K.Gowrishankar, Dr.C.Chandrasekar, R.Kaniezhil / International Journal of Engineering           Research and Applications ...
K.Gowrishankar, Dr.C.Chandrasekar, R.Kaniezhil / International Journal of Engineering           Research and Applications ...
K.Gowrishankar, Dr.C.Chandrasekar, R.Kaniezhil / International Journal of Engineering              Research and Applicatio...
K.Gowrishankar, Dr.C.Chandrasekar, R.Kaniezhil / International Journal of Engineering           Research and Applications ...
K.Gowrishankar, Dr.C.Chandrasekar, R.Kaniezhil / International Journal of Engineering           Research and Applications ...
K.Gowrishankar, Dr.C.Chandrasekar, R.Kaniezhil / International Journal of Engineering           Research and Applications ...
K.Gowrishankar, Dr.C.Chandrasekar, R.Kaniezhil / International Journal of Engineering           Research and Applications ...
K.Gowrishankar, Dr.C.Chandrasekar, R.Kaniezhil / International Journal of Engineering         Research and Applications (I...
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  1. 1. K.Gowrishankar, Dr.C.Chandrasekar, R.Kaniezhil / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1408-1415 Maximum Possibility Of Spectrum Access In Cognitive Radio Using Fuzzy Logic System K.Gowrishankar 1 , Dr.C.Chandrasekar 2 , R.Kaniezhil 3 1 Research Scholar, Department of Computer Science, Periyar University, Salem. 2 Associate Professor, Department of Computer Science, Periyar University, Salem. 3 Ph.D Research Scholar, Department of Computer Science, Periyar University, Salem.ABSTRACT Many spectrum methods have been balance in conflicting goals of satisfying performanceproposed to use spectrum effectively, the requirements for secondary user (SU) whileopportunistic spectrum access has become the minimizing interference to the active primary usersmost viable approach to achieve near-optimal (PU) and other secondary users. Secondary userspectrum utilization by allowing secondary users should not degrade performance statistics of licensedto sense and access available spectrum primary users. In order to achieve these tasks,opportunistically. However, a naive spectrum secondary user is required to recognize primaryaccess for secondary users can make spectrum users, determine environment characteristics andutilization inefficient and increase interference to quickly adapt its system parameters corresponding toadjacent users. In this paper, we propose a novel the operating environment. Main abilities ofapproach using Fuzzy Logic System (FLS) to cognitive radio (CR) with opportunistic radiocontrol the spectrum access. We have to minimize spectrum access capabilities are spectrum sensing,the call blocking and interference. The linguistic dynamic frequency selection and adaptive transmitknowledge of spectrum access are based on three power control.descriptors namely spectrum utilization efficiency This paper presents a novel approach usingof the secondary user, its degree of mobility, and Fuzzy logic system to utilize the available spectrumits distance to the primary user. The spectrum is by the secondary users without interfering thechosen for accessing based on the maximum primary user.possibility for better utilization of the spectrum The paper is organized as follows; Section 2 and 3 defines cognitive radio and fuzzy logic system for its implementation. In section 4, opportunisticKeywords—CR, FLS, Interference, Spectrum spectrum access by Fuzzy logic system to improveusers, Spectrum Utilization. the spectrum efficiency. Section 5 and 6 presents the Knowledge Processing with Opportunistic SpectrumI. INTRODUCTION Access and simulation results. Finally, conclusions In recent studies, the spectrum allocated by are presented in Section 7.the traditional approach shows that the spectrumallocated to the primary user is under-utilized and the II. COGNITIVE RADIOdemand for accessing the limited spectrum is The idea of cognitive radio was first presentedgrowing increasingly. Spectrum is no longer officially in an article by Joseph Mitola III andsufficiently available, because it has been assigned to Gerald Q. Maguire, Jr in 1999. Regulatory bodies inprimary users that own the privileges to their various countries found that most of the radioassigned spectrum. However, it is not used efficiently frequency spectrum was inefficiently utilized. Formost of the time. In order to use the spectrum in an example, cellular network bands are overloaded inopportunistic manner and to the increase spectrum most parts of the world, but amateur radio andavailability, the unlicensed users can be allowed to paging frequencies are not. This can be eradicatedutilize licensed bands of licensed users, without using the dynamic spectrum access.causing any interference with the assigned service. The key enabling technology of dynamicThe reason for allowing the unlicensed users to spectrum access techniques is cognitive radio (CR)utilize licensed bands of licensed users if they would technology, which provides the capability to sharenot cause any interference with the assigned service. the wireless channel with licensed users in anThis paradigm for wireless communication is known opportunistic manner. From this definition, two mainas opportunistic spectrum access and this is characteristics of the cognitive radio can be definedconsidered to be a feature of Cognitive Radio (CR). as follows:  Cognitive capability: It refers to the ability ofCognitive radio is an emerging wireless the radio technology to capture or sense thecommunication paradigm. The main challenge to information from its radio environment.opportunistic radio spectrum access lies in finding 1408 | P a g e
  2. 2. K.Gowrishankar, Dr.C.Chandrasekar, R.Kaniezhil / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1408-1415 Through this capability, the portions of the Since there is a need to “fuzzify” the fuzzy spectrum that are unused at a specific time or results we generate through a fuzzy system analysis location can be identified. Consequently, the i.e., we may eventually find a need to convert the best spectrum and appropriate operating fuzzy results to crisp results. Here, we may want to parameters can be selected. transform a fuzzy partition or pattern into a crisp  Reconfigurability: The cognitive capability partition or pattern; in control we may want to give a provides spectrum awareness whereas single-valued input instead of a fuzzy input reconfigurability enables the radio to be command. The “dufuzzification” has the result of dynamically programmed according to the reducing a fuzzy set to a crisp single-valued quantity, radio environment. or to a crisp set. A CR "monitors its own performance Consider a fuzzy logic system with a rulecontinuously", in addition to "reading the radios base of M rules, and let the lth rule be denoted by Rloutputs"; it then uses this information to "determine . Let each rule have p antecedents and onethe RF environment, channel conditions, link consequent (as is well known, a rule with qperformance, etc.", and adjusts the "radios settings to consequents can be decomposed into rules, eachdeliver the required quality of service subject to an having the same antecedents and one differentappropriate combination of user requirements, consequent), i.e., it is of the general formoperational limitations, and regulatory constraints". Independent studies performed in some Rl : IF u 1 is Fl1 and u 2 is Fl2 and … and u p iscountries confirmed that observation and concluded lthat spectrum utilization depends strongly on time Fl p , THEN v is G .and place. Moreover, fixed spectrum allocation u , K  1,....p and v are the input and outputprevents rarely used frequencies (those assigned to where kspecific services) from being used by unlicensed F1 lusers, even when their transmissions would not linguistic variables, respectively. Each k and Ginterfere at all with the assigned service. are subsets of possibly different universes of More specifically, the cognitive radio l Fk  U k ltechnology will enable the users to (1)determine discourse. Let and G  V . Each rule canwhich portions of the spectrum is available and R be viewed as a fuzzy relation l from U to a set Vdetect the presence of licensed users when a user U  U 1,...,U p Roperates in a licensed band (spectrum sensing), (2) where U is the Cartesian product . lselect the best available channel (spectrum itself is a subset of the Cartesian product U X V=management), (3) coordinate access to this channelwith other users (spectrum sharing), and (4) vacate ( x, y ) : x U , y V  , where x  ( x1 , x 2 ,...,x p )the channel when a licensed user is detected and x k and y are the points in the universes of(spectrum mobility). discourse U k and V of u k and v. While applying a singleton fuzzification, when an  III. FUZZY LOGIC SYSTEM A fuzzy logic system (FLS) is unique in that X  x1 , x 2 , x 3 ,...x p it is able to simultaneously handle numerical data and input is applied, the degreelinguistic knowledge. It is a nonlinear mapping of an of firing corresponding to the lth rule is given byinput data (feature) vector into a scalar output, i.e., it nmaps numbers into numbers.  x i .(x i ) Fuzzy sets theory is an excellent x   i 1mathematical tool to handle the uncertainty arising ndue to vagueness. Figure 1 shows the structure of a  ( x i )fuzzy logic system. i 1 (1) Where  denotes a T-norm, n represents the number x ( x i ) of elements, i ’s are the elements and is its membership function. There are many kinds of dufuzzification methods, but we have chosen the centre of sets method for illustrative purpose. It computes a crisp output for the FLS by first C l computing the centroid, G of every consequent setFig. 1. The Structure of the Fuzzy Logic System G l and, then computing weighted average of these centroids. The weight corresponding to the lth rule 1409 | P a g e
  3. 3. K.Gowrishankar, Dr.C.Chandrasekar, R.Kaniezhil / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1408-1415consequent centroid is the degree of firing associated xp yp p where and are the distances of the primary Ti 1  ( x ) F1l 1 users and x i and y i are the distances of thewith the lth rule, , so that  M p secondary users from the primary user. C l T  l ( x ) l 1 G i 1 F1 1 y cos ( x )   M p T  l ( x ) l 1 i 1 F1 1 (2)where M is the number of rules in the FLS.IV. OPPORTUNISTIC SPECTRUM ACCESS We design the fuzzy logic for opportunisticspectrum access using cognitive radio. In this paper,we are selecting the best suitable secondary users toaccess the available users without any interferencewith the primary users. This is collected based on the Fig. 2a. Membership Function(MF) used tofollowing three antecedents i.e., descriptors. They are represent the antecedent1 Antecedent 1: Spectrum Utilization Efficiency Antecedent 2: Degree of Mobility We apply different available spectrum Antecedent 3: Distance of Secondary user to the inorder to find out the spectrum efficiency which is PU. the main purpose of the opportunistic spectrum Fuzzy logic is used because it is a multi- access strategy. Hence, we calculate the spectrumvalued logic and many input parameters can be efficiency as the ratio of average busy spectrum overconsidered to take the decision. Generally, the total available spectrum owned by secondary users.secondary user with the furthest distance to theprimary user or the secondary user with maximumspectrum utilization efficiency can be chosen toaccess spectrum under the constraint that nointerference is created for the primary user. In ourapproach, we combine the three antecedents toallocate spectrum opportunistically inorder to findout the optimal solutions using the fuzzy logicsystem. Mobility of the secondary user plays a vitalrole in the proposed work. Wireless systems alsodiffer in the amount of mobility that they have toallow for the users. Spectrum Mobility is defined asthe process when a cognitive radio user exchanges its Fig. 2b. Membership Function(MF) used to representfrequency of operation. The movement of the the other antecedentssecondary user leads to a shift of the receivedfrequency, called the Doppler shift. Linguistic variables are used to represent the Detecting signal from the primary user can spectrum utilization efficiency, distance and degreebe reduced using the Mobility. The secondary user of mobility are divided into three levels: low,should detect the primary signal which determines moderate, and high. while we use 3 levels, i.e., near,the spectrum that is unused. If the secondary user moderate, and far to represent the distance.fails to detect the primary signal, then it will notdetermine exactly the spectrum that is unused.Thereby it leads to interference to the adjacent users.This is referred as the hidden node problem. Besides, we consider the distance betweenthe primary user and the secondary users. Distancebetween the primary user and the secondary userswere calculated using the formula di Di  20 d  max i 1 i (3)d i  ( xi  x p ) 2  ( y i  y p ) 2 (4) 1410 | P a g e
  4. 4. K.Gowrishankar, Dr.C.Chandrasekar, R.Kaniezhil / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1408-1415 Fig. 2c. Membership Function(MF) used to l c avg l cavgrepresent the consequence , where l = 1,2,..27 and is defined asThe consequence, i.e., the possibility that the follows:secondary user is chosen to access the spectrum is 5divided into five levels which are very low, low,  wil c imedium, high and very high. We use trapezoidal c avg  i 1 l 5membership functions (MFs) to represent near, low,  wilfar, high, very low and very high, and triangle MFs to i 1 (5)represent moderate, low, medium and high. MFs are lshown in Fig. 2a, 2b, 2c. Since we have 3 antecedents which wi is the number of choosing linguistic label i 3 iand fuzzy subsets, we need setup 3  27 rules for for the consequence of rule l and c is the centroid of ththis FLS. the i consequence set (i: 1; 2; ...; 5; l: 1; 2; ...; 27). i Table 2 provides c for each rule. For every inputV. KNOWLEDGE PROCESSING USINGOPPORTUNISTIC SPECTRUM ACCESS ( x1 , x 2 , x3 ) , the output Table 1 summaries the various values based y( x1 , x 2 , x3 ) of the designed FLS is computed ason the three antecedents and its consequence. 27  F ( x 1 l 1) Fl( x 2 2)  F l ( x c avg 3 3) l y( x1 , x 2 , x 3 )  i 1 27  F ( x 1 l 1) Fl( x 2 2) Fl( x 3 3) i 1 (6)Since we chose a single consequent for each rule toform a rule base, we averaged the centroids of all theresponses for each rule and used this average in placeof the rule consequent centroid. Doing this leads torules that have the following form: 1 which gives the possibility that a secondary user isR : If Degree of mobility ( x1 ) is Fl ; and its selected to access the available spectrum.distance between primary user and the secondary The weighted average value for each rule is given in the Table 2. Table 3 gives the three Descriptors and x F2 Possibility for four Secondary users. As listed inusers ( 2 ) is l ; and the spectrum utilization Table 3, at a particular time, values of three F3 descriptors and possibility for four secondary userefficiency of the secondary user ( x3 ) is l , thenthe Possibility (y) choosing the available spectrum is i.e., the secondary user chosen to the access the available spectrum is (SU4), the secondary user with 1411 | P a g e
  5. 5. K.Gowrishankar, Dr.C.Chandrasekar, R.Kaniezhil / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1408-1415the highest spectrum utilization is (SU2), thesecondary user having the furthest distance to theprimary user is (SU3), and the secondary user withthe lowest mobility is (SU4).Table 3.Three Descriptors and Possibility for FourSecondary usersParamete SU1 SU2 SU3 SU4rsDistance 8.01 2.16 12.80 6.02fromPrimaryuser tosecondaryusersDegree of 6.666 9.2528 5.2229 1.2420Mobility 7Spectrum 61.00 83.027 70.892 90.419Utilization 14 4 2 1EfficiencyPossibility 28.59 19.70 40.89 58.62 Fig. 3: Mean Arrival VS Call blocking rateFrom table 3, we see that SU1 has 61%, SU4 has90% of Spectrum utilization efficiency, SU3 only Fig. 3 shows the call blocking of the serviceachieves 70.89% and SU4 achieves 90% of spectrum provider using the Fuzzy logic system. As the callUtilization. Similarly, if we consider the Distance of arrival rate increases the blocking rate gets decreased.primary user to the secondary users, SU1 has 8.01, Traffic rate increases along with the call blockingSU2 has 2.16, SU3 has the furthest distance from the rate.primary user 12.80and SU4 has 6.02. Even though, ifSU3 has furthest distance from the primary user but ithas lowest spectrum utilization (70.89%) and lowestdegree of mobility. Thus, the secondary user willselect the spectrum for accessing based on the highestpossibility rather than the highest spectrum utilizationand the furthest distance from the primary user.VI. SIMULATION RESULTS In this section, we present simulation resultson the performance of our proposed work based onFuzzy logic System. In the proposed work, we arechoosing the available channel with the highpossibility and high spectrum utilization efficiency. To validate our approach, we randomlygenerated 20 secondary users over an area of 100 X100 meters. The primary user was placed randomly inthis area. Three descriptors were randomly generatedfor each secondary user. More specifically, thespectrum utilization efficiency of each secondaryuser was a random value in the interval [0,100] andits mobility degree in [0,10]. Distances to the primary Fig. 4: Mean Arrival VS Interference using FLSusers were normalized to [0,10]. When interference increases spectrum utilization will decrease. The Fig. 4 shows that there is a decrease in the interference which provides an increase spectrum utilization using FLS. This leads to an efficient spectrum utilization. 1412 | P a g e
  6. 6. K.Gowrishankar, Dr.C.Chandrasekar, R.Kaniezhil / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1408-1415 Fig. 7: The opportunistic spectrum access decision Fig. 5: Distance of Secondary Users to the Primary surface for the cognitive user with a fixed distance tousers using FLS the primary user: when the distance to the PU x3 = 7. As illustrated in the Fig. 5, the distance from Since it is impossible to plot visually, we fixprimary user to the secondary users is shown. Thedistance between primary user and the secondary one of three variables of y( x1 , x 2 , x3 ).Moreusers helps us for calculating the possibility for specifically, we fixed the distance to the primary useraccessing the spectrum opportunistically. x3. Two cases, i.e., x3= 1 and x3=7, were considered. Figure 6 & 7 represents the opportunistic spectrum access decision surface for the cognitive user for these two cases. From Fig. 6&7, we see clearly that, at the same spectrum utilization efficiency and mobility degree, secondary users further from the primary user have higher chance to access the spectrum.Fig. 6: The opportunistic spectrum access decisionsurface for the cognitive user with a fixed distance tothe primary user: when the distance to the PU x3 = 1. Fig. 8: An opportunistic spectrum access scenario in a specific space and a particular time. The Fig. 8 illustrates that opportunistic spectrum access in a specific time and a particular time. Here, SU1, SU2, SU3 and SU4 are denoted by 1413 | P a g e
  7. 7. K.Gowrishankar, Dr.C.Chandrasekar, R.Kaniezhil / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1408-1415using the symbols , , , . Here the Primary providers. The Fig. 10 shows that there is an increaseuser is denoted by the symbol P. in channel utilization with decrease in the call arrival rate. Our designed FLS is used to control the spectrum assignment and access procedures in order to prevent multiple users from colliding in overlapping spectrum portions. Therefore, the secondary user with the highest possibility is guaranteed to access the spectrum. VII. CONCLUSION This papers shows an efficient way to utilize the unused spectrum of the licensed users and it allows also other next generation mobile network users to benefit from available radio spectrum, leading to the improvement of overall spectrum utilization and maximizing overall PU and SU networks capacity. We proposed an approach using a Fuzzy Logic System to detect the maximum possibility of spectrum access for secondary users via cognitive radio. The secondary users are selected on the basis of spectrum utilization, degree of mobility and distance from secondary users to the primary user. By Fig. 9: Secondary Users with their corresponding using the above antecedents, we have calculated theparameters highest possibility of accessing the spectrum band for secondary users and we have minimized the call In Fig. 9 shows that the Spectrum user blocking and interference. So that we can have better(SU4) having the highest possibility of accessing the and efficient spectrum utilization.spectrum than the other users. Even though the othersecondary users have the furthest distance from REFERENCESprimary user to the secondary users, highest spectrum [1] H.J. Zimmermann, “Fuzzy Set Theory andutilization and highest mobility but we prefer SU4 to its Applications”, ISBN 81-8128-519-0,access the spectrum because it has the highest Fourth Edition, Springer Internationalpossibility i.e., 58.62. Edition,2006. [2] Timothy J. Ross, “Fuzzy Logic with Engineering Applications”, ISBN 9812-53- 180-7, Second Edition, Wiley Student Edition, 2005. [3] James J. Buckley, Esfandiar Eslami, “An Introduction to Fuzzy Logic and Fuzzy Sets”, ISBN 3-7908-1447-4, Physica Verlag, 2002. [4] Andreas F. Molisch, “Wireless Communications”, ISBN: 978-0-470-74187- 0 , Second Edition, John Wiley & Sons Ltd , 2011. [5] R. Kaniezhil, Dr. C. Chandrasekar, S. Nithya Rekha, “Dynamic Spectrum Sharing for Heterogeneous Wireless Network via Cognitive Radio”, Proceedings of the International Conference on Pattern Recognition, Informatics and Medical Engineering , March 21-23, 2012, pp. 156- Fig. 10: Mean Arrival VS Channel Utilization 162.using FLS [6] R. Kaniezhil, Dr. C. Chandrasekar, “Multiple Service providers sharing The Spectrum Efficiency (Channel Spectrum using Cognitive Radio in WirelessUtilization) is defined as the ratio of average busy Communication Networks”, Internationalchannels over total channels owned by service 1414 | P a g e
  8. 8. K.Gowrishankar, Dr.C.Chandrasekar, R.Kaniezhil / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1408-1415 Journal of Scientific & Engineering Research, Volume 3, Issue 6, June 2012.[7] R. Kaniezhil, Dr. C. Chandrasekar, “Multiple service providers sharing Spectrum dynamically via Cognitive Radio in a Heterogeneous Wireless Networks”, International Journal of Computational Intelligence and Informatics, Vol. 1 : No. 2, July - September 2011, pp. 101-108.[8] R. Kaniezhil, Dr. C Chandrasekar, S. Nithya Rekha, “Channel selection for Selective Sensing using Cognitive Radio Nodes”, International Journal of Computer Applications(IJCA), 0975-8887, Volume 39– No.3, pp. 20-25, February 2012.[9] R. Kaniezhil, Dr. C. Chandrasekar, “Spectrum Sharing in a Long Term Spectrum Strategy via Cognitive Radio for Heterogeneous Wireless Networks”, International Journal on Computer Science and Engineering (IJCSE), Vol. 4, No. 06, June 2012, pp 982-995.[10] Spectrum policy task force report, Technical report 02-135, Federal communications commission, Nov. 2002.[11] Jerry M. Mendel, “Fuzzy Logic Systems for engineering: A Tutorial”, IEEE Proc., vol. 83, no. 2, pp. 345-377, March 1995.[12] Marja Matinmikko, Tapio Rauma, Miia Mustonen, Ilkka Harjula, Heli Sarvanko, Aarne Mammela, “Application of Fuzzy Logic to Cognitive Radio Systems”, IEICE Trans. Communication, vol. E92-B, no.12, December 2009, pp. 3572-3580.[13] George C. Mouzouris and Jerry M. Mendel, “Nonsingleton Fuzzy Logic Systems: Theory and application”, IEEE Transactions on Fuzzy Systems, vol. 5, no. 1, February 1997, pp. 56-71.[14] Partha Pratim Bhattacharya, “A Novel Opportunistic Spectrum Access for Applications in Cognitive Radio”.[15] Hong-Sam T. Le, Hung D. Ly, Qilian Liang, “ Opportunistic Spectrum Access Using Fuzzy Logic for Cognitive Radio Networks”, Int J Wireless Inf Networks (2011), pp 171– 178.[16] K. Gowrishankar, Dr. C. Chandrasekar, R. Kaniezhil, “Efficient spectrum utilization for Cognitive Radio using Fuzzy Logic System”, International Conference on Computational Intelligence and Communication, July 4-6, 2012. [Accepted][17] R. Kaniezhil, Dr. C. Chandrasekar, “ Comparing Spectrum Utilization using Fuzzy Logic System for Heterogeneous Wireless Networks via Cognitive Radio”, International Journal of Scientific & Engineering Research, Volume 3, Issue 7, July-2012. [Accepted] 1415 | P a g e

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