Abstract
In this paper, we consider a case with imperfect channel state information (CSI) in practical cognitive MIMO systems. We first analyze interference to the primary users (PUs) and then propose a joint power allocation and feedback rate control algorithm based on game theory. The utility function of each secondary user (SU) is defined as the corresponding throughput by CSI feedback minus the price as a linear function of feedback rate. Besides, the existence of the Nash equilibrium (NE) is proven. We derive the relationship between power allocation and feedback rate, and an iterative algorithm is proposed to reach the NE. Also, with the purpose of reduce interference and increase system capacity, water-filling (WF) power allocation and zero-forcing (ZF) beamforming are proposed. Simulation results shows that the proposed algorithm is better than equally distributed feedback rate scheme, obviously.
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References
Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies. Notice of Proposed Rule Making and Order FCC 03-322, Federal Communications Commission (2003)
Mitola, J., Maguire, G.Q.: Cognitive radio: making software radios more personal. IEEE Personal Commun. 6(4), 13–18 (1999)
Gesbert, D., Shafi, M., Shiu, D., Smith, P.J., Naguib, A.: From theory to practice: an overview of MIMO space-time coded wireless systems. IEEE J. Sel. Areas. Commun. 21(3), 281–302 (2003)
Scutari, G., Palomar, D.P., Barbarossa, S.: Cognitive MIMO radio. IEEE Signal Process. Mag. 25(6), 46–59 (2008)
Love, D.J., Heath, R.W., Lau, V.K.N., Gesbert, D., Rao, B.D., Andrews, M.: An overview of limited feedback in wireless communication systems. IEEE J. Sel. Areas. Commun. 26(8), 1341–1365 (2008)
Love, D.J., Heath, R.W., Santipach, W., Honing, M.L.: What is the value of feedback for MIMO channels. IEEE Commun. Mag. 42(10), 54–59 (2004)
Zhang, D., Wei, G., Zhu, J., Tian, Z.: On the bounds of feedback rates for pilot-assisted MIMO systems. IEEE Trans. Veh. Tech. 56(4), 1727–1736 (2006)
Dabbagh, A.D., Love, D.J.: Feedback rate-capacity loss tradeoff for limited feedback MIMO systems. IEEE Trans. Inf. Theory 52(5), 2190–2202 (2008)
Huang, K., Zhang, R.: Cooperative feedback for multiantenna cognitive radio networks. IEEE Trans. Signal Process. 59(2), 747–758 (2011)
Huang, K., Zhang, R.: Cooperative precoding with limited feedback for MIMO interference channels. IEEE Trans. Wireless Commum. 11(3), 1012–1021 (2012)
Sohn, I., Park, C.S., Lee, K.L.: Downlink multiuser MIMO systems with adaptive feedback rate. IEEE Trans. Veh. Tech. 61(3), 1445–1451 (2012)
Lee, J.H., Choi, W.: Optimal feedback rate sharing strategy in zero-forcing MIMO broadcast channels. IEEE Trans. Wireless Commum. 12(6), 3000–3011 (2013)
Song, L., Han, Z., Zhang, Z., Jiao, B.: Non-cooperative feedback-rate control game for channel state information in wireless networks. IEEE J. Sel. Areas. Commun. 30(1), 188–197 (2012)
Myung, J., Chen, Y., Liu, K.J.R., Kang, J.: Non-cooperative feedback control game for secondary transmitter in cognitive radio network. IEEE Signal Process. lett. 20(6), 571–574 (2013)
Goodman, D.: Network assisted power control for wireless data. Mobile Netw. Appl. 6(5), 409–415 (2001)
Saraydar, C.U., Mandayam, N.B., Goodman, D.: Efficient power control via pricing in wireless data networks. IEEE Trans. Commun. 50(2), 291–303 (2002)
Maskery, M., Krishnamurthy, V., Zhao, Q.: Decentralized dynamic spectrum access for cognitive radios: cooperative design of a noncooperative game. IEEE Trans. Commun. 57(2), 459–469 (2009)
Attar, A., Nakhai, M.R., Aghvami, A.H.: Cognitive radio game for secondary spectrum access problem. IEEE Trans. Wireless Commun. 8(4), 2121–2131 (2009)
Scutari, G., Palomar, D.P., Facchinei, F., Pang, J.S.: Convex optimization, game theory, and variational inequality Theory. IEEE Signal Processing Mag. 27(3), 35–49 (2010)
Scutari, G., Palomar, D.P., Barbarossa, S.: The MIMO iterative waterfilling algorithm. IEEE Trans. Signal Process. 57(5), 1917–1935 (2009)
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Zhao, F., Wang, C., Bie, R. (2014). Game-Theoretic Joint Power Allocation and Feedback Rate Control for Cognitive MIMO Systems with Limited Feedback. In: Cai, Z., Wang, C., Cheng, S., Wang, H., Gao, H. (eds) Wireless Algorithms, Systems, and Applications. WASA 2014. Lecture Notes in Computer Science, vol 8491. Springer, Cham. https://doi.org/10.1007/978-3-319-07782-6_48
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DOI: https://doi.org/10.1007/978-3-319-07782-6_48
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07781-9
Online ISBN: 978-3-319-07782-6
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