. 2007.

[4] X. Gong, W. Yuan, W. Liu, W. Cheng, and S. Wang, “A Cooperative

Relay Scheme for Secondary Communication in Cognative Radio Net-

works,” IEEE Global Telecommunications Conference, Dec 2008, Dec.

2008.

[5] A. Nosratinia, T. E. Hunter, and A. Hedayat, “Cooperative communica-

tion in wireless networks,” IEEE Commun. Mag., vol. 42, no. 10,pp.74-

80, Oct. 2006.

[6] J. N. Laneman, D. N. C. Tse, and G. W. Wornell, “Cooperative diversity

in wireless networks: efﬁcient protocols and outage behavior,” IEEE

Trans. Inf. Theory, vol. 50, no. 12, pp. 3062-3080. Dec. 2004.

[7] J. N. Laneman and G. W. Wornell, “Distributed space-time coded

protocols for exploiting cooperative diversity in wireless networks,”

IEEE Trans. Inf. Theory, vol. 49, no. 10, pp. 2415-2425, Oct. 2003.

[8] Y. Zhao, R. Adve, and T. J. Lim, “Improving amplify-and-forward

relay networks: optimal power allocation versus selection,” Wireless

Communications, IEEE Transactions on August 2007, Aug. 2007.

[9] Z. Qi, Z. Jingmei, S. Chunju, W. Ying, Z. Ping, and H. Rong, “Power

allocation for regenerative relay channel with Rayleigh fading,” Vehic-

ular Technology Conference, 2004. VTC 2004-Spring. 2004 IEEE 59th

May 2004, May 2004.

[10] X. Deng and A. M. Haimovich, “Power allocation for cooperative

relaying in wireless networks,” Communications Letters, IEEE ,vol.9,

no.11, pp. 994-996, Nov. 2005.

[11] T. S. Rappaport, Wireless Communications: Principles and Practice.

Englewood Cliffs, NJ: Prentice-Hall, 1996.

[12] M. O. Hasna and M. S. Alouini, “Optimal power allocation for relayed

transmissions over Rayleigh-fading channels,” Wireless Communica-

tions, IEEE Transactions on Nov. 2004, vol.3, no.6, pp. 1999-2004, Nov.

2004.

[13] M. O. Hasna and M. S. Alouini, “Performance analysis of two-hop

relayed transmissions over Rayleigh fading channels,” Vehicular Tech-

nology Conference, 2002. Proceedings. VTC 2002-Fall. 2002 IEEE 56th

, vol.4, no., pp. 1992-1996, 2002.

[14] I. S. Gradshteyn and I. M. Ryzhik, Table of Integrals, Series, and

Products, 5th ed. San Diego, CA: Academic, 1994.

- CitationsCitations11
- ReferencesReferences17

- "In this study, we address these two issues of interference minimization and SU data rate maximization in an integrated framework. Unlike the popular interference management solutions in CRNs [11][12][13][14], focusing only on transmit power control, we adopt a cross layer approach. The cross layer approaches find their basis in combining the principle of layered design of network functionalities with the idea of designing greater interactions among layers in the network protocol stack. "

[Show abstract] [Hide abstract]**ABSTRACT:**An opportunistic routing problem in a cognitive radio ad hoc network is investigated with an aim to minimize the interference to primary users (PUs) and under the constraint of a minimum end-to-end data rate for secondary users (SUs). Both amplify-and-forward (AF) and decode-and-forward (DF) relaying techniques are considered for message forwarding by SU nodes in the network. Unlike popular transmit power control based solutions for interference management in cognitive radio networks, we adopt a cross layer approach. The optimization problem is formulated as a joint power control, channel assignment and route selection problem. Next, closed form expression for transmission power is derived and corresponding channel selection scheme and routing metric are designed based on this solution. The proposed route selection schemes are shown to depend not only on gains of the interference channels between SUs and PUs but also on the values of the spectrum sensing parameters at the SU nodes in the network. Two distributed routing schemes are proposed based on our analysis; (i) optimal_DF and (ii) suboptimal_AF. The routing schemes could be implemented using existing table driven as well as on demand routing protocols. Extensive simulation results are provided to evaluate performance of our proposed schemes in random multihop networks. Results show significant reduction in PUs' average interference experience and impressive performance as opportunistic routing schemes can be achieved by our schemes compared to traditional shortest path based routing schemes. Performance improvement is also reported over prominent recent schemes.- "Joint relay selection and power allocation scheme was proposed in [5] to maximize system throughput with limited interference to primary used in cognitive radio system. The optimal power allocation (OPA) technique was addressed in [6] and [7]. The outage performance of single relay networks was researched in [8]. "

[Show abstract] [Hide abstract]**ABSTRACT:**In this paper, we investigate the outage performance of multi-node relay network under equal power allocation (EPA) scheme. Due to the mobility of relay nodes, we proposed to design a method that considers random relay location based on random model. We conduct deep experiments which demonstrate that there exists optimal relay location to minimize the outage probability of multi-node relay networks. Finally, simulation results show that the outage performance of the multi-node relay network is closely related to the relay location.- "In [3], OFDM based hybrid power allocation and in [4] optimal and suboptimal power loading algorithm in different OFDM subcarriers are proposed. Relay in various forms, like maximizing signal to noise ratio (SNR) on its path selection [5], using decode and forward (DF), amplify and forward (AF) in OFDM system with a sum-power constraint [6], power allocation incorporating dynamically changing environment [7] , cooperative relay schemes (CRS) to increase signal-tointerference noise ratio (SINR) at secondary receiver [8] and many others have been explored in CR research. The selection of relay strategy depends upon the channel condition and the performance criterion. "

[Show abstract] [Hide abstract]**ABSTRACT:**This paper proposes power allocation algorithm in orthogonal frequency division multiplexing (OFDM) based cognitive radio (CR) system using decode and forward (DF) relay. It is reported in the literature that both classical i.e. uniform power loading scheme and water filling methods that allocate power on OFDM subcarriers based on the channel gain are not effective for cognitive radio network (CRN) as the schemes introduce large interference to primary user (PU). To this aim, the present work proposes a simple, computationally efficient yet effective power loading scheme that maximizes the transmission capacity of CR while keeping the interference introduced to the PU below acceptable limits. To meet the objective, proposed power allocation to subcarriers not only considers channel gains but also the relative distances between CR subcarriers and PU band. This capacity performance is further improved significantly using DF relay, where our research direction focuses on optimal sharing of power between CR and relay using maximal ratio combiner (MRC) at destination receiver. Performance for the proposed power allocation schemes are compared with OFDM based hybrid power allocation as well as joint subcarrier and power adaptation methods reported in the literature. Numerical results show that the relative gain on CR's capacity are 3.7 times and 2.3 times, respectively compared to the optimal and hybrid OFDM power allocation schemes, while for interference introduced to PU is set at 3 times 10-6 (in watt). Finally, a few related research problems are also highlighted as future scope of works.

Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.

This publication is from a journal that may support self archiving.

Learn more