Coverage performance of cognitive radio networks powered by renewable energy

We analyse the coverage performance of cognitive radio networks powered by renewable energy. Particularly, with an energy harvesting module and energy storage module, the primary transmitters (PTs) and the secondary transmitters (STs) are assumed to be able to collect ambient renewables, and store them in batteries for future use. Upon harvesting sufficient energy, the corresponding PTs and STs (denoted by eligible PTs and STs) are then allowed to access the spectrum according to their respective medium access control (MAC) protocols. For the primary network, an Aloha-type MAC protocol is considered, under which the eligible PTs make independent decisions to access the spectrum with probability \(\rho_p\). By applying tools from stochastic geometry, we characterize the transmission probability of the STs. Then, with the obtained results of transmission probability, we evaluate the coverage (transmission nonoutage) performance of the overlay CR network powered by renewable energy. Simulations are also provided to validate our analysis. doi:10.1017/S1446181117000244

[1]  François Baccelli,et al.  Stochastic analysis of spatial and opportunistic aloha , 2009, IEEE Journal on Selected Areas in Communications.

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

[3]  Kaibin Huang,et al.  Spatial Throughput of Mobile Ad Hoc Networks Powered by Energy Harvesting , 2011, IEEE Transactions on Information Theory.

[4]  Anthony Ephremides,et al.  Optimal utilization of a cognitive shared channel with a rechargeable primary source node , 2012, Journal of Communications and Networks.

[5]  Jeffrey G. Andrews,et al.  Stochastic geometry and random graphs for the analysis and design of wireless networks , 2009, IEEE Journal on Selected Areas in Communications.

[6]  K. J. Ray Liu,et al.  Advances in cognitive radio networks: A survey , 2011, IEEE Journal of Selected Topics in Signal Processing.

[7]  Sungsoo Park,et al.  Achievable Throughput of Energy Harvesting Cognitive Radio Networks , 2014, IEEE Transactions on Wireless Communications.

[8]  Yonghong Zeng,et al.  A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions , 2010, EURASIP J. Adv. Signal Process..

[9]  Xiaofei Wang,et al.  Cache in the air: exploiting content caching and delivery techniques for 5G systems , 2014, IEEE Communications Magazine.

[10]  Alexis Kwasinski,et al.  Increasing sustainability and resiliency of cellular network infrastructure by harvesting renewable energy , 2015, IEEE Communications Magazine.

[11]  Yacov Y. Haimes,et al.  Research and Practice in Multiple Criteria Decision Making , 2000 .

[12]  Frede Blaabjerg,et al.  Renewable energy resources: Current status, future prospects and their enabling technology , 2014 .

[13]  Danpu Liu,et al.  Spatial Throughput Characterization in Cognitive Radio Networks with Threshold-Based Opportunistic Spectrum Access , 2013, IEEE Journal on Selected Areas in Communications.

[14]  Anant Sahai,et al.  What is a Spectrum Hole and What Does it Take to Recognize One? , 2009, Proceedings of the IEEE.

[15]  Jeffrey G. Andrews,et al.  The Effect of Fading, Channel Inversion, and Threshold Scheduling on Ad Hoc Networks , 2007, IEEE Transactions on Information Theory.

[16]  Nirwan Ansari,et al.  Powering mobile networks with green energy , 2014, IEEE Wireless Communications.

[17]  Vijay K. Bhargava,et al.  Green Cellular Networks: A Survey, Some Research Issues and Challenges , 2011, IEEE Communications Surveys & Tutorials.

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

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

[20]  Bin Xia,et al.  Cognitive and Cache-enabled D2D Communications in Cellular Networks , 2015, ArXiv.

[21]  Martin Haenggi,et al.  Interference and Outage in Poisson Cognitive Networks , 2012, IEEE Transactions on Wireless Communications.

[22]  Chunxiao Jiang,et al.  Resource Allocation for Cognitive Small Cell Networks: A Cooperative Bargaining Game Theoretic Approach , 2015, IEEE Transactions on Wireless Communications.

[23]  Liang Yin,et al.  Optimal Cooperation Strategy in Cognitive Radio Systems with Energy Harvesting , 2014, IEEE Transactions on Wireless Communications.

[24]  Deniz Gündüz,et al.  Designing intelligent energy harvesting communication systems , 2014, IEEE Communications Magazine.

[25]  Sixing Yin,et al.  Achievable Throughput Optimization in Energy Harvesting Cognitive Radio Systems , 2015, IEEE Journal on Selected Areas in Communications.