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Multi Phase Cooperation Architecture of Primary Users and Secondary Users Using Bipartite Graph

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This paper presents, a spectrum sharing strategy in cooperative cognitive radio networks. Multi-phase cooperation architecture is explained and studied with cooperation partner selection and spectrum sharing among secondary users. The data of primary users forwarded to the cooperation partners who are selected from secondary users, and then acquire the spectrum access opportunities for their own transmissions as is ward. The partner selection is modeled as an optimally weighted bipartite matching problem to maximize the total utility where energy efficiency is also considered just to increase the utility for the primary user-secondary user cooperation pairs. By the partner secondary user, further improvisation in the spectrum utilization is done by sharing the acquired spectrum with the surrounding secondary users via cooperative network coding. In the end the simulation results provided, which shows that to the dynamic traffic loads in the cooperative cognitive radio network, the proposed partner selection and spectrum sharing approach adapts well.

Keywords Cooperative cognitive radio network, quality-of-service, Intermediate users, Minimum mean-square- error.1.


The scarcity of spectral resources has become a severe problem due to the significant growth in commercial wireless services, in recent years, with the emergence of cooperative communications in wireless networks [3], a new communication paradigm in cooperative cognitive radio networks is proposed [4–6], termed cooperative cognitive radio networks. The traditional fixed spectrum allocation is proved inefficient since the frequency band is largely under-utilized [1]. Cognitive Radio (CR) [2] has been considered as a promising technology to improve spectrum utilization by allowing secondary users to access spectrum holes unoccupied by primary users (Pus). The rapid growth in wireless communications has contributed a huge demand on the deployment of new wireless services in both the licensed and unlicensed frequency spectrum. However, recent Studies show the fixed spectrum assignment policy enforced today results in poor spectrum utilization. To Address this problem, cognitive radio [1,2] has emerged as a promising technology to enable the access of the intermittent periods of unoccupied frequency bands, known as white space spectrum holes, and thereby increase the spectral efficiency.

The fundamental task of each Cognitive radio user in cognitive radio networks, in the most primitive sense, for detection of licensed users, also called as primary users, if they are present and identify the available spectrum if they are absent. This is usually achieved by sensing the environment, a process called spectrum sensing [1–4]. The Objectives of spectrum sensing are twofold: first, CR users should not cause harmful interference to primary users by either switching to an available band or limiting its interference with primary users at an acceptable level and, second, CR users should efficiently identify the spectrum holes for required throughput and quality-of-service. Thus, the detection performance in spectrum sensing is very much crucial to the performance of both primary and CR networks. In the conventional Cooperative cognitive radio network formulation, some type of resource allocation problem was addressed, such as sub-channel assignment for secondary users, relay assignment, and power control [4– 6]. In [4], thaisub carrier assignment, relay assignment, and secondary user relay strategy optimization problems were approached with flexible channel cooperation in a multi-channel Cooperative Cognitive radio network, where a unified optimization framework based on Nash Bargaining Solutions was developed.

In [5, 6], the spectrum leasing problem was formulated for one primary user and multiple secondary users as a Stackelberg game, and the Nash equilibrium was derived. A single channel was assumed available and different transmissions were divided in time. The consideration of one channel and one primary user in [5, 6] presents a simplification for practical scenarios where there are typically multiple channels and multiple primary users that coexist in the coverage area of a base station in the cellular network. A multi-phase cooperation scheme is proposed in order to improve the network utility as well as the spectrum access opportunity. We assign the selected relaying SUs as the group of intermediate users, which cooperate with primary users in traffic relay and share the spectrum access opportunities with the remaining secondary users, respectively. With the help of intermediate users, the primary users can improve their own performance as well as not be involved in such a complicated cooperation scheme with multiple secondary users. Meanwhile, the secondary users starving for the spectrum access opportunities attain what they want as well. Second, an intermediate user selection scheme is implemented by the maximum weighted bipartite matching algorithm, and the utility of the cooperating pairs is enhanced by exploiting the ratio of cooperation pairs utility to the total energy consumption with the consideration of the intermediate user’s energy efficiency. Third, through the cooperation among the intermediate users and the surrounding secondary users by using cooperative network coding, the starving secondary users who form a cluster can obtain the transmission opportunities without consuming too much energy to relay the primary user’s traffic. Conversely, the intermediate user’s utility and communication reliability can be enhanced.


We consider primary users and secondary users are uniformly distributed in a cooperative cognitive radio network. The data has been transmitted to Thebes over its own licensed channel by a base station (BS) serves primary users and each primary user, given that the spectrum of primary users are orthogonal in frequency and/or space. Access points coexist in the same area serving secondary users and each secondary user communicates with its corresponding AP. The first phase cooperation is between the primary user and the selected cluster head intermediate users, while the second phase cooperation is between the cluster head and other secondary users in the cluster. As shown in Fig. 2, the cooperation between secondary users and primary users takes place in a two-phase cooperation scheme in each time slot. The partner intermediate users selection scheme is first performed, and then the cluster head intermediate users cooperate with the primary user in a Time Division Multiple Access manners that the primary user transmits its package to the cooperating intermediate users and the intermediate users relay primary user’s last package to the BS simultaneously.

After the cooperation between the primary users and intermediate users, the intermediate users find the cooperative secondary users who form a cluster from the surrounding starving secondary users. Then, the intermediate users and the secondary users in the cluster cooperate by cooperative network coding.Fig. 1. Scenario of Cooperative cognitive radio networks The channel conditions are assumed to be stable during a fix time slot but vary independently from one slot to another. The spectrum sharing strategy operates in a time-slotted manner and transmission channels are assumed to conform toa Rayleigh flat fading model. The CSI is available, which is estimated by exploiting techniques such as least squares (LS)estimation and minimum mean-square- error estimation[9].Fig. 2. The time frame structure for the spectrum sharing strategy The secondary users, who participate in the cooperation with the primary users, send feed backs with their transmit power values they want to devote in delivering primary users traffic to the BS. In order to improve the performance of the primary network, the BS broadcasts the cooperation selection requirement to its surrounding secondary users. If on the secondary user can serve as the relay for multiple primary users, it sends different transmit power values corresponding to each primary user to the BS.

However, in real networks, some SUs might not be willing to cooperate with the primary user, as it is quite energy-consuming to relay primary user traffic while the utility gain might be relatively low, i.e., the ratio of utility to power consumption is low. But the secondary users still desire to gain secondary transmission opportunities so as to improve the utility. In Order to solve the aforementioned problem, the selected intermediate users cooperate with the remaining secondary users to benefit them. Meanwhile, through the cooperation between cluster head intermediate users and other secondary users in the cluster, the intermediate users can improve their own performance as well. As shown in Fig. 2, The time frame structure includes two corporations: the first phase cooperation and the second phase cooperation. In the intermediate user’s selection period of the first phase cooperation, after BS acquires the acknowledgment and the information from potential intermediate users, the BS exploits the maximum weighted bipartite matching algorithm to find the most appropriate cooperative secondary users, i.e., the intermediate users.

After partner intermediate users selection, the primary user cooperates with the intermediate users in a Time Division Multiple Access manners. Then, the intermediate users broadcast their cooperation requirement to begin the second phase cooperation. The secondary users send the acknowledgment that they want to join into the cooperation with the intermediate users. After that, the intermediate users transmit its packet towards the associated AP. During this transmission Process, the surrounding secondary users (form a cluster) who are involved in the cooperation can overhear the data. Then, by using network coding, the secondary users in a cluster create new combinations of packets from the received packet and transmit those towards the respective AP. The Cooperation scheme among cluster head intermediate users and secondary users in the cluster is referred as cooperative network coding, in which the intermediate users is the source and the corresponding AP is the destination, and the secondary users form a cluster to help intermediate users relay the data from the source to the destination. Energy efficiency is considered in the system by using a ratio of utility to energy, which enables a trade-off between utility and energy consumption. Intermediate user selection is performed to select the intermediate users who cooperate with the primary users. The intermediate users are a group of secondary users that have better channel conditions than other secondary users to relay primary users’ traffic?


In this section, in comparison with the random selection scheme, the intermediate users selection scheme is evaluated in a cooperative cognitive radio network simulator. Operation factors, e.g., cooperation time allocation and secondary users power consumption, are also investigated. As shown in Fig. 1, there are 4 primary users and 6 secondary users in the cooperative cognitive radio network. The powers of primary users and secondary users vary from 1mW to2mW and from 0.5mW to 1.5mW, respectively. The proposed intermediate users selection (IS) scheme and random selection(RS) scheme, are compared i.e. the performance obtained by using two different schemes. Network Utility 7RS 6.565.554.54 6 8 10 12 14 16 18 20 2 Case Index Fig. 3. Comparison of the network utility attained by two different schemes In Fig. 4 for the BS under different values of intermediate users s power is evaluated, by the impact of choosing the value of. From the candidate secondary users in the cooperation Once BS collects the information; BS chooses an appropriate value of and to select the intermediate users, performs the maximum weighted matching. The whole utility of cooperation pairs is simulated and the utility for different values of is demonstrated in the figure. alpha=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 mWF.4. Achieved utility vs. IU’s power for different values of.


In this paper, we have studied and implemented a novel cooperative spectrum sharing approach for a wireless network consisting of multiple primary and secondary users. we have seen a spectrum sharing strategy based on two-phase cooperation including an intermediate user selection scheme in the cooperative cognitive radio network. The cooperation pairs between primary users and intermediate users s have been obtained, By solving the maximum weighted bipartite matching problem. Thus we have got the maximum total utility. Further, the energy efficiency has been considered in the intermediate user’s selection problem and the selected intermediate users cooperate with the primary user as well as its surrounding secondary users. With help from the intermediate users the system utility and the spectrum access opportunity have been improved. With the help of simulated result, we have found that the utility obtained by performing the proposed partner intermediate users selection scheme is always higher than that attained by the random selection scheme in our cooperative cognitive radio network. In future work, we will analyze the cooperation between the intermediate users and the surrounding secondary users in detail.


[1] Akyildiz, W. Y. Lee, M. Vuran, and S. Mohanty, “Next Generation/ Dynamic Spectrum Access/Cognitive Radio Wireless Networks: A survey,” ACM Journal on Computer 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: An Information theoretic perspective,” in Proceedings of IEEE, 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 to cooperating secondary ad hoc networks,” IEEE Journal On Selected Areas in Communications, vol. 26, no. 1, pp.203-213, Jan. 2008.

[4] Y. Han, A. Pandharipande, and S. Ting, “Cooperative Decode-and- forward relaying for secondary spectrum Access,”IEEE Transactions on Wireless Communications, 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 in cognitive radio networks,” in Proceedings of IEEE International Conference on Communications, pp. 1763-1767, Jun. 2012.6

[6] T. Elkouri and O. Simeone, “Spectrum Leasing via Cooperation with Multiple Primary Users,” IEEE Transactions on Vehicular Technology, vol. 61, no. 2, pp.820-825, Feb. 2012.

[7] V. Mahinthan, L. Cai, J. W. Mark and, X. Shen, “Partner Selection Based on Optimal Power Allocation in Cooperative-Diversity Systems,” IEEE Transactions on Vehicular Technology, vol. 57, no. 1, pp. 511-520, Jan.2008.

[8] M. Seyfi, S. Muhaidat, and J. Liang, “Relay Selection in Underlay Cognitive Radio Networks,” in Proceedings of IEEE Wireless Communications and Networking Conference, pp. 283-288, Apr. 2012.]

[9] M. Biguesh and A. B. Gershman, “Training-Based MIMO Channel Estimation: A Study of Estimator Tradeoffs and Optimal Training Signals,” IEEE Transactions on Signal Processing, vol. 54, no. 3, pp.328-339, Mar. 2006.

[10] “Spectrum Sharing Strategy using Bipartite Matching for Cooperative Cognitive Radio Networks”Yujie Tang,Yongkang Liu, Jon W. Mark and Xuemin (Sherman)Shen Centre for Wireless Communications, University of Waterloo, ON, Canada, N2L 3G1 Globecom

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