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Beamforming for MISO Cognitive Radio Networks Based on Successive Convex Approximation

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12384))

Abstract

This paper presents a novel beamforming optimization method for downlink underlying multiple-input single-output (MISO) cognitive radio (CR) networks. We formulate a beamforming optimization problem to maximize the sum rate of a CR network with one primary user (PU) pair and multiple secondary user (SU) pairs, subject to the quality of service requirement of the PU given the transmit power budgets at the base stations (BSs). To find the solution of the nonconvex problem, an iterative solving algorithm is proposed based on successive convex approximation (SCA). In developing the algorithm, we first reformulate the original nonconvex objective function as the difference of two concave functions. A concave substitute function is then derived using the one-order Taylor expansion. Based on this concave substitute, a convex semidefinite programming (SDP) is derived and solved. The new solution is then utilized to construct a new substitute. This process is repeated until a smooth point is reached. Simulation results show the effectiveness of the proposed SCA-based beamforming algorithm to achieve spectrum sharing.

This work was partially supported by the National Natural Science Foundation of China (NSFC) under Grants 61701269, 61672321, 61832012, 61771289 and 61373027, the Key Research and Development Program of Shandong Province under Grants 2019JZZY010313 and 2019JZZY020124, the Natural Science Foundation of Shandong Province under Grant ZR2017BF012, the Joint Research Fund for Young Scholars in Qilu University (Shandong Academy of Sciences) under Grant 2017BSHZ005.

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References

  1. Mitola, J., Maguire, G.Q.: Cognitive radio: making software radios more personal. IEEE Pers. Commun. 6(4), 13–18 (1999)

    Article  Google Scholar 

  2. Haykin, S.: Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005)

    Article  Google Scholar 

  3. Cai, Z., Ji, S., He, J., Bourgeois, A.G.: Optimal distributed data collection for asynchronous cognitive radio networks. In: 2012 IEEE 32nd International Conference on Distributed Computing Systems. IEEE (2012)

    Google Scholar 

  4. Tuan, P.V., Duy, T.T., Koo, I.: Multiuser miso beamforming design for balancing the received powers in secure cognitive radio networks. In: 2018 IEEE Seventh International Conference on Communications and Electronics (ICCE), pp. 39–43. IEEE (2018)

    Google Scholar 

  5. Zhang, R., Liang, Y.-C., Cui, S.: Dynamic resource allocation in cognitive radio networks. IEEE Sig. Process. Mag. 27(3), 102–114 (2010)

    Article  Google Scholar 

  6. Cai, Z., Ji, S., He, J., Wei, L., Bourgeois, A.G.: Distributed and asynchronous data collection in cognitive radio networks with fairness consideration. IEEE Trans. Parallel Distrib. Syst. 25(8), 2020–2029 (2014)

    Article  Google Scholar 

  7. Pang, J.-S., Scutari, G., Palomar, D.P., Facchinei, F.: Design of cognitive radio systems under temperature-interference constraints: a variational inequality approach. IEEE Trans. Sig. Process. 58(6), 3251–3271 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  8. Huang, S., Liu, X., Ding, Z.: Decentralized cognitive radio control based on inference from primary link control information. IEEE J. Sel. Areas Commun. 29(2), 394–406 (2011)

    Article  Google Scholar 

  9. Dadallage, S., Yi, C., Cai, J.: Joint beamforming, power, and channel allocation in multiuser and multichannel underlay miso cognitive radio networks. IEEE Trans. Veh. Technol. 65(5), 3349–3359 (2015)

    Article  Google Scholar 

  10. Wang, W., et al.: Joint precoding optimization for secure SWIPT in UAV-aided NOMA networks. IEEE Trans. Commun. 68(8), 5028–5040 (2020)

    Article  Google Scholar 

  11. Zhao, N., et al.: Secure transmission via joint precoding optimization for downlink MISO NOMA. IEEE Trans. Veh. Technol. 68(8), 7603–7615 (2019)

    Article  Google Scholar 

  12. Lai, I.-W., Zheng, L., Lee, C.-H., Tan, C.W.: Beamforming duality and algorithms for weighted sum rate maximization in cognitive radio networks. IEEE J. Sel. Areas Commun. 33(5), 832–847 (2014)

    Article  Google Scholar 

  13. Du, H., Ratnarajah, T., Pesavento, M., Papadias, C.B.: Joint transceiver beamforming in MIMO cognitive radio network via second-order cone programming. IEEE Trans. Sig. Process. 60(2), 781–792 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  14. Yao, R., Liu, Y., Lu, L., Li, G.Y., Maaref, A.: Cooperative precoding for cognitive transmission in two-tier networks. IEEE Trans. Commun. 64(4), 1423–1436 (2016)

    Article  Google Scholar 

  15. Dong, A., Zhang, H., Yuan, D., Zhou, X.: Interference alignment transceiver design by minimizing the maximum mean square error for MIMO interfering broadcast channel. IEEE Trans. Veh. Technol. 65(8), 6024–6037 (2016)

    Article  Google Scholar 

  16. Zhang, H., Dong, A., Jin, S., Yuan, D.: Joint transceiver and power splitting optimization for multiuser MIMO SWIPT under MSE QoS constraints. IEEE Trans. Veh. Technol. 66(8), 7123–7135 (2017)

    Article  Google Scholar 

  17. Luo, Z.-Q., Zhang, S.: Dynamic spectrum management: complexity and duality. IEEE J. Sel. Top. Sign. Process. 2(1), 57–73 (2008)

    Article  Google Scholar 

  18. Razaviyayn, M., Sanjabi, M., Luo, Z.-Q.: A stochastic successive minimization method for nonsmooth nonconvex optimization with applications to transceiver design in wireless communication networks. Math. Program. 157(2), 515–545 (2016)

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Anming Dong .

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Mao, R., Dong, A., Yu, J. (2020). Beamforming for MISO Cognitive Radio Networks Based on Successive Convex Approximation. In: Yu, D., Dressler, F., Yu, J. (eds) Wireless Algorithms, Systems, and Applications. WASA 2020. Lecture Notes in Computer Science(), vol 12384. Springer, Cham. https://doi.org/10.1007/978-3-030-59016-1_31

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  • DOI: https://doi.org/10.1007/978-3-030-59016-1_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59015-4

  • Online ISBN: 978-3-030-59016-1

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