Energy-efficient resource allocation for cognitive radio networks

In this paper, we investigate resource allocation for underlay cognitive radio (CR) networks, where the CR users coexist but do not cause unacceptable interference with licensed users. We focus on the energy efficiency (EE) performance of the system, where both throughput and energy consumption will be considered. We first formulate a sum-EE maximization problem and solve it by the Hungarian algorithm, called sum- EE-based scheme. Then, we take into account the preferences of CR users and licensed users and motivate a resource allocation scheme based on stable matching, called preference-based scheme. Numerical results demonstrate that the preference-based scheme has up to 50% performance gain on interference over the sum-EE-based scheme, while the former scheme has around 5% performance loss on EE performance compared to the latter one.

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

[2]  Geoffrey Ye Li,et al.  Ten years of research in spectrum sensing and sharing in cognitive radio , 2012, EURASIP J. Wirel. Commun. Netw..

[3]  Geoffrey Ye Li,et al.  Energy-Efficient Transmission for Protection of Incumbent Users , 2011, IEEE Transactions on Broadcasting.

[4]  Geoffrey Ye Li,et al.  Probabilistic Resource Allocation for Opportunistic Spectrum Access , 2010, IEEE Transactions on Wireless Communications.

[5]  Cong Xiong,et al.  Energy-efficient wireless communications: tutorial, survey, and open issues , 2011, IEEE Wireless Communications.

[6]  Vahid Asghari,et al.  Adaptive Rate and Power Transmission in Spectrum-Sharing Systems , 2010, IEEE Transactions on Wireless Communications.

[7]  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.

[8]  Gerhard Fettweis,et al.  Framework for Link-Level Energy Efficiency Optimization with Informed Transmitter , 2011, IEEE Transactions on Wireless Communications.

[9]  Yiyang Pei,et al.  Energy-Efficient Design of Sequential Channel Sensing in Cognitive Radio Networks: Optimal Sensing Strategy, Power Allocation, and Sensing Order , 2011, IEEE Journal on Selected Areas in Communications.

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

[11]  Ephraim Zehavi,et al.  Stable matching for channel access control in cognitive radio systems , 2010, 2010 2nd International Workshop on Cognitive Information Processing.

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

[13]  Carlos Mosquera,et al.  Dynamic Spectrum Leasing: A New Paradigm for Spectrum Sharing in Cognitive Radio Networks , 2010, IEEE Transactions on Vehicular Technology.

[14]  Syed Ali Jafar,et al.  Soft Sensing and Optimal Power Control for Cognitive Radio , 2010, IEEE Transactions on Wireless Communications.