Maximum Possibility Of Spectrum Access In Cognitive Radio Using Fuzzy Logic System

Transcription

1 Maimum Possibiity Of Spectrum Access In Cognitive Radio Using Fuzzy Logic System K.Gowrishankar, Dr.C.Chandrasekar 2, R.Kaniezhi 3 Research Schoar, Department of Computer Science, Periyar University, Saem. 2 Associate Professor, Department of Computer Science, Periyar University, Saem. 3 Ph.D Research Schoar, Department of Computer Science, Periyar University, Saem. ABSTRACT Many spectrum methods have been proposed to use spectrum effectivey, the opportunistic spectrum access has become the most viabe approach to achieve near-optima spectrum utiization by aowing secondary users to sense and access avaiabe spectrum opportunisticay. However, a naive spectrum access for secondary users can make spectrum utiization inefficient and increase interference to adjacent users. In this paper, we propose a nove approach using Fuzzy Logic System (FLS) to contro the spectrum access. We have to minimize the ca bocking and interference. The inguistic knowedge of spectrum access are based on three descriptors namey spectrum utiization efficiency of the secondary user, its degree of mobiity, and its distance to the primary user. The spectrum is chosen for accessing based on the maimum possibiity for better utiization of the spectrum Keywords CR, FLS, Interference, Spectrum users, Spectrum Utiization. I. INTRODUCTION In recent studies, the spectrum aocated by the traditiona approach shows that the spectrum aocated to the primary user is under-utiized and the demand for accessing the imited spectrum is growing increasingy. Spectrum is no onger sufficienty avaiabe, because it has been assigned to primary users that own the privieges to their assigned spectrum. However, it is not used efficienty most of the time. In order to use the spectrum in an opportunistic manner and to the increase spectrum avaiabiity, the unicensed users can be aowed to utiize icensed bands of icensed users, without causing any interference with the assigned service. The reason for aowing the unicensed users to utiize icensed bands of icensed users if they woud not cause any interference with the assigned service. This paradigm for wireess communication is known as opportunistic spectrum access and this is considered to be a feature of Cognitive Radio (CR). Cognitive radio is an emerging wireess communication paradigm. The main chaenge to opportunistic radio spectrum access ies in finding baance in conficting goas of satisfying performance requirements for secondary user (SU) whie minimizing interference to the active primary users (PU) and other secondary users. Secondary user shoud not degrade performance statistics of icensed primary users. In order to achieve these tasks, secondary user is required to recognize primary users, determine environment characteristics and quicky adapt its system parameters corresponding to the operating environment. Main abiities of cognitive radio (CR) with opportunistic radio spectrum access capabiities are spectrum sensing, dynamic frequency seection and adaptive transmit power contro. This paper presents a nove approach using Fuzzy ogic system to utiize the avaiabe spectrum by the secondary users without interfering the primary user. The paper is organized as foows; Section 2 and 3 defines cognitive radio and fuzzy ogic system for its impementation. In section 4, opportunistic spectrum access by Fuzzy ogic system to improve the spectrum efficiency. Section 5 and 6 presents the Knowedge Processing with Opportunistic Spectrum Access and simuation resuts. Finay, concusions are presented in Section 7. II. COGNITIVE RADIO The idea of cognitive radio was first presented officiay in an artice by Joseph Mitoa III and Gerad Q. Maguire, Jr in 999. Reguatory bodies in various countries found that most of the radio frequency spectrum was inefficienty utiized. For eampe, ceuar network bands are overoaded in most parts of the word, but amateur radio and paging frequencies are not. This can be eradicated using the dynamic spectrum access. The key enabing technoogy of dynamic spectrum access techniques is cognitive radio (CR) technoogy, which provides the capabiity to share the wireess channe with icensed users in an opportunistic manner. From this definition, two main characteristics of the cognitive radio can be defined as foows: Cognitive capabiity: It refers to the abiity of the radio technoogy to capture or sense the information from its radio environment. Through this capabiity, the portions of the

2 spectrum that are unused at a specific time or ocation can be identified. Consequenty, the best spectrum and appropriate operating parameters can be seected. Reconfigurabiity: The cognitive capabiity provides spectrum awareness whereas reconfigurabiity enabes the radio to be dynamicay programmed according to the radio environment. A CR "monitors its own performance continuousy", in addition to "reading the radio's outputs"; it then uses this information to "determine the RF environment, channe conditions, ink performance, etc.", and adjusts the "radio's settings to deiver the required quaity of service subject to an appropriate combination of user requirements, operationa imitations, and reguatory constraints". Independent studies performed in some countries confirmed that observation and concuded that spectrum utiization depends strongy on time and pace. Moreover, fied spectrum aocation prevents rarey used frequencies (those assigned to specific services) from being used by unicensed users, even when their transmissions woud not interfere at a with the assigned service. More specificay, the cognitive radio technoogy wi enabe the users to ()determine which portions of the spectrum is avaiabe and detect the presence of icensed users when a user operates in a icensed band (spectrum sensing), (2) seect the best avaiabe channe (spectrum management), (3) coordinate access to this channe with other users (spectrum sharing), and (4) vacate the channe when a icensed user is detected (spectrum mobiity). III. FUZZY LOGIC SYSTEM A fuzzy ogic system (FLS) is unique in that it is abe to simutaneousy hande numerica data and inguistic knowedge. It is a noninear mapping of an input data (feature) vector into a scaar output, i.e., it maps numbers into numbers. Fuzzy sets theory is an eceent mathematica too to hande the uncertainty arising due to vagueness. Figure shows the structure of a fuzzy ogic system. Fig.. The Structure of the Fuzzy Logic System Since there is a need to fuzzify the fuzzy resuts we generate through a fuzzy system anaysis i.e., we may eventuay find a need to convert the fuzzy resuts to crisp resuts. Here, we may want to transform a fuzzy partition or pattern into a crisp partition or pattern; in contro we may want to give a singe-vaued input instead of a fuzzy input command. The dufuzzification has the resut of reducing a fuzzy set to a crisp singe-vaued quantity, or to a crisp set. Consider a fuzzy ogic system with a rue base of M rues, and et the th rue be denoted by R. Let each rue have p antecedents and one consequent (as is we known, a rue with q consequents can be decomposed into rues, each having the same antecedents and one different consequent), i.e., it is of the genera form p F R : IF u is, THEN v is F and u2 is G. 2 F and and u p is u,k,...p where k and v are the input and output F inguistic variabes, respectivey. Each k and G are subsets of possiby different universes of F U discourse. Let k k and G V. Each rue can R be viewed as a fuzzy reation from U to a set V U U,...,U where U is the Cartesian product p R. itsef is a subset of the Cartesian product U X V= (, y ) : U, y V (,,..., ), where 2 p and k and y are the points in the universes of U discourse k u and V of k and v. Whie appying a singeton fuzzification, when an ' ' ' ' ' X input, 2, 3,... p is appied, the degree of firing corresponding to the th rue is given by n i. (i ) n (i ) () Where denotes a T-norm, n represents the number of eements, i ( ) s are the eements and i is its membership function. There are many kinds of dufuzzification methods, but we have chosen the centre of sets method for iustrative purpose. It computes a crisp output for the FLS by first C computing the centroid, G of every consequent set G and, then computing weighted average of these centroids. The weight corresponding to the th rue consequent centroid is the degree of firing associated p ' T ( ) with the th F rue,, so that

3 y ' cos( ) M p ' C T ( ) G F M p ' T ( F ) where M is the number of rues in the FLS. (2) secondary users from the primary user. IV. OPPORTUNISTIC SPECTRUM ACCESS We design the fuzzy ogic for opportunistic spectrum access using cognitive radio. In this paper, we are seecting the best suitabe secondary users to access the avaiabe users without any interference with the primary users. This is coected based on the foowing three antecedents i.e., descriptors. They are Antecedent : Spectrum Utiization Efficiency Antecedent 2: Degree of Mobiity Antecedent 3: Distance of Secondary user to the PU. Fuzzy ogic is used because it is a mutivaued ogic and many input parameters can be considered to take the decision. Generay, the secondary user with the furthest distance to the primary user or the secondary user with maimum spectrum utiization efficiency can be chosen to access spectrum under the constraint that no interference is created for the primary user. In our approach, we combine the three antecedents to aocate spectrum opportunisticay inorder to find out the optima soutions using the fuzzy ogic system. Mobiity of the secondary user pays a vita roe in the proposed work. Wireess systems aso differ in the amount of mobiity that they have to aow for the users. Spectrum Mobiity is defined as the process when a cognitive radio user echanges its frequency of operation. The movement of the secondary user eads to a shift of the received frequency, caed the Dopper shift. Detecting signa from the primary user can be reduced using the Mobiity. The secondary user shoud detect the primary signa which determines the spectrum that is unused. If the secondary user fais to detect the primary signa, then it wi not determine eacty the spectrum that is unused. Thereby it eads to interference to the adjacent users. This is referred as the hidden node probem. Besides, we consider the distance between the primary user and the secondary users. Distance between the primary user and the secondary users were cacuated using the formua di Di 20 ma di (3) 2 2 di ( i p ) ( yi y p ) where users and p y and p i (4) are the distances of the primary y and i are the distances of the Fig. 2a. Membership Function(MF) used to represent the antecedent We appy different avaiabe spectrum inorder to find out the spectrum efficiency which is the main purpose of the opportunistic spectrum access strategy. Hence, we cacuate the spectrum efficiency as the ratio of average busy spectrum over tota avaiabe spectrum owned by secondary users. Fig. 2b. Membership Function(MF) used to represent the other antecedents Linguistic variabes are used to represent the spectrum utiization efficiency, distance and degree of mobiity are divided into three eves: ow, moderate, and high. whie we use 3 eves, i.e., near, moderate, and far to represent the distance. Fig. 2c. Membership Function(MF) used to represent the consequence The consequence, i.e., the possibiity that the secondary user is chosen to access the spectrum is

4 divided into five eves which are very ow, ow, medium, high and very high. We use trapezoida membership functions (MFs) to represent near, ow, far, high, very ow and very high, and triange MFs to represent moderate, ow, medium and high. MFs are shown in Fig. 2a, 2b, 2c. Since we have 3 antecedents and fuzzy subsets, we need setup rues for this FLS. V. KNOWLEDGE PROCESSING USING OPPORTUNISTIC SPECTRUM ACCESS Tabe summaries the various vaues based on the three antecedents and its consequence. cavg 5 i wi c 5 w i (5) wi is the number of choosing inguistic abe i which i for the consequence of rue and c is the centroid of the i th consequence set (i: ; 2;...; 5; : ; 2;...; 27). i Tabe 2 provides c for each rue. For every input (, 2, 3 ), the output y(, 2, 3 ) of the designed FLS is computed as y(, 2, 3 ) 27 F ( ) 27 i F ( ) F2 ( 2 ) F2 ( 2 ) c F ( avg 3 3 ) F3 ( 3 ) (6) Since we chose a singe consequent for each rue to form a rue base, we averaged the centroids of a the responses for each rue and used this average in pace of the rue consequent centroid. Doing this eads to rues that have the foowing form: R ' : If Degree of mobiity ( F ) is ; and its distance between primary user and the secondary users ( 2 F 2 ) is ; and the spectrum utiization efficiency of the secondary user ( 3 F 3 ) is, then the Possibiity (y) choosing the avaiabe spectrum is c avg, where =,2,..27 and foows: c avg is defined as which gives the possibiity that a secondary user is seected to access the avaiabe spectrum. The weighted average vaue for each rue is given in the Tabe 2. Tabe 3 gives the three Descriptors and Possibiity for four Secondary users. As isted in Tabe 3, at a particuar time, vaues of three descriptors and possibiity for four secondary user i.e., the secondary user chosen to the access the avaiabe spectrum is (SU4), the secondary user with the highest spectrum utiization is (SU2), the secondary user having the furthest distance to the

5 primary user is (SU3), and the secondary user with the owest mobiity is (SU4). Tabe 3. Three Descriptors and Possibiity for Four Secondary users Paramete SU SU2 SU3 SU4 rs Distance from Primary user to secondary users Degree of Mobiity 7 Spectrum Utiization Efficiency Possibiity From tabe 3, we see that SU has 6%, SU4 has 90% of Spectrum utiization efficiency, SU3 ony achieves 70.89% and SU4 achieves 90% of spectrum Utiization. Simiary, if we consider the Distance of primary user to the secondary users, SU has 8.0, SU2 has 2.6, SU3 has the furthest distance from the primary user 2.80and SU4 has Even though, if SU3 has furthest distance from the primary user but it has owest spectrum utiization (70.89%) and owest degree of mobiity. Thus, the secondary user wi seect the spectrum for accessing based on the highest possibiity rather than the highest spectrum utiization and the furthest distance from the primary user. VI. SIMULATION RESULTS In this section, we present simuation resuts on the performance of our proposed work based on Fuzzy ogic System. In the proposed work, we are choosing the avaiabe channe with the high possibiity and high spectrum utiization efficiency. To vaidate our approach, we randomy generated 20 secondary users over an area of 00 X 00 meters. The primary user was paced randomy in this area. Three descriptors were randomy generated for each secondary user. More specificay, the spectrum utiization efficiency of each secondary user was a random vaue in the interva [0,00] and its mobiity degree in [0,0]. Distances to the primary users were normaized to [0,0]. Fig. 3: Mean Arriva VS Ca bocking rate Fig. 3 shows the ca bocking of the service provider using the Fuzzy ogic system. As the ca arriva rate increases the bocking rate gets decreased. Traffic rate increases aong with the ca bocking rate. Fig. 4: Mean Arriva VS Interference using FLS When interference increases spectrum utiization wi decrease. The Fig. 4 shows that there is a decrease in the interference which provides an increase spectrum utiization using FLS. This eads to an efficient spectrum utiization.

6 Fig. 5: Distance of Secondary Users to the Primary users using FLS As iustrated in the Fig. 5, the distance from primary user to the secondary users is shown. The distance between primary user and the secondary users heps us for cacuating the possibiity for accessing the spectrum opportunisticay. Fig. 7: The opportunistic spectrum access decision surface for the cognitive user with a fied distance to the primary user: when the distance to the PU 3 = 7. Since it is impossibe to pot visuay, we fi one of three variabes of y(, 2, 3 ).More specificay, we fied the distance to the primary user 3. Two cases, i.e., 3 = and 3 =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 ceary that, at the same spectrum utiization efficiency and mobiity degree, secondary users further from the primary user have higher chance to access the spectrum. Fig. 6: The opportunistic spectrum access decision surface for the cognitive user with a fied distance to the primary user: when the distance to the PU 3 =. Fig. 8: An opportunistic spectrum access scenario in a specific space and a particuar time. The Fig. 8 iustrates that opportunistic spectrum access in a specific time and a particuar time. Here, SU, SU2, SU3 and SU4 are denoted by

7 using the symbos,,,. Here the Primary user is denoted by the symbo P. Fig. 9: Secondary Users with their corresponding parameters In Fig. 9 shows that the Spectrum user (SU4) having the highest possibiity of accessing the spectrum than the other users. Even though the other secondary users have the furthest distance from primary user to the secondary users, highest spectrum utiization and highest mobiity but we prefer SU4 to access the spectrum because it has the highest possibiity i.e., Fig. 0: Mean Arriva VS Channe Utiization using FLS The Spectrum Efficiency (Channe Utiization) is defined as the ratio of average busy channes over tota channes owned by service providers. The Fig. 0 shows that there is an increase in channe utiization with decrease in the ca arriva rate. Our designed FLS is used to contro the spectrum assignment and access procedures in order to prevent mutipe users from coiding in overapping spectrum portions. Therefore, the secondary user with the highest possibiity is guaranteed to access the spectrum. VII. CONCLUSION This papers shows an efficient way to utiize the unused spectrum of the icensed users and it aows aso other net generation mobie network users to benefit from avaiabe radio spectrum, eading to the improvement of overa spectrum utiization and maimizing overa PU and SU networks capacity. We proposed an approach using a Fuzzy Logic System to detect the maimum possibiity of spectrum access for secondary users via cognitive radio. The secondary users are seected on the basis of spectrum utiization, degree of mobiity and distance from secondary users to the primary user. By using the above antecedents, we have cacuated the highest possibiity of accessing the spectrum band for secondary users and we have minimized the ca bocking and interference. So that we can have better and efficient spectrum utiization. REFERENCES [] H.J. Zimmermann, Fuzzy Set Theory and its Appications, ISBN , Fourth Edition, Springer Internationa Edition,2006. [2] Timothy J. Ross, Fuzzy Logic with Engineering Appications, ISBN , Second Edition, Wiey Student Edition, [3] James J. Buckey, Esfandiar Esami, An Introduction to Fuzzy Logic and Fuzzy Sets, ISBN , Physica Verag, [4] Andreas F. Moisch, Wireess Communications, ISBN: , Second Edition, John Wiey & Sons Ltd, 20. [5] R. Kaniezhi, Dr. C. Chandrasekar, S. Nithya Rekha, Dynamic Spectrum Sharing for Heterogeneous Wireess Network via Cognitive Radio, Proceedings of the Internationa Conference on Pattern Recognition, Informatics and Medica Engineering, March 2-23, 202, pp [6] R. Kaniezhi, Dr. C. Chandrasekar, Mutipe Service providers sharing Spectrum using Cognitive Radio in Wireess Communication Networks, Internationa

8 Journa of Scientific & Engineering Research, Voume 3, Issue 6, June 202. [7] R. Kaniezhi, Dr. C. Chandrasekar, Mutipe service providers sharing Spectrum dynamicay via Cognitive Radio in a Heterogeneous Wireess Networks, Internationa Journa of Computationa Inteigence and Informatics, Vo. : No. 2, Juy - September 20, pp [8] R. Kaniezhi, Dr. C Chandrasekar, S. Nithya Rekha, Channe seection for Seective Sensing using Cognitive Radio Nodes, Internationa Journa of Computer Appications(IJCA), , Voume 39 No.3, pp , February 202. [9] R. Kaniezhi, Dr. C. Chandrasekar, Spectrum Sharing in a Long Term Spectrum Strategy via Cognitive Radio for Heterogeneous Wireess Networks, Internationa Journa on Computer Science and Engineering (IJCSE), Vo. 4, No. 06, June 202, pp [0] Spectrum poicy task force report, Technica report 02-35, Federa communications commission, Nov [] Jerry M. Mende, Fuzzy Logic Systems for engineering: A Tutoria, IEEE Proc., vo. 83, no. 2, pp , March 995. [2] Marja Matinmikko, Tapio Rauma, Miia Mustonen, Ikka Harjua, Hei Sarvanko, Aarne Mammea, Appication of Fuzzy Logic to Cognitive Radio Systems, IEICE Trans. Communication, vo. E92-B, no.2, December 2009, pp [3] George C. Mouzouris and Jerry M. Mende, Nonsingeton Fuzzy Logic Systems: Theory and appication, IEEE Transactions on Fuzzy Systems, vo. 5, no., February 997, pp [4] Partha Pratim Bhattacharya, A Nove Opportunistic Spectrum Access for Appications in Cognitive Radio. [5] Hong-Sam T. Le, Hung D. Ly, Qiian Liang, Opportunistic Spectrum Access Using Fuzzy Logic for Cognitive Radio Networks, Int J Wireess Inf Networks (20), pp [6] K. Gowrishankar, Dr. C. Chandrasekar, R. Kaniezhi, Efficient spectrum utiization for Cognitive Radio using Fuzzy Logic System, Internationa Conference on Computationa Inteigence and Communication, Juy 4-6, 202. [Accepted] [7] R. Kaniezhi, Dr. C. Chandrasekar, Comparing Spectrum Utiization using Fuzzy Logic System for Heterogeneous Wireess Networks via Cognitive Radio, Internationa Journa of Scientific & Engineering Research, Voume 3, Issue 7, Juy-202. [Accepted]