An approach for beamforming with reduced complexity in MIMO cooperative cognitive radio networks (MIMO-CCRN) is presented. Specifically, a suboptimal approach with reduced complexity is proposed to jointly determine the transmit beamforming (TB) and cooperative beamforming (CB) weight vectors along with antenna subset selection in MIMO-CCRN. Two multiantenna secondary users (SU) constitute the desired link, one acting as transmitter (SU TX) and the other as receiver (SU RX) and they coexist with single-antenna primary and secondary users. Some of single antenna secondary users are recruited by desired link as cooperative relay. The maximization of the achievable rates in the desired link is the objective of this work, provided to interference constraints on the primary users are not violated. The objective is achieved by exploiting transmit beamforming at SU TX, cooperation of some secondary users, and cooperative beamforming. Meanwhile, the costs associated with RF chains at the radio front end at SU RX are reduced. Through simulations, it is shown that better performance in the desired link is attained, as a result of cooperation of SUs. 1. Introduction and Related Works The rigid structure of current spectrum allocation policies creates a bottleneck for rapidly growing wireless users. On the other hand, the Federal Communication Commission (FCC) measurements reveal that most of the licensed frequency bands are either unused or utilized less than 10% of the time [1]. To address the limitations on spectrum usage, the FCC has motivated the use of opportunistic spectrum sharing to make the licensed frequency bands accessible for unlicensed users. Transmit beamforming with receive combining is one of the simplest approaches to achieve full diversity [2]. Compared with traditional space time codes, beamforming and combining systems provide the same diversity order as well as significantly more array gain [3] at the expense of requiring channel state information at the transmitter. The issue of transmit beamforming in cognitive radio networks (CRN) and more specifically for secondary users has been investigated from various points of view in [4–7]. In [4], transmit beamforming (TB) is designed for MIMO cognitive radio networks in a single primary user- (PU-) single secondary user (SU) network, to minimize the transmit power of the SU while limiting the interference temperature to PU and achieving the signal-to-interference-plus-noise ratio (SINR) target at SU. The joint problem of TB and power control in CRN have been considered in [5, 6], where the
References
[1]
“FCC report of the spectrum efficiency working group,” Tech. Rep., FCC Spectrum Policy Task Force, 2002, http://transition.fcc.gov/sptf/files/SEWGFinalReport_1.pdf.
[2]
D. J. Love and R. W. Heath Jr., “Equal gain transmission in multiple-input multiple-output wireless systems,” in Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM '02), vol. 2, pp. 1124–1128, November 2002.
[3]
B. Hassibi and B. M. Hochwald, “High-rate codes that are linear in space and time,” IEEE Transactions on Information Theory, vol. 48, no. 7, pp. 1804–1824, 2002.
[4]
H. Du and T. Ratnarajah, “Transmit beamforming in MIMO cognitive radio network via semidefinite programming,” in Proceedings of the IEEE 73rd Vehicular Technology Conference (VTC '11), pp. 1–5, Yokohama, Japan, May 2011.
[5]
S. You, G. Noh, J. Lee, H. Wang, and D. Hong, “Joint beamforming and power control algorithm for cognitive radio network with the multi-antenna base station,” in Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC '10), pp. 1–6, Sydney, Australia, April 2010.
[6]
F. Wang and W. Wang, “Robust beamforming and power control for multiuser cognitive radio network,” in Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM '10), pp. 1–5, Miami, Fla, USA, December 2010.
[7]
M. G. Adian and H. Aghaeinia, “Joint transmit beamforming and antenna selection in cognitive radio networks,” in Proceedings of the 5th International Symposium on Telecommunications (IST '10), pp. 33–37, Tehran, Iran, December 2010.
[8]
K. Zarifi, S. Affes, and A. Ghrayeb, “Joint source power control and relay beamforming in amplify-and-forward cognitive networks with multiple source-destination pairs,” in Proceedings of the IEEE International Conference on Communications (ICC '11), pp. 1–6, Kyoto, Japan, June 2011.
[9]
J. Liu, W. Chen, Z. Cao, and Y. J. Zhang, “Delay optimal scheduling for cognitive radio networks with cooperative beamforming,” in Proceedings of the IEEE International Conference on Communications (ICC '11), pp. 1–5, Kyoto, Japan, June 2011.
[10]
M. A. Beigi and S. M. Razavizadeh, “Cooperative beamforming in cognitive radio networks,” in Proceedings of the 2nd IFIP Wireless Days (WD '09), pp. 1–5, Paris, France, December 2009.
[11]
J. Liu, W. Chen, Z. Cao, and Y. J. Zhang, “Cooperative beamforming aided incremental relaying in cognitive radios,” in Proceedings of the IEEE International Conference on Communications (ICC '11), pp. 1–5, Kyoto, Japan, June 2011.
[12]
D. J. Love, R. W. Heath, and T. Strohmer, “Grassmannian beamforming for multiple-input multiple-output wireless systems,” IEEE Transactions on Information Theory, vol. 49, no. 10, pp. 2735–2747, 2003.
[13]
A. F. Molisch, M. Z. Win, and J. H. Winters, “Capacity of MIMO systems with antenna selection,” in Proceedings of the International Conference on Communications (ICC '01), pp. 570–574, Helsinki, Finland, June 2000.
[14]
A. Gorokhov, D. A. Gore, and A. J. Paulraj, “Receive antenna selection for MIMO spatial multiplexing: theory and algorithms,” IEEE Transactions on Signal Processing, vol. 51, no. 11, pp. 2796–2807, 2003.
[15]
M. Kang, L. Yang, and M. S. Alouini, “Capacity of MIMO Rician channels with multiple correlated Rayleigh co-channel interferers,” in Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM '03), vol. 2, pp. 1119–1123, December 2003.
[16]
J. N. Laneman and G. W. Wornell, “Distributed space-time-coded protocols for exploiting cooperative diversity in wireless networks,” IEEE Transactions on Information Theory, vol. 49, no. 10, pp. 2415–2425, 2003.
[17]
W. Yu and R. Lui, “Dual methods for nonconvex spectrum optimization of multicarrier systems,” IEEE Transactions on Communications, vol. 54, no. 7, pp. 1310–1322, 2006.
[18]
S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, New York, NY, USA, 2004.
[19]
P. Ubaidulla and A. Chockalingam, “Non-linear transceiver designs with imperfect CSIT using convex optimization,” in Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC '09), pp. 1–6, Budapest, Hungary, April 2009.
[20]
C. D. Meyer, Matrix Analysis and Applied Linear Algebra, SIAM, Philadelphia, Pa, USA, 2000.
[21]
D. N. C. Tse and P. Viswanath, Fundamentals of Wireless Communication, Cambridge University Press, Cambridge, UK, 2005.
