Optimal time sharing in underlay cognitive radio systems with RF energy harvesting

Due to the fundamental tradeoffs, achieving spectrum efficiency and energy efficiency are two contending design challenges for the future wireless networks. However, applying radio-frequency (RF) energy harvesting (EH) in a cognitive radio system could potentially circumvent this tradeoff, resulting in a secondary system with limitless power supply and meaningful achievable information rates. This paper proposes an online solution for the optimal time allocation (time sharing) between the EH phase and the information transmission (IT) phase in an underlay cognitive radio system, which harvests the RF energy originating from the primary system. The proposed online solution maximizes the average achievable rate of the cognitive radio system, subject to the ε-percentile protection criteria for the primary system. The optimal time sharing achieves significant gains compared to equal time allocation between the EH and IT phases.

[1]  Xiao Lu,et al.  Dynamic spectrum access in cognitive radio networks with RF energy harvesting , 2014, IEEE Wireless Communications.

[2]  Sungsoo Park,et al.  Spectrum Sensing Optimization for Energy-Harvesting Cognitive Radio Systems , 2014, IEEE Transactions on Wireless Communications.

[3]  Sungsoo Park,et al.  Optimal Spectrum Access for Energy Harvesting Cognitive Radio Networks , 2013, IEEE Transactions on Wireless Communications.

[4]  Ying-Chang Liang,et al.  Optimal Power Allocation Strategies for Fading Cognitive Radio Channels with Primary User Outage Constraint , 2011, IEEE Journal on Selected Areas in Communications.

[5]  Robert Schober,et al.  Asymptotically optimal power allocation for point-to-point energy harvesting communication systems , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).

[6]  Sennur Ulukus,et al.  Achieving AWGN Capacity Under Stochastic Energy Harvesting , 2012, IEEE Transactions on Information Theory.

[7]  Hyungsik Ju,et al.  Throughput Maximization in Wireless Powered Communication Networks , 2013, IEEE Trans. Wirel. Commun..

[8]  Björn E. Ottersten,et al.  Information and Energy Cooperation in Cognitive Radio Networks , 2014, IEEE Transactions on Signal Processing.

[9]  Valentin Rakovic,et al.  Medium Access Control Protocols in Cognitive Radio Networks: Overview and General Classification , 2014, IEEE Communications Surveys & Tutorials.

[10]  Rui Zhang,et al.  Optimal Energy Allocation for Wireless Communications With Energy Harvesting Constraints , 2011, IEEE Transactions on Signal Processing.

[11]  Kaibin Huang,et al.  Opportunistic Wireless Energy Harvesting in Cognitive Radio Networks , 2013, IEEE Transactions on Wireless Communications.

[12]  Ekram Hossain,et al.  Cognitive and Energy Harvesting-Based D2D Communication in Cellular Networks: Stochastic Geometry Modeling and Analysis , 2014, IEEE Transactions on Communications.

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

[14]  Dong In Kim,et al.  Channel selection in cognitive radio networks with opportunistic RF energy harvesting , 2014, 2014 IEEE International Conference on Communications (ICC).

[15]  Anant Sahai,et al.  Shannon meets Tesla: Wireless information and power transfer , 2010, 2010 IEEE International Symposium on Information Theory.

[16]  Dong In Kim,et al.  Opportunistic Channel Access and RF Energy Harvesting in Cognitive Radio Networks , 2014, IEEE Journal on Selected Areas in Communications.