Research Article Dynamic Interference Control in OFDM ...downloads.hindawi.com/journals/ijdsn/2015/870607.pdf · Research Article Dynamic Interference Control in OFDM-Based Cognitive

Research ArticleDynamic Interference Control in OFDM-Based CognitiveRadio Network Using Genetic Algorithm

Hamza Khan and Sang-Jo Yoo

The School of Information and Communication Engineering Inha University Incheon 402-020 Republic of Korea

Correspondence should be addressed to Sang-Jo Yoo sjyooinhaackr

Received 3 June 2015 Revised 14 September 2015 Accepted 15 September 2015

Academic Editor Lin Chen

Copyright copy 2015 H Khan and S-J Yoo This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

In OFDM-based cognitive radio networks minimizing the interference caused to the primary user (PU) by the substantial amountof out-of-band (OOB) emission is a great challenge In this paper we propose a dynamic interference control method using theadditive signal side lobe reduction technique and genetic algorithm (GA) in CR-OFDM systems Additive signal side lobe reductiontechnique is based on adding a complex array tomodulated data symbols in the constellation plane for side lobe reduction inOFDMsystem In the proposedmethod GA generates optimum additive signal which can effectively reduce theOOB signal interference tothe primary systemTheGAalso strives to keep the interference below a predefined tolerable limit and at the same time itmaximizessecondary userrsquos transmission opportunity The results show that the side lobes of the OFDM-based secondary user signal can bereduced by up to 38 dB and the PU interference tolerable limit can be satisfied at the cost of a minor addition in bit error rate (BER)The results further show that the proposed method delivers better performance as compared to non-GA additive signal method interms of side lobe reduction as well as BER

1 Introduction

The inefficient usage of existing spectrum can be improvedthrough opportunistic access to the licensed bands by sec-ondary users (SUs) without interfering or keeping the inter-ference under a tolerable level to the existing primary users(PUs) Cognitive radio (CR) technology enables the SUs todetermine which portion of the spectrum is not used by thePUs [1] The overall implementation of the cognitive radionetwork (CRN) consists of spectrum sensing interferencecontrol and dynamic spectrum access Spectrum sensingand dynamic spectrum access techniques allow determiningwhich portion of the spectrum is available selecting the bestavailable channel coordinating access to this channel withother users and vacating the channel when a licensed useris detected The CR users also need to make sure that there isno harmful interference caused to the PUs with interferencecontrol [2 3]

Orthogonal frequency-division multiplexing (OFDM)is considered an attractive candidate in CRNs because ofhaving the quality of transmitting over the noncontiguous

frequency bands OFDM is also a good option for realizinga transmission system which does not require a continuoustransmission band Therefore it is suitable for spectrumsharing in CRNs [4] However a major trade-off of CR-OFDM signals is their large out-of-band (OOB) side lobepower The leakage power can greatly interfere with theexisting neighboring primary transmissions It is importantto minimize these side lobes to keep the interference underthe tolerable level in order to allow spectrum sharing withprimary system

Several techniques have already been proposed forOFDM side lobe suppression In [4] subcarriers lying at theborder of the OFDM spectrum are deactivated by insertingguard bands and windowing of the transmitted signal infrequency domain However inserting a guard band resultsin wastage of the available bandwidth and windowing theOFDM transmitted signal results in prolonged symbol dura-tion In [5] and [6] techniques with cancellation carrierand weighting the subcarrier are proposed respectivelyBoth of these techniques require complicated optimizationwhich makes them very hard to implement in real time In

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2015 Article ID 870607 10 pageshttpdxdoiorg1011552015870607

2 International Journal of Distributed Sensor Networks

GA optimizationof additive signal

Secondary systempower spectral density

level estimationSi

de lo

besu

ppre

ssio

n pa

ram

eter

addi

ng

Parallelto serialconverter

N-pointIFFT

Add cyclicprefix andparallel to

serialconverter

Primaryinterference

thresholdlevel

Primarysignal

detector

Input bits

OFDM system blocksAdditional CR blocksInterference reduction blocks

Sensing

DASymbolmapper

xn

x1

xN

P(flowast)

X1 = x1 + d1

XN = xN + dN

X(1)998400

X(N)998400

flowast

fc

n = 1 2 N

d = (d1 d2 dN)

Pth

Figure 1 OFDM system combined with proposed dynamic interference control model including GA and CR blocks

[7] multiple choice sequence (MCS) technique is used forreducing the side lobe power level However the throughputof the system is reduced due to the amount of transmission ofside information other than data

In this paper we propose a dynamic interference controltechnique for OFDM-based CR system In additive signalmethod (ASM) [8] an additive complex signal sequenceis added with the original transmit data sequence to betransmitted by the OFDM user In the proposed techniquewe used genetic algorithms (GA) to find an optimal additivesequence formajor side lobe suppression GA is a search tech-nique used to find the best possible solution to optimizationproblems [9] It is an evolutionary algorithm which utilizesevolutionary biological techniques like mutation crossoverand survival of the fittest The GA converges for an optimaladditive sequence to be added with the symbols transmittedby the OFDM-based SU with constraints like keeping the SUOOB transmission under PU tolerable limit and lowBERThemain contributions of this paper are as follows

(i) We develop a dynamic interference control modelusing GA to efficiently reduce the interference at 119891lowastin which 119891lowast is the closest boundary frequency ofthe primary system sensed to the secondary systemOur proposed scheme guarantees the reduction of SUinterference at119891lowast down to the acceptable interferencethreshold defined by PU

(ii) We propose a new fitness function of the GA toeffectively manage the SUrsquos interference to the PUswithin the defined interference safe region Using theproposed fitness function additive complex signal isfast converged to the optimum parameter set

(iii) With the precise side lobe reduction control wecan not only avoid any harmful interference to the

primary users but also reduce the possible BER lossdue to the deviations in the constellation

The rest of this paper is organized as follows In Section 2the system model is introduced In Section 3 the pro-posed scheme is explained Simulation results are shown inSection 4 Finally we conclude this paper in Section 5

2 System Model

We consider an OFDM-based CR system throughout thispaper In this section first we describe the proposed dynamicinterference control CR-OFDM model followed by detailedexplanation of the GA optimization model

21 Interference Control OFDM-Based Cognitive RadioModelOFDM has recently been considered as a preferred schemeto be applied in CR systems [2 4] In CR system every CRnode needs to sense the spectrum by using spectral analysistechniques Fast Fourier transform (FFT) can be used forspectral analysis while at the same time acting as an OFDMdemodulator The shortcoming of OFDM is the large sidelobes of the frequency response filters that characterize thechannel associated with each subcarrier The large side lobesresult in significant interference to other SUs and PUs Totackle this problem in CR we design a dynamic interferencecontrol system for OFDM-based CR users As shown inFigure 1 the side lobe suppressionOFDMmodel proposed in[8] is further extended by using GA and applied to CRNs AnOFDM system with 119873 subcarriers is considered The inputbits aremapped to symbols by applying quadrature amplitudemapping (QAM) or phase shift keying (PSK) thus having119873 complex-valued data symbols 119909 = (119909

1 1199092 119909

119873) These

data symbols are serial to parallel converted and fed intoa side lobe suppression unit The suppression unit adds a

International Journal of Distributed Sensor Networks 3

1

Im

Re1

Xn = xn + dn

dn

xn

R

minus1

minus1

Figure 2 Additive signal example in 4-QAM constellation

complex additive signal array 119889 = (1198891 1198892 119889

119873) with the

data symbols forming a modified complex array of symbols119883 = (119883

1 1198832 119883

119873) The additive signal is determined by

the proposed GA optimization algorithm that can reduce theinterference under the PUrsquos tolerable interference thresholdThe threshold is automatically computed using primarysignal bandwidth and allowed interference level Figure 2shows the addition of additive signal 119889 with the data symbols119909 in which 119889

119899is chosen appropriately within a limited circle

of radius |119877| for BER control The resultant data symbols canbe written as

119883119899= 119909119899+ 119889119899 (1)

Finally the resulting data symbols are modulated onto thesubcarriers using inverse fast Fourier transform (IFFT)Thena guard interval in the form of cyclic prefix is added

To avoid causing harmful interference to the PU fre-quency band that is in operation spectrum sensing is neededfor the CRs [10 11] If primary signal structure is known theusual way to detect a primary signal is coherent detection[12] Current alternative techniques for primary detectionare energy detection and feature detection In practice acombination of different techniques may be needed in orderto handle a variety of situations As shown in Figure 1 theprimary signal detector performs spectrum sensing In caseof detection of PU the primary interference threshold 119875thand secondary system OOB power 119875(119891lowast) at frequency point119891lowast are input to the GA block In the proposed method theinterference is evaluated at the frequency point 119891lowast outsidethe main transmission frequency of CR-OFDM according tothe sensing result of the primary signal detector 119891lowast is theclosest boundary frequency of the primary system sensedto the secondary system Figure 3 shows a scenario of CR-OFDM coexisting with a licensed PU Even though the CR-OFDM operating band is quite different from that of primarysystem the side lobes fromOFDM-basedCRare far above theprimary userrsquos acceptable interference threshold Such a casecould be extremely harmful in terms of primary interferenceand will be addressed in this paper

To bring the interference to the primary system belowacceptable levels further side lobe reduction is required The

0

Normalized frequency (MHz)

Nor

mal

ized

PSD

0 1 115 120 125 150

OOB recording

Minimum tolerableinterference

Primary appearance

OFDM signal

minus20

minus40

minus60

minus80

minus100

minus120minus118 minus115 minus1

Figure 3 OFDM-based CR coexisting with PU

GA is used to derive the optimum additive signal 119889lowast whichguarantees that themaximum interference to primary systemis always lower than the predefined threshold It should alsobe noted that generally lower side lobes are achieved by eitherreducing secondary transmission power or sacrificing BERIn this paper using GA we can maximize the secondarytransmission opportunity and minimize the BER increase bymaintaining the interference level119891lowast under the threshold butvery close to the threshold value dynamically The GA keepsconsidering 119875(119891lowast) and 119875th and generates the optimum set of119889 iteratively Afterwards the values are added with the datasymbols as given in (1) The detailed GA optimization [13 14]algorithm is explained in the proceeding section

In addition to the conventional OFDM technology theproposed GA optimization method can be applied to otherevolutional versions of multicarrier systems such as filterbank multicarrier (FBMC) and generalized frequency divi-sion multiplexing (GFDM) systems [15 16] FBMC systemsare based on the orthogonal lapped transform [17] and filterbank theory [18] GFDM systems add flexibility of choosing asuitable pulse such as a rooted raised cosine (RRC) or raisedcosine (RC) This pulse shaping technique brings about theadvantage of out-of-band radiation Like FBMC and GFDMall the evolutional versions of OFDM involve mapping ofbits into symbols to be transmitted Therefore similar to theOFDM system model the information bits are first mappedto symbols 119883 drawn from the complex QAM constellationwhich allows us to apply the proposed model effectively forthe FBMC and GFDM systems by adding the additive vector119889 optimized by GA to the symbol vector 119883 Figure 4 showsthe system of FMBC in which the proposed optimizationmechanism is cooperated

22 The Genetic Algorithm (GA) Model GA is a type of fea-ture selection algorithm based on the idea of natural selectionand natural genetics [9] This search is performed based onan objective function also called a fitness function The GAtries to generate results such that the fitness function reachesa minimum or maximum value and finds the solutions of


Cosinefilter bank

N2 delay

Constellation mapping

Bits

Geneticalgorithm

SensingPU signaldetector

PUrsquos interferencethreshold level

SU signal levelestimation

vector

FBMC blocksProposed interference reduction blocks

Sine

filter

bank

Real (Xn)

Imag (Xn)

Optimum dn

xen

Xn = xn + dn

Sn

xSn

Figure 4 Block diagram of an implementation of the FBMC transmitter with proposed interference reduction scheme

variables for the best-possible result Individuals or cur-rent approximations are encoded as strings (chromosomes)composed over some numbers referred to as genes so thatthe genotypes (chromosome values) are uniquely mappedonto the real decision variables (phenotypic) domain Therepresentation used in this paper is binary numbers 0 1A problem with 119873 variables (in our case the additive signalvector) may be mapped onto the chromosome with selectednumber of bits The number of bits is reflecting the range ofthe decision variables (range of |119877|) Binary values in chromo-some do not give any information related to the problem bythemselves unless a meaning is applied to the representationHaving decoded the chromosome into the decision variabledomain it is possible to assess the performance or fitness ofindividual members of the population However the searchprocess will operate on the encoding of the decision variablesrather than the decision variables themselves

The initial random population is subject to treat withcrossover and mutation after being evaluated with the pro-posed fitness function The evaluation of the populationand the improvement of additive signal are continued untilthe stopping criteria are reached The chromosome repre-sentation in our evaluation is [119889

1 1198892 119889

119873] therefore the

population consists of the following119872 individuals

[[[[[[

[

11988911

11988912

sdot sdot sdot 1198891119873

11988921

11988922


d

1198891198721

1198891198722

sdot sdot sdot 119889119872119873

]]]]]]

]

Individual 1Individual 2

Individual 119872

(2)

where119889119872119873

represents a complex variable for the119873th additivesignal of the119872th individual

3 The Proposed GA-Based InterferenceControl Scheme

In the proposed scheme the side lobe suppression of OFDM-based CR is achieved by adding the frequency domain value119909119899with an additive value 119889

119899 This addition is done in such

a way that the resulting power spectral density (PSD) in PUregion that is primary operating frequency is less than thepredetermined threshold 119875th Now the optimization problembecomes as follows

119875 (119891) =1

119873FFT

1003816100381610038161003816100381610038161003816100381610038161003816

119873FFT

sum119899=1

radic119901119899(119909119899+ 119889119899)

sdot int(1+120572)119905

119906

minus(1+120572)119905119906

119892 (119905) 119890minus1198942120587(119891minus119891

119899)119905119889119905

1003816100381610038161003816100381610038161003816100381610038161003816

2

(3)

where 119875(119891) is interference at frequency 119891 and 119889119899is the

additive signal on subcarrier 119899119873FFT(119873) is the number of FFTpoints 119879

119906is the useful signal duration 119892(119905) is the window

function and 120572 is the roll-off factor of the window while 119891119899

119901119899 and 119909

119899are the respective frequency allocated power and

symbol fromQAMmapping on the subcarrier 119899 respectivelyEquation (3) shows that by setting the parameters such asallocated power (119901

119899) symbol amplitude (119878

119899= 119909119899+ 119889119899) and

window (119892(119905)) we can suppress the side lobes power at PUfrequency which is denoted as 119875(119891) In this paper to achievethe optimal interference control we have decided to alter thesymbol amplitude by making use of [119889

1 1198892 119889

119873] In order

to avoid the BER loss the range of 119889119899is kept limited to a circle

with radius |119877| [8]In this paper we also propose a fitness function for the

optimization of 119889 = [1198891 1198892 119889

119873] The algorithm strives

to reduce the OOB interference at a single frequency point119891lowast outside the transmission bandwidth of the CR-OFDM


CR-OFDM

Primary

Primary band

flowast

(a)

Primary BPrimary A

flowast

(b)

Figure 5 Target frequency on primary band

The frequency 119891lowast is the target point to control CR-OFDMsystem side lobes in which 119891lowast is the closest frequency of thedetected primary system to the CR-OFDM system as shownin Figure 5 In this paper we only consider frequency binsfor the integer multiple harmonics of CR-OFDM subcarrierIt is obvious that if we maintain the side lobe at 119891lowast underthe threshold then for all primary userrsquos operating bandthe interference is always lower than the threshold becauseSUrsquos side lobe function 119875(119891) is a monotonically decreasingfunction To guarantee that secondary transmission added tothe existing interference must not exceed the allowed limitat the licensed receiver the following conditions should besatisfied

119875119879119868(119891lowast) = 120575119875 (119891lowast) + 119875

119868(119891lowast) le 120581119879

119871(119891lowast) (4)

where 119875119879119868(119891lowast) is the total interference power to the primary

receiver 119875(119891lowast) is the unlicensed user transmit power 119879119871(119891lowast)

is the interference temperature limit 120581 is the Boltzmannconstant equal to 13806503 times 10minus23 and 120575 is the propagationloss factor Therefore the threshold 119875th can be determined as

119875th =[120581119879119871(119891lowast) minus 119875

119868(119891lowast)]

120575 (5)

In general the larger range diversity of119889 = [1198891 1198892 119889119873]

results in more side lobe reduction that is smaller 119875(119891lowast)However it also results in larger bit error rate because thederived symbol set [119909

1+ 1198891 1199092+ 1198892 119909

119873+ 119889119873] becomes

weaker to noise and fading effect Therefore we derive anoptimum 119889lowast that keeps 119875(119891lowast) close to the threshold 119875th Nowour optimization problem can be reduced to

119889lowast = arg119889

min 1003816100381610038161003816119875 (119891lowast) minus 119875th

1003816100381610038161003816 (6)

The entire procedure of the proposed GA-based inter-ference control scheme is shown in Algorithm 1 At firstchromosomes are randomly initialized The resultant chro-mosomes then transform to phenotypes (complex variables)The additive vectors are analyzed in OFDM system iterativelyas illustrated in Figure 1 The GA converges to the optimum119889lowast according to the following minimizing fitness function

(i) if 119875(119891lowast) gt 119875th

119865 =100381610038161003816100381610038161003816100381610038161003816

119875 (119891lowast) minus 119875th119875 (119891lowast) + 119875th

100381610038161003816100381610038161003816100381610038161003816times 1205721

(7a)

(ii) if 119875th minus Δ le 119875(119891lowast) le 119875th

119865 = minus100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205722

(7b)

(iii) if 119875(119891lowast) lt 119875th minus Δ

119865 =100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205722 (7c)

where Δ is the marginal range specified for GA to con-verse to the threshold and 120572

1and 120572

2are the weighting

constants to keep the fitness value in interference safe regionThe proposed fitness function is a decreasing functionFigure 6 shows the behavior of the proposed fitness functionAccording to the proposed fitness function the GA tendsto minimize the objective value 119865 in an iterative mannerby making generational improvements in chromosome GAreaches the threshold using (7a) After that the GA furtherconverges down Δ = 5 dB below the threshold using fitnessfunction (7b) to be on the safe side The fitness function (7c)restricts 119875(119891lowast) in interference safe region and does not gobelow119875thminusΔ for BER control It should be noted that119901(119891lowast) lt119875thminusΔ results in smaller SUrsquos OFDM systemmain lobe powereven though it satisfies the interference constraintThereforethe SNR is decreasing and the BER loss is also increasingThisbehaviour is not desirable and that is why fitness function 119865is designed to increase at the region of 119875(119891lowast) lt 119875th minus Δ with1205722parameterIn recent 5G OFDM-based LTE (Long Term Evolution)

and WiMAX (Worldwide Interoperability for MicrowaveAccess) systems one of the key components is the RF


Initialize GA parameters(2) Initialize chromosomes create genotypes

Convert chromosomes to phenotypes(4) Initialize OFDM parameters

whilemaximum number of generations is not reached do(6) Generate bit stream

Modulate using 4-QAM 119909119899generated

(8) for 119894 = 1 119894 lt size(Phenotypes) 119894 + + do119883119899= 119909119899+ phenotype(119894 ) add the rows (individuals) of chromosome iteration wise with 4-QAMmodulated symbols

(10) for 119895 = 1 119895 lt frequency 119895 + + doPerform inverse fast fourier transform using newly created symbols

(12) Add cyclic prefixend for

(14) Compute 119875(119891lowast) of current individualif 119875(119891lowast)[119894] gt 119875ththen

(16) 119865 =100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205721

else(18) if 119875th minus Δ le 119875(119891lowast)[119894] le 119875th

then

119865 = minus100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205722

(20) elseif 119875(119891lowast)[119894] lt 119875th minus Δthen

(22) 119865 = |(119875(119891lowast) minus 119875th)(119875(119891lowast) + 119875th)| times 1205722 Evaluate and save fitness values of all individuals and return the array

end if(24) end if

end if(26) Calculate the lowest 119875(119891lowast) and fitness value of current generation

end for performance check of current population(28) Calculate the best fitness value in current generation

Assign rank to individuals(30) Select individuals on the basis of fitness Best individuals which flows throughout

Perform crossover(32) Perform mutation

Reinsert the best individuals in current population(34) Evaluate the chromosome in problem domain Repeat from line 8 to 27

Calculate the best fitness value in current generation(36) end while

Algorithm 1 GA-based interference control

power amplifier Mostly the RF amplifiers used commerciallyare not linear There are several researches on the effectof nonlinear power amplifier on the spectral regrowth inwireless communication systems [19] In general for a closed-form expression for the autocovariance function of the PAoutput its Fourier transform yields the output power spectraldensity function Usual nonlinear effects on the transmittedOFDM signal are spectral spreading of the OFDM signal andwarping of the signal constellation in each signal [20 21] Webelieve that our proposed scheme is generalized enough toincorporate with the nonlinear PA spectral modelsThe effectof nonlinear power amplifier only requires the change of thepower spectral density function of (3) The implementationand analysis that consider the nonlinear power amplifier areleft as a further study

4 Simulation Results

A simple OFDM system scenario is considered We usedQAM modulation scheme applied on 128 subcarrierswhereas rectangular windowing is used

The simulation parameters are shown in Table 1 Thereare 128 parameters [119889

1 1198892 119889

128] in GA which are actually

the additive signals and also equal to the modulated symbolsOne complex parameter variable is expressed by 8 bits inchromosome Minimizing fitness functions defined in (7a)(7b) and (7c) are used and the primary interference threshold119875th is kept either at minus30 dB or at minus60 dB The normalizedfrequency of the secondary OFDM system is minus034 to+034 as shown in Figure 7 Throughout the simulation weconsider different 119891lowast from 04 to 10 (in terms of normalized


0

Interferencesafe region

minus15

minus20

minus25

minus30

minus35

minus40

minus45

minus50

minus55

minus60

minus65

minus70

minus75

minus80

F

1205721

12057221205722

1205721 = 2 1205722 = 1

Δ = 5dB

Δ

P(flowast) (dB)

Pth = minus60dB

Pth

Figure 6 Minimizing fitness function versus 119875(119891lowast)

Table 1 Simulation parameters

Parameters ValuePopulation size 40Crossover rate 07Mutation rate 0001 to 001Iterations 20119873FFT(= 119873) 128119875th minus60 dBΔ 5 dB1205721

21205722

1119891lowast 04 to 10

frequency) on the right side of the OFDM signal Figure 7further shows the effect of the interference control of theproposed mechanism As shown in Figure 7(a) originalOFDM system side lobes give harmful interference to theprimary users because at the primary band the interferencelevel from the secondary OFDM system exceeds the primaryuserrsquos allowable interference threshold When the proposedGA-based side lobe reduction mechanism is applied asshown in Figure 7(b) the interference to the primary usersis tightly controlled within the interference safe region

Figure 8 shows the change of fitness value as GA gener-ations are moving on The decreasing objective value showsthe decrease of side lobe power at 119891lowast = 1 For Figure 8additive complex variable range |119877| for boundary 119889 vector isset to the radius of 05 and 03 In both cases GA achieves fastconvergence to the optimum value in just 15 generations Incase of |119877| = 03 GA converges slightly slower because of thelimited search space This shows that as we increase |119877| wecan get better reduction in the side lobes

Figure 9 shows the obtained fitness value when we vary119891lowast from 04 to 10 Different 119891lowast indicates different primaryappearing frequency band In both variable search radiusesthe GA reaches the optimal value from 06 and onwards Weobserve that the fitness performance of |119877| = 05 is slightlybetter than that of |119877| = 03 As we can see in Figure 9at 119891lowast = 04 the achieved fitness value is little higher than

minus1 minus08 minus06 minus04 0 04 06 08 1 12 13minus90

minus80

minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10


Pow

er sp

ectr

al d

ensit

y (d

B)

PUmainband

Side lobesinterfering

SU mainband

PUrsquosallowable

interferencethreshold

(a) PSD of the original secondary OFDM signal and primary signal

minus90

minus80

minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10


PUmainband

minus1 minus08 minus06 minus04 0 04 06 08 1 12 13Normalized frequency (MHz)

Pow

er sp

ectr

al d

ensit

y (d

B)

(b) PSD of the proposed interference controlled secondary OFDM signaland primary signal

Figure 7 Power spectral density of primary and secondary signals

0 2 4 6 8 10 12 14 16minus005

0

005

01

015

02

025

03

Generations

Fitn

ess (

F)

|R| = 05

|R| = 03

Figure 8 GA fitness convergence versus the number of iterations at119891lowast = 1


1minus004minus003minus002minus001

0001002003004

Fitn

ess (

F)

|R| = 05

|R| = 03

Frequency point flowast04 05 06 07 08 09

Figure 9 Acquired GA fitness (119865) at last iteration on the respectivetarget frequency 119891lowast

0 2 4 6 8 10 12 14EbNo

BER

100

10minus1

10minus2

OFDM reference|R| = 05 f

lowast= 1

|R| = 05 flowast= 04


|R| = 03 flowast= 1

Figure 10 BER versus EbNo comparison of OFDM signal inRayleigh fading with the effect of optimum 119889lowast acquired by GA atdifferent cases

the optimal point It indicates that the controlled interferencelevel is slightly higher than the tolerable threshold Becausethe primary operating frequency band is too close to thesecondary system it is very hard to exactly control theinterference always below the threshold In this case we mayset 119875th little higher than the required tolerable level

Figure 10 illustrates BER versus EbNo curves of OFDMsystem with the effect of 119889lowast derived from different cases A 4-tap frequency selective Rayleigh fading channel is consideredand 256 random bits are transmitted by the transmitter Thefigure shows that as we increase |119877| the effect of acquired 119889lowaston BER performance also increases However the BER lossof the proposed method is not significant because of limiting|119877| under 05

Figure 11 shows the BER values of OFDM systemwith theeffect of acquired 119889lowast sets by setting different target frequency119891lowastWeobserved that in case of119891lowast being closest to theOFDM

0085

009

0095

01

BER

1

|R| = 05

|R| = 03


Figure 11 BER versus 119891lowast comparison of OFDM signal in Rayleighfading with EbNo fixed at 8 dB and with the effect of optimum 119889lowast

acquired by GA

main band we got a higher BER Additionally in case ofvariable radius 05 we got a higher BER as compared to |119877| =03 because the larger variable radius causes more distortionin the constellation and consequently causes the higher BER

Figure 12 shows the comparison of the proposed schemewithASM in power spectral density for theOFDMsignalTheshortest primary appearance frequency point 119891lowast is 10 |119877| =05 is used for both the proposed method and ASM For theproposed method the population size is 40 and the numberof iterations is 16 For ASM from the randomly generatedone thousand sets of additive signals (119889

119899) an optimum set is

selected which generates the minimum interference at119891lowast Asshown in Figure 12(a) in which the allowable threshold levelof the primary system is minus60 dB the PSD of the proposedscheme successfully reduces the interference at 119891lowast withinthe interference safe region However ASM fails to controlthe side lobe interference under the required interferencethresholdThe PSD of ASM at 119891lowast (=10) is about 15 dB higherthan the threshold Meanwhile in the proposed scheme theside lobe suppression is acquired by the precise optimizationof GA Figure 12(b) shows the results when the allowablethreshold level of the primary system is minus30 dB As we cansee the PSD acquired from the proposed scheme stays inthe interference safe region Meanwhile the PSD from ASMshows too much reduction of side lobes At 119891lowast ASM reduces15 dB more below the threshold In general more reductionin side lobes will causemore distortion in the constellation InASM the side lobe reduction ofOFDMsignal is donewithoutconsideration of any constraints such as BER or interferencethreshold This may cause too much reduction in the sidelobes as in Figure 12(b) so it consequently can increase theBER

Figure 13 shows the BER comparison of the proposedscheme and ASM when the interference threshold is minus30 dBWe can see that ASM scheme generates higher BER than thatof the proposed scheme

5 Conclusion

In this paper a technique that can dynamically control inter-ference to PUs caused by OFDM-based SUs is proposed Themethod is based on a small shift of the symbol in the symbolconstellation plane by the addition of an additive signal This


minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10

Interference safe region


Pow

er sp

ectr

al d

ensit

y (d

B)

minus1 minus08 minus06 minus04 minus02 0 02 04 06 08 1

OFDM referenceProposed schemeASM(a) The interference threshold = minus60 dB

minus50

minus40

minus30

minus20

minus10

0

10



Pow

er sp

ectr

al d

ensit

y (d

B)


OFDM referenceProposed schemeASM(b) The interference threshold = minus30 dB

Figure 12 Comparison of the proposed method with ASM in power spectral density for the OFDM signal

0 2 4 6 8 10 12 14EbNo

BER

OFDM referenceProposed schemeASM

100

10minus1

10minus2

Figure 13 BER comparison of proposed scheme and ASM (theallowable interference threshold = minus30 dB)

addition can lead to significant interference suppression ofthe OFDM-based SU to PUsThe interference to the primaryuser is avoided by the precise optimization of additive signalusing GA which helps satisfy the interference thresholddefined by any licensed system Simulation results show thatour proposed scheme is effective in minimizing interferencein OFDM-based CR systems The overall achievable sidelobe suppression is 38 dB Additionally the results show thatincreasing the radius of additive signal causes small loss inSNR performance but achieves better side lobe suppressionWe observed that the dynamic additive signal optimizationcan successfully suppress the secondary systemrsquos side lobes

and control the interference to the primary system underthe allowable level with small loss in BER performance Theresults further show that the performance of the proposedscheme is controlled as compared to non-GA ASM-basedside lobe reduction scheme in terms of BER and also providesbetter side lobe reduction

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This research was supported by the MSIP (Ministry ofScience ICT and Future Planning) Korea under the ITRC(Information Technology Research Center) support program(IITP-2015-H8501-15-1019) supervised by the IITP (Institutefor Informationamp communications Technology Promotion)

References

[1] N Hao and S-J Yoo ldquoInterference avoidance throughputoptimization in cognitive radio ad hoc networksrdquo EURASIPJournal on Wireless Communications and Networking vol 2012article 295 2012

[2] T A Weiss and F K Jondral ldquoSpectrum pooling an innovativestrategy for the enhancement of spectrum efficiencyrdquo IEEECommunications Magazine vol 42 no 3 pp S8ndashS14 2004

[3] Federal Communications Commission ldquoSpectrum policy taskforce reportrdquo ET Docket 02-135 Federal CommunicationsCommission 2002

[4] T Weiss J Hillenbrand A Krohn and F K Jondral ldquoMutualinterference in OFDM-based spectrum pooling systemsrdquo in


Proceedings of the IEEE 59th Semiannual Vehicular TechnologyConference pp 1873ndash1877 May 2004

[5] S Brandes I Cosovic and M Schnell ldquoSidelobe suppressionin OFDM systems by insertion of cancellation carriersrdquo inProceedings of the IEEE 62nd Vehicular Technology Conference(VTC rsquo05) pp 152ndash156 Dallas Tex USA September 2005

[6] I Cosovic S Brandes and M Schnell ldquoSubcarrier weightinga method for sidelobe suppression in OFDM systemsrdquo IEEECommunications Letters vol 10 no 6 pp 444ndash446 2006

[7] I Cosovic and T Mazzoni ldquoSuppression of sidelobes in OFDMsystems by multiple-choice sequencesrdquo European Transactionson Telecommunications vol 17 no 6 pp 623ndash630 2006

[8] I Cosovic and T Mazzoni ldquoSidelobe suppression in OFDMspectrum sharing systems via additive signal methodrdquo inProceedings of the 65th IEEE Vehicular Technology Conference(VTC rsquo07) pp 2692ndash2696 IEEE Dublin Ireland April 2007

[9] J D E Goldberg Genetic Algorithms in Search Optimizationand Machine Learning Addison-Wesley Longman PublishingBoston Mass USA 1989

[10] A Ghasemi and E S Sousa ldquoSpectrum sensing in cognitiveradio networks requirements challenges and design trade-offsrdquo IEEE Communications Magazine vol 46 no 4 pp 32ndash392008

[11] T Yucek and H Arslan ldquoA survey of spectrum sensing algo-rithms for cognitive radio applicationsrdquo IEEE CommunicationsSurveys and Tutorials vol 11 no 1 pp 116ndash130 2009

[12] D Cabric S MMishra and RW Brodersen ldquoImplementationissues in spectrum sensing for cognitive radiosrdquo in Proceedingsof the Conference Record of the 38th Asilomar Conference onSignals Systems and Computers vol 1 pp 772ndash776 IEEEPacific Grove Calif USA November 2004

[13] Y Zhou S Pagadarai and A M Wyglinski ldquoCancellationcarrier technique using genetic algorithm for OFDM sidelobesuppressionrdquo in Proceedings of the IEEE Military Communica-tions Conference (MILCOM rsquo08) pp 1ndash5 San Diego Calif USANovember 2008

[14] E Zitzler and L Thiele ldquoMultiobjective evolutionary algo-rithms a comparative case study and the strength paretoapproachrdquo IEEETransactions onEvolutionaryComputation vol3 no 4 pp 257ndash271 1999

[15] S Dikmese S Srinivasan M Shaat F Bader and M RenforsldquoSpectrum sensing and resource allocation formulticarrier cog-nitive radio systems under interference and power constraintsrdquoEURASIP Journal on Advances in Signal Processing vol 2014article 68 2014

[16] B Farhang-Boroujeny and R Kempter ldquoMulticarrier commu-nication techniques for spectrum sensing and communicationin cognitive radiosrdquo IEEE Communications Magazine vol 46no 4 pp 80ndash85 2008

[17] H S Malvar ldquoExtended lapped transforms properties appli-cations and fast algorithmsrdquo IEEE Transactions on SignalProcessing vol 40 no 11 pp 2703ndash2714 1992

[18] P P VaidyanathanMultirate Systems and Filter Banks PrenticeHall 1993

[19] G T Zhou and J S Kenney ldquoPredicting spectral regrowth ofnonlinear power amplifiersrdquo IEEE Transactions on Communi-cations vol 50 no 5 pp 718ndash722 2002

[20] MMajidi AMohammadi andAAbdipour ldquoAccurate analysisof spectral regrowth of nonlinear power amplifier driven by

cyclostationary modulated signalsrdquo Analog Integrated Circuitsand Signal Processing vol 74 no 2 pp 425ndash437 2013

[21] I Ahmad A I Sulyman A Alsanie A K Alasmari and S AAlshebeili ldquoSpectral broadening effects of high-power ampli-fiers inMIMOndashOFDM relaying channelsrdquo EURASIP Journal onWireless Communications and Networking vol 2013 article 322013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of



RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



GA optimizationof additive signal

Secondary systempower spectral density

level estimationSi

de lo

besu

ppre

ssio

n pa

ram

eter

addi

ng

Parallelto serialconverter

N-pointIFFT

Add cyclicprefix andparallel to

serialconverter

Primaryinterference

thresholdlevel

Primarysignal

detector

Input bits

OFDM system blocksAdditional CR blocksInterference reduction blocks

Sensing

DASymbolmapper

xn

x1

xN

P(flowast)

X1 = x1 + d1

XN = xN + dN

X(1)998400

X(N)998400

flowast

fc

n = 1 2 N

d = (d1 d2 dN)

Pth

Figure 1 OFDM system combined with proposed dynamic interference control model including GA and CR blocks

[7] multiple choice sequence (MCS) technique is used forreducing the side lobe power level However the throughputof the system is reduced due to the amount of transmission ofside information other than data

In this paper we propose a dynamic interference controltechnique for OFDM-based CR system In additive signalmethod (ASM) [8] an additive complex signal sequenceis added with the original transmit data sequence to betransmitted by the OFDM user In the proposed techniquewe used genetic algorithms (GA) to find an optimal additivesequence formajor side lobe suppression GA is a search tech-nique used to find the best possible solution to optimizationproblems [9] It is an evolutionary algorithm which utilizesevolutionary biological techniques like mutation crossoverand survival of the fittest The GA converges for an optimaladditive sequence to be added with the symbols transmittedby the OFDM-based SU with constraints like keeping the SUOOB transmission under PU tolerable limit and lowBERThemain contributions of this paper are as follows

(i) We develop a dynamic interference control modelusing GA to efficiently reduce the interference at 119891lowastin which 119891lowast is the closest boundary frequency ofthe primary system sensed to the secondary systemOur proposed scheme guarantees the reduction of SUinterference at119891lowast down to the acceptable interferencethreshold defined by PU

(ii) We propose a new fitness function of the GA toeffectively manage the SUrsquos interference to the PUswithin the defined interference safe region Using theproposed fitness function additive complex signal isfast converged to the optimum parameter set

(iii) With the precise side lobe reduction control wecan not only avoid any harmful interference to the

primary users but also reduce the possible BER lossdue to the deviations in the constellation

The rest of this paper is organized as follows In Section 2the system model is introduced In Section 3 the pro-posed scheme is explained Simulation results are shown inSection 4 Finally we conclude this paper in Section 5

2 System Model

We consider an OFDM-based CR system throughout thispaper In this section first we describe the proposed dynamicinterference control CR-OFDM model followed by detailedexplanation of the GA optimization model

21 Interference Control OFDM-Based Cognitive RadioModelOFDM has recently been considered as a preferred schemeto be applied in CR systems [2 4] In CR system every CRnode needs to sense the spectrum by using spectral analysistechniques Fast Fourier transform (FFT) can be used forspectral analysis while at the same time acting as an OFDMdemodulator The shortcoming of OFDM is the large sidelobes of the frequency response filters that characterize thechannel associated with each subcarrier The large side lobesresult in significant interference to other SUs and PUs Totackle this problem in CR we design a dynamic interferencecontrol system for OFDM-based CR users As shown inFigure 1 the side lobe suppressionOFDMmodel proposed in[8] is further extended by using GA and applied to CRNs AnOFDM system with 119873 subcarriers is considered The inputbits aremapped to symbols by applying quadrature amplitudemapping (QAM) or phase shift keying (PSK) thus having119873 complex-valued data symbols 119909 = (119909

1 1199092 119909

119873) These

data symbols are serial to parallel converted and fed intoa side lobe suppression unit The suppression unit adds a


1

Im

Re1

Xn = xn + dn

dn

xn

R

minus1

minus1



119873) with the


1 1198832 119883





119883119899= 119909119899+ 119889119899 (1)




0


Nor

mal

ized

PSD

0 1 115 120 125 150

OOB recording


Primary appearance

OFDM signal

minus20

minus40

minus60

minus80

minus100







Cosinefilter bank

N2 delay


Bits

Geneticalgorithm




vector


Sine

filter

bank

Real (Xn)

Imag (Xn)

Optimum dn

xen

Xn = xn + dn

Sn

xSn




1 1198892 119889



[[[[[[

[

11988911

11988912


11988921

11988922


d

1198891198721

1198891198722

sdot sdot sdot 119889119872119873

]]]]]]

]


Individual 119872

(2)

where119889119872119873






119875 (119891) =1

119873FFT

1003816100381610038161003816100381610038161003816100381610038161003816

119873FFT

sum119899=1

radic119901119899(119909119899+ 119889119899)

sdot int(1+120572)119905

119906

minus(1+120572)119905119906

119892 (119905) 119890minus1198942120587(119891minus119891

119899)119905119889119905

1003816100381610038161003816100381610038161003816100381610038161003816

2

(3)





119901119899 and 119909




119899= 119909119899+ 119889119899) and


1 1198892 119889

119873] In order



optimization of 119889 = [1198891 1198892 119889




CR-OFDM

Primary

Primary band

flowast

(a)

Primary BPrimary A

flowast

(b)



119875119879119868(119891lowast) = 120575119875 (119891lowast) + 119875

119868(119891lowast) le 120581119879

119871(119891lowast) (4)




119875th =[120581119879119871(119891lowast) minus 119875

119868(119891lowast)]

120575 (5)



1+ 1198891 1199092+ 1198892 119909

119873+ 119889119873] becomes


119889lowast = arg119889


1003816100381610038161003816 (6)


(i) if 119875(119891lowast) gt 119875th

119865 =100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205721

(7a)


119865 = minus100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205722

(7b)


119865 =100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205722 (7c)


1and 120572

2are the weighting












(16) 119865 =100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205721


then

119865 = minus100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205722



end if(24) end if












1 1198892 119889




0


minus15

minus20

minus25

minus30

minus35

minus40

minus45

minus50

minus55

minus60

minus65

minus70

minus75

minus80

F

1205721

12057221205722

1205721 = 2 1205722 = 1

Δ = 5dB

Δ

P(flowast) (dB)

Pth = minus60dB

Pth




21205722

1119891lowast 04 to 10





minus80

minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10


Pow

er sp

ectr

al d

ensit

y (d

B)

PUmainband


SU mainband

PUrsquosallowable



minus90

minus80

minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10


PUmainband


Pow

er sp

ectr

al d

ensit

y (d

B)



0 2 4 6 8 10 12 14 16minus005

0

005

01

015

02

025

03

Generations

Fitn

ess (

F)

|R| = 05

|R| = 03




0001002003004

Fitn

ess (

F)

|R| = 05

|R| = 03



0 2 4 6 8 10 12 14EbNo

BER

100

10minus1

10minus2


lowast= 1



|R| = 03 flowast= 1





0085

009

0095

01

BER

1

|R| = 05

|R| = 03



acquired by GA






5 Conclusion



minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10



Pow

er sp

ectr

al d

ensit

y (d

B)



minus50

minus40

minus30

minus20

minus10

0

10



Pow

er sp

ectr

al d

ensit

y (d

B)




0 2 4 6 8 10 12 14EbNo

BER


100

10minus1

10minus2






Acknowledgment


References



























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










1

Im

Re1

Xn = xn + dn

dn

xn

R

minus1

minus1



119873) with the


1 1198832 119883





119883119899= 119909119899+ 119889119899 (1)




0


Nor

mal

ized

PSD

0 1 115 120 125 150

OOB recording


Primary appearance

OFDM signal

minus20

minus40

minus60

minus80

minus100







Cosinefilter bank

N2 delay


Bits

Geneticalgorithm




vector


Sine

filter

bank

Real (Xn)

Imag (Xn)

Optimum dn

xen

Xn = xn + dn

Sn

xSn




1 1198892 119889



[[[[[[

[

11988911

11988912


11988921

11988922


d

1198891198721

1198891198722

sdot sdot sdot 119889119872119873

]]]]]]

]


Individual 119872

(2)

where119889119872119873






119875 (119891) =1

119873FFT

1003816100381610038161003816100381610038161003816100381610038161003816

119873FFT

sum119899=1

radic119901119899(119909119899+ 119889119899)

sdot int(1+120572)119905

119906

minus(1+120572)119905119906

119892 (119905) 119890minus1198942120587(119891minus119891

119899)119905119889119905

1003816100381610038161003816100381610038161003816100381610038161003816

2

(3)





119901119899 and 119909




119899= 119909119899+ 119889119899) and


1 1198892 119889

119873] In order



optimization of 119889 = [1198891 1198892 119889




CR-OFDM

Primary

Primary band

flowast

(a)

Primary BPrimary A

flowast

(b)



119875119879119868(119891lowast) = 120575119875 (119891lowast) + 119875

119868(119891lowast) le 120581119879

119871(119891lowast) (4)




119875th =[120581119879119871(119891lowast) minus 119875

119868(119891lowast)]

120575 (5)



1+ 1198891 1199092+ 1198892 119909

119873+ 119889119873] becomes


119889lowast = arg119889


1003816100381610038161003816 (6)


(i) if 119875(119891lowast) gt 119875th

119865 =100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205721

(7a)


119865 = minus100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205722

(7b)


119865 =100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205722 (7c)


1and 120572

2are the weighting












(16) 119865 =100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205721


then

119865 = minus100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205722



end if(24) end if












1 1198892 119889




0


minus15

minus20

minus25

minus30

minus35

minus40

minus45

minus50

minus55

minus60

minus65

minus70

minus75

minus80

F

1205721

12057221205722

1205721 = 2 1205722 = 1

Δ = 5dB

Δ

P(flowast) (dB)

Pth = minus60dB

Pth




21205722

1119891lowast 04 to 10





minus80

minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10


Pow

er sp

ectr

al d

ensit

y (d

B)

PUmainband


SU mainband

PUrsquosallowable



minus90

minus80

minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10


PUmainband


Pow

er sp

ectr

al d

ensit

y (d

B)



0 2 4 6 8 10 12 14 16minus005

0

005

01

015

02

025

03

Generations

Fitn

ess (

F)

|R| = 05

|R| = 03




0001002003004

Fitn

ess (

F)

|R| = 05

|R| = 03



0 2 4 6 8 10 12 14EbNo

BER

100

10minus1

10minus2


lowast= 1



|R| = 03 flowast= 1





0085

009

0095

01

BER

1

|R| = 05

|R| = 03



acquired by GA






5 Conclusion



minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10



Pow

er sp

ectr

al d

ensit

y (d

B)



minus50

minus40

minus30

minus20

minus10

0

10



Pow

er sp

ectr

al d

ensit

y (d

B)




0 2 4 6 8 10 12 14EbNo

BER


100

10minus1

10minus2






Acknowledgment


References



























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Cosinefilter bank

N2 delay


Bits

Geneticalgorithm




vector


Sine

filter

bank

Real (Xn)

Imag (Xn)

Optimum dn

xen

Xn = xn + dn

Sn

xSn




1 1198892 119889



[[[[[[

[

11988911

11988912


11988921

11988922


d

1198891198721

1198891198722

sdot sdot sdot 119889119872119873

]]]]]]

]


Individual 119872

(2)

where119889119872119873






119875 (119891) =1

119873FFT

1003816100381610038161003816100381610038161003816100381610038161003816

119873FFT

sum119899=1

radic119901119899(119909119899+ 119889119899)

sdot int(1+120572)119905

119906

minus(1+120572)119905119906

119892 (119905) 119890minus1198942120587(119891minus119891

119899)119905119889119905

1003816100381610038161003816100381610038161003816100381610038161003816

2

(3)





119901119899 and 119909




119899= 119909119899+ 119889119899) and


1 1198892 119889

119873] In order



optimization of 119889 = [1198891 1198892 119889




CR-OFDM

Primary

Primary band

flowast

(a)

Primary BPrimary A

flowast

(b)



119875119879119868(119891lowast) = 120575119875 (119891lowast) + 119875

119868(119891lowast) le 120581119879

119871(119891lowast) (4)




119875th =[120581119879119871(119891lowast) minus 119875

119868(119891lowast)]

120575 (5)



1+ 1198891 1199092+ 1198892 119909

119873+ 119889119873] becomes


119889lowast = arg119889


1003816100381610038161003816 (6)


(i) if 119875(119891lowast) gt 119875th

119865 =100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205721

(7a)


119865 = minus100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205722

(7b)


119865 =100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205722 (7c)


1and 120572

2are the weighting












(16) 119865 =100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205721


then

119865 = minus100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205722



end if(24) end if












1 1198892 119889




0


minus15

minus20

minus25

minus30

minus35

minus40

minus45

minus50

minus55

minus60

minus65

minus70

minus75

minus80

F

1205721

12057221205722

1205721 = 2 1205722 = 1

Δ = 5dB

Δ

P(flowast) (dB)

Pth = minus60dB

Pth




21205722

1119891lowast 04 to 10





minus80

minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10


Pow

er sp

ectr

al d

ensit

y (d

B)

PUmainband


SU mainband

PUrsquosallowable



minus90

minus80

minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10


PUmainband


Pow

er sp

ectr

al d

ensit

y (d

B)



0 2 4 6 8 10 12 14 16minus005

0

005

01

015

02

025

03

Generations

Fitn

ess (

F)

|R| = 05

|R| = 03




0001002003004

Fitn

ess (

F)

|R| = 05

|R| = 03



0 2 4 6 8 10 12 14EbNo

BER

100

10minus1

10minus2


lowast= 1



|R| = 03 flowast= 1





0085

009

0095

01

BER

1

|R| = 05

|R| = 03



acquired by GA






5 Conclusion



minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10



Pow

er sp

ectr

al d

ensit

y (d

B)



minus50

minus40

minus30

minus20

minus10

0

10



Pow

er sp

ectr

al d

ensit

y (d

B)




0 2 4 6 8 10 12 14EbNo

BER


100

10minus1

10minus2






Acknowledgment


References



























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










CR-OFDM

Primary

Primary band

flowast

(a)

Primary BPrimary A

flowast

(b)



119875119879119868(119891lowast) = 120575119875 (119891lowast) + 119875

119868(119891lowast) le 120581119879

119871(119891lowast) (4)




119875th =[120581119879119871(119891lowast) minus 119875

119868(119891lowast)]

120575 (5)



1+ 1198891 1199092+ 1198892 119909

119873+ 119889119873] becomes


119889lowast = arg119889


1003816100381610038161003816 (6)


(i) if 119875(119891lowast) gt 119875th

119865 =100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205721

(7a)


119865 = minus100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205722

(7b)


119865 =100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205722 (7c)


1and 120572

2are the weighting












(16) 119865 =100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205721


then

119865 = minus100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205722



end if(24) end if












1 1198892 119889




0


minus15

minus20

minus25

minus30

minus35

minus40

minus45

minus50

minus55

minus60

minus65

minus70

minus75

minus80

F

1205721

12057221205722

1205721 = 2 1205722 = 1

Δ = 5dB

Δ

P(flowast) (dB)

Pth = minus60dB

Pth




21205722

1119891lowast 04 to 10





minus80

minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10


Pow

er sp

ectr

al d

ensit

y (d

B)

PUmainband


SU mainband

PUrsquosallowable



minus90

minus80

minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10


PUmainband


Pow

er sp

ectr

al d

ensit

y (d

B)



0 2 4 6 8 10 12 14 16minus005

0

005

01

015

02

025

03

Generations

Fitn

ess (

F)

|R| = 05

|R| = 03




0001002003004

Fitn

ess (

F)

|R| = 05

|R| = 03



0 2 4 6 8 10 12 14EbNo

BER

100

10minus1

10minus2


lowast= 1



|R| = 03 flowast= 1





0085

009

0095

01

BER

1

|R| = 05

|R| = 03



acquired by GA






5 Conclusion



minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10



Pow

er sp

ectr

al d

ensit

y (d

B)



minus50

minus40

minus30

minus20

minus10

0

10



Pow

er sp

ectr

al d

ensit

y (d

B)




0 2 4 6 8 10 12 14EbNo

BER


100

10minus1

10minus2






Acknowledgment


References



























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation


















(16) 119865 =100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205721


then

119865 = minus100381610038161003816100381610038161003816100381610038161003816


100381610038161003816100381610038161003816100381610038161003816times 1205722



end if(24) end if












1 1198892 119889




0


minus15

minus20

minus25

minus30

minus35

minus40

minus45

minus50

minus55

minus60

minus65

minus70

minus75

minus80

F

1205721

12057221205722

1205721 = 2 1205722 = 1

Δ = 5dB

Δ

P(flowast) (dB)

Pth = minus60dB

Pth




21205722

1119891lowast 04 to 10





minus80

minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10


Pow

er sp

ectr

al d

ensit

y (d

B)

PUmainband


SU mainband

PUrsquosallowable



minus90

minus80

minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10


PUmainband


Pow

er sp

ectr

al d

ensit

y (d

B)



0 2 4 6 8 10 12 14 16minus005

0

005

01

015

02

025

03

Generations

Fitn

ess (

F)

|R| = 05

|R| = 03




0001002003004

Fitn

ess (

F)

|R| = 05

|R| = 03



0 2 4 6 8 10 12 14EbNo

BER

100

10minus1

10minus2


lowast= 1



|R| = 03 flowast= 1





0085

009

0095

01

BER

1

|R| = 05

|R| = 03



acquired by GA






5 Conclusion



minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10



Pow

er sp

ectr

al d

ensit

y (d

B)



minus50

minus40

minus30

minus20

minus10

0

10



Pow

er sp

ectr

al d

ensit

y (d

B)




0 2 4 6 8 10 12 14EbNo

BER


100

10minus1

10minus2






Acknowledgment


References



























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










0


minus15

minus20

minus25

minus30

minus35

minus40

minus45

minus50

minus55

minus60

minus65

minus70

minus75

minus80

F

1205721

12057221205722

1205721 = 2 1205722 = 1

Δ = 5dB

Δ

P(flowast) (dB)

Pth = minus60dB

Pth




21205722

1119891lowast 04 to 10





minus80

minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10


Pow

er sp

ectr

al d

ensit

y (d

B)

PUmainband


SU mainband

PUrsquosallowable



minus90

minus80

minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10


PUmainband


Pow

er sp

ectr

al d

ensit

y (d

B)



0 2 4 6 8 10 12 14 16minus005

0

005

01

015

02

025

03

Generations

Fitn

ess (

F)

|R| = 05

|R| = 03




0001002003004

Fitn

ess (

F)

|R| = 05

|R| = 03



0 2 4 6 8 10 12 14EbNo

BER

100

10minus1

10minus2


lowast= 1



|R| = 03 flowast= 1





0085

009

0095

01

BER

1

|R| = 05

|R| = 03



acquired by GA






5 Conclusion



minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10



Pow

er sp

ectr

al d

ensit

y (d

B)



minus50

minus40

minus30

minus20

minus10

0

10



Pow

er sp

ectr

al d

ensit

y (d

B)




0 2 4 6 8 10 12 14EbNo

BER


100

10minus1

10minus2






Acknowledgment


References



























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











0001002003004

Fitn

ess (

F)

|R| = 05

|R| = 03



0 2 4 6 8 10 12 14EbNo

BER

100

10minus1

10minus2


lowast= 1



|R| = 03 flowast= 1





0085

009

0095

01

BER

1

|R| = 05

|R| = 03



acquired by GA






5 Conclusion



minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10



Pow

er sp

ectr

al d

ensit

y (d

B)



minus50

minus40

minus30

minus20

minus10

0

10



Pow

er sp

ectr

al d

ensit

y (d

B)




0 2 4 6 8 10 12 14EbNo

BER


100

10minus1

10minus2






Acknowledgment


References



























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










minus70

minus60

minus50

minus40

minus30

minus20

minus10

0

10



Pow

er sp

ectr

al d

ensit

y (d

B)



minus50

minus40

minus30

minus20

minus10

0

10



Pow

er sp

ectr

al d

ensit

y (d

B)




0 2 4 6 8 10 12 14EbNo

BER


100

10minus1

10minus2






Acknowledgment


References



























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









Documents

Research Article Dynamic Interference Control in OFDM ...downloads.hindawi.com/journals/ijdsn/2015/870607.pdf · Research Article Dynamic Interference Control in OFDM-Based Cognitive