Cognitive radios with ergodic capacity guarantees for primary users

Cognitive radios implement adaptive resource allocation schemes that exploit knowledge of the channel state information to optimize the performance of the secondary users while limiting the interference to the primary users. The algorithms in this paper are designed to maximize the weighted sum-rate of secondary users which transmit orthogonally and adhere to three different constraints: i) limits on the long-term (average) power at each secondary transmitter; ii) limits on the long-term interfering power at each primary receiver; and iii) limits on the long-term capacity loss inflicted to each primary receiver. Although the long-term capacity constraints render the resultant optimization problem non-convex, it holds that it has zero-duality gap and that, due to the favorable structure in the dual domain, it can be efficiently solved.

[1]  Brian M. Sadler,et al.  A Survey of Dynamic Spectrum Access , 2007, IEEE Signal Processing Magazine.

[2]  Shuguang Cui,et al.  Dynamic Resource Allocation in Cognitive Radio Networks: A Convex Optimization Perspective , 2010, ArXiv.

[3]  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.

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

[5]  Georgios B. Giannakis,et al.  Optimal Cross-Layer Resource Allocation in Cellular Networks Using Channel- and Queue-State Information , 2012, IEEE Transactions on Vehicular Technology.

[6]  Georgios B. Giannakis,et al.  Optimizing Orthogonal Multiple Access Based on Quantized Channel State Information , 2009, IEEE Transactions on Signal Processing.

[7]  A. Banerjee Convex Analysis and Optimization , 2006 .

[8]  Chintha Tellambura,et al.  Joint bandwidth and power allocation in cognitive radio networks under fading channels , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  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.

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

[11]  Georgios B. Giannakis,et al.  Resource Allocation for Interweave and Underlay CRs Under Probability-of-Interference Constraints , 2012, IEEE Journal on Selected Areas in Communications.

[12]  Michael J. Neely,et al.  Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[13]  Xin Wang,et al.  Jointly Optimal Sensing Selection and Power Allocation for Cognitive Communications , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[14]  Shuguang Cui,et al.  Dynamic Resource Allocation in Cognitive Radio Networks , 2010, IEEE Signal Processing Magazine.

[15]  Abbas Jamalipour,et al.  Wireless communications , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[16]  Xin Wang,et al.  Optimal stochastic dual resource allocation for cognitive radios based on quantized CSI , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[17]  Alejandro Ribeiro,et al.  Separation Principles in Wireless Networking , 2010, IEEE Transactions on Information Theory.

[18]  Mohamed-Slim Alouini,et al.  Optimal power allocation of a sensor node under different rate constraints , 2012, 2012 IEEE International Conference on Communications (ICC).