Adaptive Self-regulating Null Navigation in cognitive radio

Syed Azmat Hussain School of Engineering & Applied Science

ISRA, university Islamabad, Pakistan.

1009-pd-e002@isra.edu.pk

A.Naveed Malik

School of Engineering & Applied Science, ISRA, university,

Islamabad, Pakistan. anaveed@isra.edu.pk

Abstract- In this paper, we have taken the scenario that cognitive user is fix while the primary user in mobility mode. We capitalize on the power in the direction of the cognitive user and create nulls in the direction of the primary user with self-regulating null navigation technique. When the primary user travel in direction of cognitive user, the cognitive user adaptively regulate the weights and construct null headed for the primary user with notion that, direction of arrivals (DOA) of the primary user are known by cognitive user. When the primary user changes the direction, cognitive user adjusts the complex weights adaptively and creates null toward primary user. In this technique we use the two weights for pair of consecutive antenna elements and attain summer output at different stages. The nulls are generated corresponding to the primary angle and the entire weights are adaptively optimized only reference to angle. When both the primary and the cognitive user revolutionize the direction then all the weights are changes and improve the spectral efficiency of the spectrum with navigating the beam toward cognitive user and bring into being self-generating nulls in direction of primary users. We suppress the interference to primary user by navigating lower power in the direction of primary user and construct the main lobe toward cognitive user. Simulation results established the algorithm effectiveness. Keywords: Cognitive radio, spectrum access, Uniform linear array, Beam-forming, Cognitive user, Primary user. 1. INTRODUCTION

Cognitive radio is a rising technology in wireless communication. Foremost concept of cognitive radio was given by John Mitola [1] in 1999. According to per pronouncement of the FCC that 15% to 85% of the spectrum is underutilization [2]. The existing spectrum is fix and inefficient. By [3] FCC reviews the policy of the spectrum utilization and allows the cognitive user to utilize the spectrum without disturbing the PU. Cognitive radio solves the conflict of spectrum scarcity and spectrum underutilization. It was proven in [4] that Cognitive radio sense the spectrum endlessly by using different techniques and access the spectrum hole with opportunistic spectrum access. Dynamic spectrum access was discuss in [5,6] while the estimation of angle of arrival detail is given in [7].It was given in [8] that Smart antennas technology can be used to improve the spectrum efficiency of mobile communication networks.

Cognitive radio transmits the signal at low hindrance power and maintains the licensed rights of primary user (PU’s). PU’s and the cognitive user (CU’s) activate in the same frequency spectrum band. Cognitive radio sense, learn and adapt according to radio environment. There are three types of techniques [9] to access the spectrum. These techniques are interweaving, overly and underlay. We consider the scenario of the interweave technique .In this technique the cognitive radio opportunistically access the spectrum hole and shift to others band when PU,s wants to utilize the spectrum. The CU,s and the PU,s operate in the same frequency band but not active at the same time. CUs will be active with the confirmation that PUs is not using the spectrum. It is proven in [10,11,12] that by utilizing the adaptive beam-forming , we can improve capacity, data rates and exposure of the cognitive radio . The spectral efficiency of the communication system increases by directing its communication beam toward the target and put nulls to the un-intended directions. This condition enables the cognitive radio users to exist among the licensed user and utilize the unused spectrum.

In this paper, we confer the case when cognitive radios coexist with the primary user. Cognitive transmitter and receiver are fix and primary user in movable mode. CU knows the direction of arrival (DOA) of the PU. CU-Tx(cognitive user transmitter) consists of multiple antenna at base station with down link adaptive beam-forming .The main beam is transmitted toward CU while the self-regulating nulls are generated toward PU. The weights are optimizing elegantly corresponding to nulls in the PU direction. The weights can be calculated when the CU navigate the main beam with changing new direction. The CU-Tx navigates the main beam for one user and put nulls toward PU-Rx at four different angles. When the primary user move than all the weights are changes respectively, so CU-Tx will optimize the weights adaptively with automatic generation of nulls and save the PU-Rx from interference with maintaining quality of service (QoS).Similarly when the cognitive user change the direction all the weights are adjusted corresponding to primary user angles. Adaptive antenna with self-regulating null navigation has the ability to mitigate the interference in the direction of

978-1-4673-2252-2/12/$31.00 ©2012 IEEE

CUPU

PU

PU

Fig.1.Model for proposed algorithm

CU-Tx PU

PU by generating null and navigating beam in the direction of cognitive users.

This paper is planned as follows: In Section 2, we present the system model and proposed algorithm. In section 3, we discuss the simulation result while in section 4 we confer the conclusion.

2. SYSTM MODEL AND PROPOSED ALGORITHM

The system model shown in Fig-1 consists of cognitive radio base station with array antennas cognitive transmitter, cognitive receiver and primary receiver. It has one cognitive user and four primary users, directions.CU transmitters know the channel state information of the CU receiver and PU receiver. In other words the CU-Tx know the direction of arrival of PU-Rx and CU-Rx. Array with multiple antennas direct it main beam toward one of CU-Rx and generates null toward the four PU-Rx.CU-Tx and CU-Rx are fixed while the PU-Rx in mobility mode. Adaptive beam-forming reject the interference toward the primary receiver and direct it main beam toward cognitive user.

Consider the base station consisting of M antenna array elements. The spacing between each antenna elements is

/ 2d λ= where λ , is the wavelength of incoming electromagnetic wave.

Navigating vector for M antenna elements, derived from array factor is given by

( 1) 0 0( ) 1, ,...., , 90 90j j M

i i toe eθ − Ψ − − Ψ⎡ ⎤= = −⎣ ⎦a (1)

Where

( 2 / )( sin( ))idψ π λ θ= − (2)

In the propose algorithm we merge the output of the two

successive antenna elements of the array via adaptive weights

for self-regulating null navigation as shown inFig-2.

Consider the navigating vector for the cognitive user for a pair

of neighboring antenna with two weights as shown in Fig-2.

sin( )( ) 1, cu

jcu e π θθ −⎡ ⎤=

⎣ ⎦a (3)

Similarly the navigating vector in the direction of the PU

having two antenna elements is given

sin( )( ) 1, puj

pu e π θθ −⎡ ⎤=⎣ ⎦

a (4)

Correlations matrix for the CU is given as

( ) ( )Hcu cu cuθ θ=R a a (5)

Similarly correlations matrix for PU is given as ( ) ( )H

pu pu puθ θ=R a a (6)

Sum of above correlation matrices is given as:

pu cu= +R R R (7)

For maximization of power in the direction of CU and minimization in the direction of PU we consider

MVDR such that

min

Hw Rw .s t ( ) 1Hcuθ =w a

(8)

By applying the langrage multiplier method we find the cost

function as

(1 ( ))H HcuJ λ θ= + −w Rw w a

(9)

For confirmation that the cost function given in (9) is convex

or concave we use the equation given in (10),(11),(12) and

(13) which obey the inequalities properties for function

: nf R→R defined on nR

Constant: ( )(1 ) ( )f x y f xα α+ − =

, ,nx y α∀ ∈ ∈R R (10)

Affine:

( )(1 ) ( ) (1 ) ( )f x y f x f yα α α α+ − = + −

, ,nx y α∀ ∈ ∈R R (11)

1

1

1

1

+

1

S-1 S-2 S-3

11

1

1

+

+

+ 2

2

2

2

2

2

3

3

3

3

+

+

+

Fig- 2 Proposed construction for Five AElements

z

,

,

,

,

,

,

Convex:

( )(1 ) ( ) (1f x y f xα α α+ − ≤ + −

, , [0,1]nx y α∀ ∈ ∈R

Concave:

( )(1 ) ( ) (1f x y f xα α α+ − ≥ + −

, , [0,1]nx y α∀ ∈ ∈R

By using the above definitions it is clear thais convex and we get the optimized weight v

1 1( ) / ( ) ( )H

cu cu cuθ θ θ− −=w R a a R a

Proposed construction with uniform elements is shown in Fig-2 and results a

an M-element array.

Fig.2 shows that each summer has two instage, first input of each summer is multiThese weights keep up a correspondence to tnull in the array factor.

The general formula for first stage(s-1) of an1M − elements can be expressed as

S-4

4

4 +

+

+

Antenna

,

,

,

) ( )f yα−

(12)

) ( )f yα−

(13)

at the equation (9) vector W such as

(14)

linear array of 5 are generalized for

nputs. In foremost iplied by weights. the arrangement of

n array with

( 1)

1, 1 ( )H j k

k ez θθ −= aw for

The general formula for stageM elements can be expressed

( 1)

2, 12

H j k

k ez z θ−= w for

The general formula for thiselements

( 1)

3, 23

H j k

k ez z θ−= w

Now we are capable of writing4) having M elements. This arr

( 1)

4, 34

H j k

k ez z θ−= w

Where, 1,... 4k M= −

3. SIMULATIO

We think about the uniformantennas element spacing / 2λnavigated with self-regulated mradio user toward zero degree θpu2, θpu3and θpu4. The weiusers are w1, w2, w3 and w4.user navigating the beam toregulated nulls toward PU a

080 respectively. The weightgiven in table-1

W1 W2 0.5000 - 1.7874i

0.5000 - 0.0119i

0.5000 + 1.7874i

0.5000 + 0.0119i

Fig-4 shows that cognitive ra

00 and self-regulating nulls tow

010 and 080 respectively. Tangles are in table -2

W1 W2 0.5000

- 0.8394i

0.5000 - 0.0119i

0.5000 +

0.5000 + 0.0119i

Table-1

r 1,..., 1k M= − (15) e(s-2) outputs of an array with d a

r 1,..., 2k M= − (16)

s stage(s-3) outputs with M is given as for 1,..., 3k M= − (17)

g a general formula for a stage(s-ray will have M-1 stages.

(18)

ON AND RESULTS

m linear array with multiple 2 for simulations. Four nulls are manner. Consider the cognitive while the PU angles are θpu1,

ights corresponding to primary The Fig-3 shows that cognitive oward 00 and generating self-

at angles 010− , 080− , 010 and ts corresponding to angles are

W3 W4 0.5000 + 1.7874i

0.5000 + 0.0119i

0.5000 - 1.7874i

0.5000 - 0.0119i

dio navigating the beam toward ward PU at angles 020− , 080−

,The weights corresponding to

W3 W4

0.5000 + 1.7874i

0.5000 + 0.0119i

0.5000 - 1.7874i

0.5000 - 0.0119i

1

0.8394i

The Fig-5 shows that cognitive user navigating the beam toward 00 and self-regulating nulls toward PU at angles 010− ,

085− , 010 and 080 respectively. The weights corresponding to angles are given in table-3

W1 W2 W3 W4 0.5000 - 1.7874i

0.5000 - 0.0030i

0.5000 + 1.7874i

0.5000 + 0.0119i

0.5000 +

1.7874i

0.5000 + 0.0030i

0.5000 - 1.7874i

0.5000 - 0.0119i

The Fig-6 shows that cognitive user navigating the beam toward 00 and self-regulating nulls toward PU at angles

010− , 080− , 020 and 080 respectively. The weights corresponding to angles are given in table-4

W1 W2 W3 W4 0.5000 - 1.7874i

0.5000 - 0.0119i

0.5000 + 0.8394i

0.5000 + 0.0119i

0.5000 + 1.7874i

0.5000 + 0.0119i

0.5000 - 0.8394i

0.5000 - 0.0119i

The Fig-7 shows that cognitive user navigating the beam toward 00 and self-regulating nulls toward PU at angles 010− ,

080− , 010 and 085 respectively. The weights corresponding to angles are given in table-5

W1 W2 W3 W4 0.5000 - 1.7874i

0.5000 - 0.0119i

0.5000 + 1.7874i

0.5000 + 0.0030i

0.5000 + 1.7874i

0.5000 + 0.0119i

0.5000 - 1.7874i

0.5000 - 0.0030i

The Fig-8 shows that cognitive user navigating the beam toward 05 and self-regulating nulls toward PU at angles 010− ,

080− , 010 and 080 respectively. The weights corresponding to angles are given in table-6

W1 W2 W3 W4

0.5000- 0.5000 + 0.5000 + 0.5000 +

1.1514i 0.0568i 3.6575i 0.0811i 0.1700 + 1.2437i

0.4967 + 0.0806i

1.4704 - 3.3861i 0.5033 +

0.0571i

Fig -3 Navigating angle at 00 and nulls at 010− , 080− , 010and 080

Fig -4 Navigating angle at 00 and nulls at 020− , 080− , 010and 080

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Table-2

Table-3

Table-4

Table-5

Table-6

Fig -5 Navigating angle at 00 and nulls at 010− , 085− , 010and 080

Fig -6 Navigating angle at 00 and nulls at 010− , 080− , 020

and 080

Fig -7 Navigating angle at 00 and nulls at 010− ,080− , 010 and 085

Fig-8 Navigating angle at 05 and nulls at 010− , 080− ,010 and 080

4. Conclusion:

Self-regulating null method is used for generating navigation and optimized the weights corresponding to PU angles.Only subsequent weight value required to change if the change in the direction of any PU takes position. The advantage of complex weight is that the number of navigating nulls is maintained to their maximum value. By using ( 1)M M − complex weights we can navigate ( 1)M − nulls in uniform linear array of M elements. Side-lobes are able to be control when the numbers of navigating nulls are more than the PU to be suppress. In spite of Computational power due to weights increases but efficiency enhanced. The networks power will be minimize whereas the spectral efficiency of the

cognitive radio force to increased due to the navigation of main lobe toward the cognitive user.

REFERENCES

[ 1]. Mitola, J., “Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio,” Doctor of Technology, Royal Inst. Technol. (KTH), Stockholm, Sweden, 2000. [2]. Federal Communications Commission, “Spectrum Policy Task Force Report, ET Docket,” no. 02-135, Nov 2002. [3]. Haykin, S., “Cognitive Radio: Brain Empowered Wireless Communication,” IEEE Journal in Selected Areas of Communications, vol. 23, no. 2, pp. 201-210, Feb 2005. [4]. Cabric, D., Tkachenko, A., Broedersen, R. W., “Spectrum Sensing Measurements of Pilot, Energy, and Collaborative Detection,” In Proceedings of IEEE Military Communications Conference (MILCOM), Washington, D.C., USA, Oct 2006. [5]. Budiarjo, I., Lakshmanan, M. K., Nikookar, H., “Cognitive Dynamic Access Techniques,” Wireless Pers Commun., 2008. [6] Zhu Ji, K. J. Ray Liu, “COGNITIVE RADIOS FOR DYNAMIC SPECTRUM ACCESS – Dynamic Spectrum Sharing: A Game Theoretical Overview,” Communications Magazine, IEEE Vol. 45, Issue 5, pp. 88 - 94 May 2007. [7] Schell, S.V., Calabretta, R.A., Gardner, W.A., Agee, B.G., “Cyclic MUSIC algorithms for signal-selective direction estimation,” Acoustics, Speech, and Signal Processing, 1989. ICASSP-89, International Conference on 23-26, vol.4, pp.2278 – 2281 May 1989. [8] Simon Yiu, Mai Vu, and Vahid Tarokh, “Interference Reduction by Beam-forming in Cognitive Networks”,Global Telecommunications Conference, 2008. [9] A. Jovicic, P. Viswanath, .Cognitive Radio: An Information-Theoretic Perspective.IEEE Transactions on Information Theory Vol. 55(9), pp. 3945-3958, September 2009. [10] Senhua Huang, Zhi Ding, Xin Liu, “Non-Intrusive Cognitive Radio Networks Based on Smart Antenna Technology,” Global Telecommunications Conference, 2007.GLOBECOM '07, IEEE 26-30 pp, 4862 – 4867 Nov. 2007 . [11]. Lian, X., Nikookar, H., Zhou, J., “Adaptive Robust Beam-formers for Cognitive Radio,” IEEE Wireless Technology, 2008 (EuWiT), pp. 103-106, Oct 2008. [12]. Lian, X., Nikookar, H., Ligthart, L. P., Zhou, J., “Adaptive OFDM Beam-former with Constrained Weights for Cognitive Radio,” IEEE 69th Vehicular Technology Conference (VTC Spring), pp. 1-5, Apr 2009.

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