Modeling of Spectrum Sensing Energy Consumption in Cognitive Radio Networks

doi:10.16182/j.issn1004731x.joss.201710014

Abstract

Abstract: In cognitive radio networks, the energy consumption in the process of spectrum sensing is the hot spot of research. A unified framework for modeling based on Poisson process was developed which could be used even under interference limited to count the energy consumption, thereby obtaining a spectrum sensing energy consumption. Spectrum sensing energy as the function of the number of secondary users in cognitive radio networks, their spatial density and the number of hops, could be modelled as a truncated gamma distribution. This model is used to statistically analyze cognitive users' energy consumption in the process of spectrum sensing under the circumstance of temporal perception, spatial perception and temporal - spatial joint perception. Simulation and analysis results show that the model is of scientific rationality.

Key words: gamma distribution, radio spectrum, sensing energy, statistical modeling

CLC Number:

TP393.17

Cui Ziru, Peng Yi. Modeling of Spectrum Sensing Energy Consumption in Cognitive Radio Networks[J]. Journal of System Simulation, 2017, 29(10): 2330-2335.

References

[1] Efe F Orumwense, Thomas J Afullo, Viranjay M Srivastava.Secondary User Energy Consumption in Cognitive Radio Networks[J]. Africon (S2153-0033), 2015, 23(4): 1-5.
[2] Saud Althunibat, Sandeep Narayanan, Marco Di Renzo, et al.On the Energy Consumption of the Decision-Fusion Rules in Cognitive Radio Networks[J]. Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 2012, 9(17): 125-129.
[3] P Sungsoo.Cognitive Radio Network with Energy Harvesting[J]. IEEE Transactions on Wireless Communications (S1536-1276), 2013, 12(3): 1386-1397.
[4] W Y Lee, I F Akyildiz.Spectrum-aware mobility management in cognitive radio cellular networks[J]. IEEE Transactions on Mobile Computing (S1536-1233), 2012, 11(4): 529-542.
[5] S Sengupta, K P Subbalakshmi.Open research issues in multi-hop cognitive radio networks[J]. IEEE Communications Magazine (S0163-6804), 2013, 51(4): 168-176.
[6] 杨明极, 吴琼, 石佳庆, 等. 信噪比比较的协作频谱感知算法[J]. 哈尔滨理工大学学报(S1007-2683), 2013, 18(2): 105-108.
(Yang M J, Wu Q, Shi J Q.A cooperative spectrum sensing algorithm of SNR comparison[J]. Journal of Harbin University of Science and Technology (S1007-2683), 2013, 18(2): 105-108.)
[7] 张阳. 基于频谱感知的自适应跳频抗干扰通信系统的仿真研究 [D]. 成都: 电子科技大学, 2014: 1-6.
(Zhang Y.Simulation research on adaptive frequency hopping anti-jamming communications system based on spectrum sensing [D]. Chengdu, China: University of Electronic Science and Technology of China, 2014.)
[8] 马恒. 认知无线电中频谱检测技术研究[J]. 无线电工程, 2014, 44(3): 77-80.
(Ma H.Research on spectrum detection technology in Cognitive radio[J]. Radio Engineering, 2014, 44(3): 77-80.)
[9] R W Heath, M Kountouris, T Bai.Modeling heterogeneous network interference using possion point processes[J]. IEEE Transactions on Signal Processing (S1053-587X), 2013, 61(16): 4114-4126.
[10] M S Ali, N B Mehta.Modeling time-varying aggregate interference in cognitive radio systems, and application to primary exclusive zone design[J]. IEEE Transactions on Wireless Communication (S1536-1276), 2014, 13(1): 429-439.
[11] Y Kondareddy, P Agrawal.Enforcing Cooperative spectrum sensing in cognitive radio networks[J]. Global Telecommunications Conference (S1930-529X), 2011, 10(11): 1-6.
[12] 李映雪. 认知无线电中的频谱感知技术研究技术研究 [D]. 北京: 北京邮电大学, 2013: 1-7.
(Li Y X.Research on spectrum sensing technology in cognitive radio [D]. Beijing, China: Beijing University of Posts and Telecommunications, 2013: 1-7.)
[13] Huang Xinlin, Wang Gang, Hu Fei, et al.Multitask Spectrum Sensing in Cognitive Radio Network via Spatiotemporal Data Mining[J]. IEEE Transactions on Vehicular Technology (S0018-9545), 2013, 62(3): 809-823.