B.ParameshwariAssistant ProfessorDepartment ofElectronics andCommunication Engineering,CJITS, Yeshwanthapur,T.SE-mail:[email protected] paper presents, a spectrum sharing strategy incooperative cognitive radio network. Multi-phase cooperationarchitecture is explained and studied with cooperation partnerselection and spectrum sharing among secondary users .Thedata of primary users forwarded to the cooperation partnerswho are selected from secondary users, and then acquire thespectrum access opportunities for their own transmissions as areward. The partner selection is modeled as an optimallyweighted bipartite matching problem to maximize the totalutility where energy efficiency is also considered just toincrease the utility for the primary user-secondary usercooperation pairs. By the partner secondary user furtherimprovisation in the spectrum utilization is done by sharingthe acquired spectrum with the surrounding secondary usersvia cooperative network coding. At the end the simulationresults provided, which shows that to the dynamic traffic loadsin cooperative cognitive radio network, the proposed partnerselection and spectrum sharing approach adapts well.KeywordsCooperative cognitive radio network, quality-of service ,Intermediate users, Minimum mean-square- error.1. INTRODUCTIONThe scarcity of spectral resources has become a severeproblem due to the significant growth in commercial wirelessservices, in recent years, with the emergence of cooperativecommunications in wireless networks 3, a newcommunication paradigm in cooperative cognitive radionetworks is proposed 4–6, termed cooperative cognitiveradio networks. The traditional fixed spectrum allocation isproved inefficient, since the frequency band is largely under-utilized 1. Cognitive Radio (CR) 2 has been considered asa promising technology for improve spectrum utilization byallowing secondary users to access spectrum holes unoccupiedby primary users (Pus).The rapid growth in wireless communications has contributeda huge demand on the deployment of new wireless services inboth the licensed and unlicensed frequency spectrum.However, recent Studies show the fixed spectrum assignmentpolicy enforced today results in poor spectrum utilization. Toaddress this problem, cognitive radio 1,2 has emerged as apromising technology to enable the access of the intermittentperiods of unoccupied frequency bands, known as white spaceor spectrum holes, and thereby increase the spectralefficiency.The fundamental task of each Cognitive radio user incognitive radio networks, in the most primitive sense, fordetection of licensed users, also called as primary users,M.SrujanaAssistant ProfessorDepartment ofElectronics andCommunication Engineering,CJITS, Yeshwanthapur,T.SE-mail:[email protected] they are present and identify the available spectrum if theyare absent. This is usually achieved by sensing the RFenvironment, process called spectrum sensing 1–4. Theobjectives of spectrum sensing are twofold: first, CR usersshould not cause harmful interference to primary users byeither switching to an available band or limiting itsinterference with primary users at an acceptable level and,second, CR users should efficiently identify the spectrumholes for required throughput and quality-of service. Thus, thedetection performance in spectrum sensing is very muchcrucial to the performance of both primary and CR networks.In the conventional Cooperative cognitive radio networkformulation, some type of resource allocation problem wasaddressed, such as sub channel assignment for secondaryusers, relay assignment, and power control 4– 6. In 4, thesub carrier assignment, relay assignment, and secondary userrelay strategy optimization problems were approached withflexible channel cooperation in a multi-channel Cooperativecognitive radio network, where a unified optimizationframework based on Nash Bargaining Solutions wasdeveloped. In 5, 6, the spectrum leasing problem wasformulated for one primary user and multiple secondary usersas a Stackelberg game and the Nash equilibrium was derived.A single channel was assumed available and differenttransmissions were divided in time. The consideration of onechannel and one primary user in 5, 6 presents asimplification for practical scenarios where there are typicallymultiple channels and multiple primary users that coexist inthe coverage area of a base station in the cellular network.A multi phase cooperation scheme is proposed in order toimprove the network utility as well as the spectrum accessopportunity. We assign the selected relaying SUs as the groupof intermediate users, which cooperate with primary users intraffic relay and share the spectrum access opportunities withthe remaining secondary users, respectively. With the help ofintermediate users, the primary users can improve their ownperformance as well as not be involved in such a complicatedcooperation scheme with multiple secondary users.Meanwhile, the secondary users starving for the spectrumaccess opportunities attain what they want as well.Second, an intermediate users selection scheme isimplemented by the maximum weighted bipartite matchingalgorithm, and the utility of the cooperating pairs is enhancedby exploiting the ratio of cooperation pairs utility to the totalenergy consumption with the consideration of theintermediate users energy efficiency. Third, through thecooperation among the intermediate users and the surroundingsecondary users by using cooperative network coding, thestarving secondary users who form a cluster can obtain thetransmission opportunities without consuming too muchenergy to relay the primary users traffic. Conversely, theintermediate users utility and communication reliability canbe enhanced.2. SYSTEM MODELAs demonstrated in Fig. , we consider primary users andsecondary users are uniformly distributed in a cooperativecognitive radio network. The data has been transmitted to theBS over its own licensed channel by a base station (BS) servesprimary users and each primary user, given that the spectrumof primary users are orthogonal in frequency and/or space.Access points coexist in the same area serving secondary usersand each secondary user communicates with its correspondingAP.The first phase cooperation is between the primary user andthe selected cluster head intermediate users, while the secondphase cooperation is between the cluster head and othersecondary users in the cluster. As shown in Fig. 2, thecooperation between secondary users and primary users takesplace in a two-phase cooperation scheme in each time slot.The partner intermediate users selection scheme is firstperformed, and then the cluster head intermediate userscooperates with the primary user in a Time Division MultipleAccess manner that the primary user transmits its package tothe cooperating intermediate users and the intermediate usersrelays primary user’s last package to the BS simultaneously.After the cooperation between primary user and intermediateusers, the intermediate users find the cooperative secondaryusers who form a cluster from the surrounding starvingsecondary users. Then, the intermediate users and thesecondary users in the cluster cooperate by cooperativenetwork coding.Fig. 1. Scenario of Cooperative cognitive radio networkThe channel conditions are assumed to be stable during a fixtime slot, but vary independently from one slot to another.The spectrum sharing strategy operates in a time-slottedmanner and transmission channels are assumed to conform toa Rayleigh flat fading model. The CSI is available, which isestimated by exploiting techniques such as least squares (LS)estimation and minimum mean-square- error estimation9.Fig. 2. Time frame structure for the spectrum sharing strategyThe secondary users, who participate in the cooperation withthe primary users, send feed backs with their transmit powervalues they want to devote in delivering primary users trafficto the BS. In order to improve the performance of primarynetwork, the BS broadcasts the cooperation selectionrequirement to its surrounding secondary users. If onesecondary user can serve as the relay for multiple primaryusers, it sends different transmit power values correspondingto each primary user to the BS. However, in real networks,some SUs might not be willing to cooperate with the primaryuser, as it is quite energy consuming to relay primary userstraffic while the utility gain might be relatively low, i.e., theratio of utility to power consumption is low.But the secondary users still desire to gain the secondarytransmission opportunities so as to improve the utility. Inorder to solve the aforementioned problem, the selectedintermediate users cooperate with the remaining secondaryusers to benefit them. Meanwhile, through the cooperationbetween cluster head intermediate users and other secondaryusers in the cluster, the intermediate users can improve itsown performance as well.As shown in Fig. 2, The time frame structure includes two cooperations: the first phase cooperation and the second phasecooperation. In the intermediate users selection period of thefirst phase cooperation, after BS acquires theacknowledgement and the information from potentialintermediate users, the BS exploits the maximum weightedbipartite matching algorithm to find the most appropriatecooperative secondary users, i.e., the intermediate users. Afterpartner intermediate users selection, the primary usercooperates with the intermediate users in a Time DivisionMultiple Access manner. Then, the intermediate users broadcast its cooperation requirement to begin the second phasecooperation.The secondary users send the acknowledgement that theywant to join into the cooperation with the intermediate users.After that, the intermediate users transmit its packet towardsthe associated AP. During this transmissionProcess, the surrounding secondary users (form a cluster) whoare involved in the cooperation can overhear the data. Then,by using network coding, the secondary users in a clustercreate new combinations of packets from the received packetsand transmit those towards the respective AP. Thecooperation scheme among cluster head intermediate usersand secondary users in the cluster is referred as cooperativenetwork coding, in which the intermediate users is the sourceand the corresponding AP is the destination, and thesecondary users form a cluster to help intermediate users relaythe data from the source to the destination.Energy efficiency is considered in the system by using a ratioof utility to energy, which enables a trade off between utilityand energy consumption. Intermediate users selection isperformed to select the intermediate users who cooperate withthe primary users. The intermediate users are a group ofsecondary users that have better channel conditions than othersecondary users to relay primary users traffic.3. NUMERICAL RESULTSIn this section, in comparison with the random selectionscheme, the intermediate users selection scheme is evaluatedin a cooperative cognitive radio network simulator. Theoperation factors, e.g., cooperation time allocation andsecondary users power consumption, are also investigated.As shown in Fig. 1, there are 4 primary users and 6 secondaryusers in the cooperative cognitive radio network. The powersof primary users and secondary users vary from 1mW to2mW and from 0.5mW to 1.5mW, respectively. The proposedintermediate users selection (IS) scheme and random selection(RS) scheme, are compared i.e. the performance obtained byusing two different schemes.Network Utility7RSIS6.565.554.54 6 8 10 12 14 16 18 20 2Case IndexFig. 3. Comparison of the network utility attained by two different schemesIn Fig. 4 for the BS under different values of intermediateusers s power is evaluated, by the impact of choosing thevalue of. From the candidate secondary users in thecooperation Once BS collects the information; BS chooses anappropriate value of and to select the intermediate users,performs the maximum weighted matching. The whole utilityof cooperation pairs is simulated and the utility for differentvalues of is demonstrated in the figure.1.81.61.41.21alpha=1/50.8 alpha=1/3alpha=1/2alpha=3/50.6alpha=4/5alpha=4.5/50.40.2 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1IU power mWFig.4. Achieved utility vs. IU’s power for different valuesof .4. CONCLUSIONIn this paper, we have studied and implemented a novelcooperative spectrum sharing approach for a wireless networkconsisting of multiple primary and secondary users. we haveseen a spectrum sharing strategy based on two-phasecooperation including an intermediate users selection schemein cooperative cognitive radio network. The cooperation pairsbetween primary users and intermediate users s have beenobtained,By solving the maximum weighted bipartite matchingproblem. Thus we have got the maximum total utility.Further, the energy efficiency has been considered in theintermediate users selection problem and the selectedintermediate users cooperates with the primary user as well asits surrounding secondary users. With the help from theintermediate users the system utility and the spectrum accessopportunity have been improved. With the help of simulatedresult we have find that the utility obtained by performing theproposed partner intermediate users selection scheme isalways higher than that attained by the random selectionscheme in our cooperative cognitive radio network. In futurework, we will analyze the cooperation between theintermediate users and the surrounding secondary users indetail.5. REFERENCES1 Akyildiz, W. Y. Lee, M. Vuran, and S. Mohanty, “NextGeneration/ Dynamic Spectrum Access/Cognitive RadioWireless Networks: A survey,” ACM Journal onComputer Networks, vol. 50, no. 13, pp. 2127- 2159,Sep. 2006.2 A. Goldsmith, S. Jafer, I. Maric, and S. Srinivasa,”Breaking spectrum gridlock with cognitive radios: Aninformation theoretic perspective,” in Proceedings ofIEEE, vol. 97, no. 5, pp. 894-914, May 2009.3 O. Simeone, I. Stanojev, S. Savazzi, Y. Bar-Ness, U.Spagnolini, and R. Pickholtz, “Spectrum leasing tocooperating secondary ad hoc networks,” IEEE Journalon Selected Areas in Communications, vol. 26, no. 1, pp.203-213, Jan. 2008.4 Y. Han, A. Pandharipande, and S. Ting, “Cooperativedecode-and- forward relaying for secondary spectrumAccess,”IEEETransactionsonWirelessCommunications, vol. 8, no. 10, pp. 4945-4950, Oct.2009.5 N. Zhang, N. Lu, R. Lu, J. W. Mark, and X. Shen,”Energy-efficient and trust-aware cooperation incognitive radio networks,” in Proceedings of IEEEInternational Conference on Communications, pp. 1763-1767, Jun. 2012.66 T. Elkourdi and O. Simeone, “Spectrum Leasing viaCooperation with Multilpe Primary Users,” IEEETransactions on Vehicular Technology, vol. 61, no. 2, pp.820-825, Feb. 2012.7 V. Mahinthan, L. Cai, J. W. Mark and, X. Shen, “PartnerSelection Based on Optimal Power Allocation inCooperative-Diversity Systems,” IEEE Transactions onVehicular Technology, vol. 57, no. 1, pp. 511-520, Jan.2008.8 M. Seyfi, S. Muhaidat, and J. Liang, “Relay Selection inUnderlay Cognitive Radio Networks,” in Proceedings ofIEEE Wireless Communications and NetworkingConference, pp. 283-288, Apr. 2012.9 M. Biguesh and A. B. Gershman, “Training-BasedMIMO Channel Estimation: A Study of EstimatorTradeoffs and Optimal Training Signals,” IEEETransactions on Signal Processing, vol. 54, no. 3, pp.328-339, Mar. 2006.10 “Spectrum Sharing Strategy using Bipartite Matching forCooperative Cognitive Radio Networks”Yujie Tang,Yongkang Liu, Jon W. Mark and Xuemin (Sherman)Shen Centre for Wireless

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