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COVERAGE PERFORMANCE OF COGNITIVE RADIO NETWORKS POWERED BY RENEWABLE ENERGY

Part of: Geometric probability and stochastic geometry

Published online by Cambridge University Press:  24 May 2017

XIAOLONG CHEN Open the ORCID record for XIAOLONG CHEN [Opens in a new window] ,
XIANGBO MENG Open the ORCID record for XIANGBO MENG [Opens in a new window] ,
XIAOSHI SONG Open the ORCID record for XIAOSHI SONG [Opens in a new window]  and
CHUN SHAN
XIAOLONG CHEN*
Affiliation:
Jinhua Polytechnic, Jinhua, 321017, China email xlycxl@126.com
XIANGBO MENG
Affiliation:
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China email mengxiangbo.neu@gmail.com, songxiaoshi@cse.neu.edu.cn
XIAOSHI SONG
Affiliation:
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China email mengxiangbo.neu@gmail.com, songxiaoshi@cse.neu.edu.cn
CHUN SHAN
Affiliation:
School of Accountancy, Guangdong Polytechnic Normal University, Guangdong 510665, China email mmc518@126.com
*
✉xlycxl@126.com
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Abstract

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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 $\unicode[STIX]{x1D70C}_{p}$. 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.

Keywords

cognitive radioopportunistic spectrum accessstochastic geometryPoisson point processcoverage probabilityrenewable energy

MSC classification

Primary: 60D05: Geometric probability and stochastic geometry
Type
Research Article
Information
The ANZIAM Journal , Volume 58 , Issue 3-4 , April 2017 , pp. 287 - 305
Copyright
© 2017 Australian Mathematical Society 

References

Baccelli, F. and Blaszczyszyn, B., Stochastic geometry and wireless networks, Volume 1 (Now Publishers Inc, Boston, MA, 2010); doi:10.1561/1300000006.Google Scholar
Baccelli, F., Blaszczyszyn, B. and Muhlethaler, P., “Stochastic analysis of spatial and opportunistic aloha”, IEEE J. Sel. Areas Commun. 27 (2009) 1105–1119; doi:10.1109/JSAC.2009.090908.Google Scholar
Chung, W., Park, S., Lim, S. and Hong, D., “Spectrum sensing optimization for energy-harvesting cognitive radio systems”, IEEE Trans. Wirel. Commun. 13 (2014) 2601–2613; doi:10.1109/TWC.2014.032514.130637.Google Scholar
Ellabban, O., Abu-Rub, H. and Blaabjerg, F., “Renewable energy resources: current status, future prospects and their enabling technology”, Renew. Sustainable Energy Rev. 39 (2014) 748–764; doi:10.1016/j.rser.2014.07.113.CrossRefGoogle Scholar
Gallager, R. G., Stochastic processes: theory for applications (Cambridge University Press, UK, 2013), https://pdfs.semanticscholar.org/2152/c76ba0b6e30f990b52572d6cfc204af9c619.pdf.Google Scholar
Gunduz, D., Stamatiou, K., Michelusi, N. and Zorzi, M., “Designing intelligent energy harvesting communication systems”, IEEE Commun. Mag. 52 (2014) 210–216; doi:10.1109/mcom.2014.6710085.Google Scholar
Haenggi, M., Andrews, J. G., Baccelli, F., Dousse, O. and Franceschetti, M., “Stochastic geometry and random graphs for the analysis and design of wireless networks”, IEEE J. Sel. Areas Commun. 27 (2009) 1029–1046; doi:10.1109/jsac.2009.090902.Google Scholar
Haimes, Y., Research and practice in multiple criteria decision making: Proc. XIVth Int. Conf. on Multiple Criteria Decision Making (MCDM) Charlottesville, Virginia, USA, 1998, Volume of 487 Lecture Notes in Economics and Mathematical Systems (Springer, Berlin–Heidelberg, 2000); doi:10.1007/978-3-642-57311-8.CrossRefGoogle Scholar
Han, T. and Ansari, N., “Powering mobile networks with green energy”, IEEE Wirel. Commun. 21 (2014) 90–96; doi:10.1109/mwc.2014.6757901.CrossRefGoogle Scholar
Hasan, Z., Boostanimehr, H. and Bhargava, V. K., “Green cellular networks: a survey, some research issues and challenges”, IEEE Commun. Surv. Tutor. 13 (2011) 524–540; doi:10.1109/surv.2011.092311.00031.Google Scholar
Huang, K., “Spatial throughput of mobile ad hoc networks powered by energy harvesting”, IEEE Trans. Inf. Theory 59 (2013) 7597–7612; doi:10.1109/TIT.2013.2276811.Google Scholar
Kwasinski, A. and Kwasinski, A., “Increasing sustainability and resiliency of cellular network infrastructure by harvesting renewable energy”, IEEE Commun. Mag. 53 (2015) 110–116; doi:10.1109/mcom.2015.7081083.CrossRefGoogle Scholar
Lee, J., Andrews, J. G. and Hong, D., “The effect of interference cancellation on spectrum-sharing transmission capacity”, in: IEEE International Conference on Communications (ICC) (IEEE, Kyoto, Japan, 2011) 1–5; doi:10.1109/icc.2011.5963391.Google Scholar
Lee, C.-h. and Haenggi, M., “Interference and outage in Poisson cognitive networks”, IEEE Trans. Wirel. Commun. 11 (2012) 1392–1401; doi:10.1109/twc.2012.021512.110131.Google Scholar
Lee, S., Zhang, R. and Huang, K., “Opportunistic wireless energy harvesting in cognitive radio networks”, IEEE Trans. Wirel. Commun. 12 (2013) 4788–4799; doi:10.1109/twc.2013.072613.130323.Google Scholar
Pappas, N., Jeon, J., Ephremides, A. and Traganitis, A., “Optimal utilization of a cognitive shared channel with a rechargeable primary source node”, J. Commun. Netw. 14 (2012) 162–168; doi:10.1109/itw.2011.6089526.Google Scholar
Park, S. and Hong, D., “Achievable throughput of energy harvesting cognitive radio networks”, IEEE Trans. Wirel. Commun. 13 (2014) 1010–1022; doi:10.1109/twc.2013.121713.130820.Google Scholar
Park, S., Lee, S., Kim, B., Hong, D. and Lee, J., “Energy-efficient opportunistic spectrum access in cognitive radio networks with energy harvesting”, in: Proceedings of the 4th international conference on cognitive radio and advanced spectrum management Barcelona, Spain (ACM, New York, 2011) 62, 1–5; https://pdfs.semanticscholar.org/7359/2aa42bfc4f9d69b4a300049f46bd330ed628.pdf.Google Scholar
Proakis, J., Digital communications (McGraw-Hill, New York, 1995).Google Scholar
Song, X., Yin, C., Liu, D. and Zhang, R., “Spatial throughput characterization in cognitive radio networks with threshold-based opportunistic spectrum access”, IEEE J. Sel. Areas Commun. 32 (2014) 2190–2204; doi:10.1109/JSAC.2014.1411RP05.Google Scholar
Stoyan, D., Kendall, W. S. and Mecke, J., “Stochastic geometry and its applications”, in: Wiley series in probability and mathematical statistics (Wiley, Chichester, W. Sussex, New York, 1987). Rev. translation of: stochastische geometrie; doi:10.1002/9781118658222.Google Scholar
Tandra, R., Mishra, S. M. and Sahai, A., “What is a spectrum hole and what does it take to recognize one?”, Proc. IEEE 97 (2009) 824–848; doi:10.1109/JPROC.2009.2015710.Google Scholar
Vaze, R., “Transmission capacity of spectrum sharing ad hoc networks with multiple antennas”, IEEE Trans. Wirel. Commun. 10 (2011) 2334–2340; doi:10.1109/wiopt.2011.5930039.Google Scholar
Wang, X., Chen, M., Taleb, T., Ksentini, A. and Leung, V. C., “Cache in the air: exploiting content caching and delivery techniques for 5g systems”, IEEE Commun. Mag. 52 (2014) 131–139; doi:10.1109/mcom.2014.6736753.Google Scholar
Wang, B. and Liu, K. R., “Advances in cognitive radio networks: a survey”, IEEE J. Sel. Top. Signal Process. 5 (2011) 5–23; doi:10.1109/JSTSP.2010.2093210.Google Scholar
Weber, S., Andrews, J. G. and Jindal, N., “The effect of fading, channel inversion, and threshold scheduling on ad hoc networks”, IEEE Trans. Inf. Theory 53 (2007) 4127–4149; doi:10.1109/TIT.2007.907482.Google Scholar
Yin, S., Qu, Z. and Li, S., “Achievable throughput optimization in energy harvesting cognitive radio systems”, IEEE J. Sel. Areas Commun. 33 (2015) 407–422; doi:10.1109/JSAC.2015.2391712.Google Scholar
Yin, S., Zhang, E., Qu, Z., Yin, L. and Li, S., “Optimal cooperation strategy in cognitive radio systems with energy harvesting”, IEEE Trans. Wirel. Commun. 13 (2014) 4693–4707; doi:10.1109/TWC.2014.2322972.Google Scholar
Zeng, Y., Liang, Y.-C., Hoang, A. T. and Zhang, R., “A review on spectrum sensing for cognitive radio: challenges and solutions”, EURASIP J. Adv. Signal Process. 2010 (2010) 381–465; doi:10.1109/COMST.2016.2631080.Google Scholar
Zhang, H., Jiang, C., Beaulieu, N. C., Chu, X., Wang, X. and Quek, T. Q., “Resource allocation for cognitive small cell networks: a cooperative bargaining game theoretic approach”, IEEE Trans. Wirel. Commun. 14 (2015) 3481–3493; doi:10.1109/TWC.2015.2407355.Google Scholar
Zhao, Q. and Sadler, B. M., “A survey of dynamic spectrum access”, IEEE Signal Proc. Mag. 24 (2007) 79–89; doi:10.1109/msp.2007.361604.Google Scholar
Zhao, X., Yang, C., Yao, Y., Chen, Z. and Xia, B., “Cognitive and cache-enabled d2d communications in cellular networks”, Preprint, 2015, arXiv:1510.06480.Google Scholar