On user selection in cognitive broadcast channels

This paper considers the cognitive broadcast channels (BC) with user selection, and presents an analytical investigation on the performance of such systems. Specifically, exact closed-form expressions for the outage probability and multiuser interference diversity (MID) gain of the cognitive BC are derived. In addition, closed-form upper and lower bounds for the ergodic capacity of the system are presented. These analytical results not only provide a fast and efficient means to evaluate the performance of the system, they also enable us to gain valuable insights on the impact of key parameters such as peak transmit power, interference temperature constraint, primary transmit power and channel fading parameters on the system performance. Our findings suggest an intuitive result that increasing the number of cognitive users significantly improves the performance of the system, and a rather surprising conclusion that strong primary user interference is beneficial in terms of the MID gain.

[1]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[2]  Sonia Aïssa,et al.  Capacity and power allocation for spectrum-sharing communications in fading channels , 2009, IEEE Transactions on Wireless Communications.

[3]  Milton Abramowitz,et al.  Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables , 1964 .

[4]  Ying-Chang Liang,et al.  Investigation on multiuser diversity in spectrum sharing based cognitive radio networks , 2008, IEEE Communications Letters.

[5]  Rui Zhang,et al.  On peak versus average interference power constraints for protecting primary users in cognitive radio networks , 2008, IEEE Transactions on Wireless Communications.

[6]  Amir Ghasemi,et al.  Fundamental limits of spectrum-sharing in fading environments , 2007, IEEE Transactions on Wireless Communications.

[7]  H. N. Nagaraja,et al.  Order Statistics, Third Edition , 2005, Wiley Series in Probability and Statistics.

[8]  Herbert A. David,et al.  Order Statistics , 2011, International Encyclopedia of Statistical Science.

[9]  Mansoor Shafi,et al.  Capacity Limits and Performance Analysis of Cognitive Radio With Imperfect Channel Knowledge , 2010, IEEE Transactions on Vehicular Technology.

[10]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[11]  M. Abramowitz,et al.  Handbook of Mathematical Functions With Formulas, Graphs and Mathematical Tables (National Bureau of Standards Applied Mathematics Series No. 55) , 1965 .

[12]  Leila Musavian,et al.  Fundamental capacity limits of cognitive radio in fading environments with imperfect channel information , 2009, IEEE Transactions on Communications.

[13]  Michael Gastpar On Capacity Under Received-Signal Constraints , 2004 .

[14]  Andrea J. Goldsmith,et al.  Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective , 2009, Proceedings of the IEEE.

[15]  Wan Choi,et al.  Multi-user diversity in a spectrum sharing system , 2009, IEEE Transactions on Wireless Communications.

[16]  Ying-Chang Liang,et al.  Optimal power allocation for fading channels in cognitive radio networks: Ergodic capacity and outage capacity , 2008, IEEE Transactions on Wireless Communications.

[17]  Jason Gao,et al.  Channel Capacity Limits of Cognitive Radio in Asymmetric Fading Environments , 2008, 2008 IEEE International Conference on Communications.

[18]  Patrick Mitran,et al.  Rate of Channel Hardening of Antenna Selection Diversity Schemes and Its Implication on Scheduling , 2007, IEEE Transactions on Information Theory.