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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2022

16.07.2022

Performance Analysis of a Prediction-Sensing Based Cooperative Energy Harvesting CRN Over Rician Fading Channels

verfasst von: Banani Talukdar, Deepak Kumar, Wasim Arif

Erschienen in: Wireless Personal Communications | Ausgabe 4/2022

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Abstract

In this work, we investigate a prediction-sensing based cooperative energy harvesting cognitive radio network (EH-CRN) over a Rician fading environment. Incorporating a prediction based sensing mechanism provides enhanced protection to the quality-of-service (QoS) of the primary user (PU) subsequently leading to an improved spectrum efficiency. The complex channel model in analysing the performance of the prediction-sensing based EH-CRN is taken to be as Rician considering the envisioned higher frequency communication systems. An optimal sensing time is also evaluated whereat the secondary network throughput is the maximum. The system behaviour is analysed in terms of two standard performance indices viz., outage probability (OP) and the symbol error rate (SER). The exact analytical expression for both the OP and SER in a Rician fading channel environment is obtained. The impact of Rician fading factor, signal-to-noise ratio (SNR), number of cooperative cognitive radio nodes, data rate parameter and prediction error on the detection probability, harvested energy, normalized throughput, OP and SER is analysed and discussed. The SER for different digital modulation techniques are also obtained. Monte Carlo simulations verify the correctness of the analytical modelling of the system.
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Literatur
1.
Federal Communications Commission.(2002). Spectrum policy task force report, FCC 02–155.
2.
Haykin, S.(2005). Cognitive radio: brain-empowered wireless communications. IEEE Journal on selected areas in communications, 23(2), 201–220.
3.
Mitola, J.I. (2002). Cognitive radio. An integrated agent architecture for software defined radio.
4.
Liang, Y. C., Zeng, Y., Peh, E. C., & Hoang, A. T. (2008). Sensing-throughput tradeoff for cognitive radio networks. IEEE Transactions on Wireless Communications, 7(4), 1326–1337.CrossRef
5.
Yang, J., & Zhao, H. (2015). Enhanced throughput of cognitive radio networks by imperfect spectrum prediction. IEEE Communications Letters, 19(10), 1738–1741.CrossRef
6.
Han, C., Harrold, T., Armour, S., Krikidis, I., Videv, S., Grant, P. M., Haas, H., Thompson, J. S., Ku, I., Wang, C. X., & Le, T. A. (2011). Green radio: Radio techniques to enable energy-efficient wireless networks. IEEE communications magazine, 49(6), 46–54.CrossRef
7.
Paradiso, J. A., & Starner, T. (2005). Energy scavenging for mobile and wireless electronics. IEEE Pervasive computing, 4(1), 18–27.CrossRef
8.
Sudevalayam, S., & Kulkarni, P. (2010). Energy harvesting sensor nodes: Survey and implications. IEEE Communications Surveys & Tutorials, 13(3), 443–461.CrossRef
9.
El Shafie, A., Ashour, M., Khattab, T., & Mohamed, A. (2015). On spectrum sharing between energy harvesting cognitive radio users and primary users. International conference on computing, networking and communications (ICNC), pp. 214–220.
10.
El Shafie, A., & Khattab, T. (2014). Maximum throughput of a cooperative energy harvesting cognitive radio user. IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC), pp. 1067–1072.
11.
Yang, J., & Ulukus, S. (2011). Optimal packet scheduling in an energy harvesting communication system. IEEE Transactions on Communications, 60(1), 220–230.CrossRef
12.
Tutuncuoglu, K., & Yener, A. (2012). Optimum transmission policies for battery limited energy harvesting nodes. IEEE Transactions on Wireless Communications, 11(3), 1180–1189.CrossRef
13.
Park, S., & Hong, D. (2014). Achievable throughput of energy harvesting cognitive radio networks. IEEE Transactions on Wireless Communications, 13(2), 1010–1022.CrossRef
14.
Park, S., & Hong, D. (2013). Optimal spectrum access for energy harvesting cognitive radio networks. IEEE Transactions on Wireless Communications, 12(12), 6166–6179.CrossRef
15.
Mao, S., Cheung, M.H., & Wong, V.W. (2012). An optimal energy allocation algorithm for energy harvesting wireless sensor networks. IEEE International Conference on Communications (ICC), pp. 265–270.
16.
Lee, S., Zhang, R., & Huang, K. (2013). Opportunistic wireless energy harvesting in cognitive radio networks. IEEE transactions on Wireless Communications, 12(9), 4788–4799.CrossRef
17.
Bhowmick, A., Roy, S. D., & Kundu, S. (2016). Throughput of a Cognitive radio network with energy-harvesting based on primary user signal. IEEE Wireless Communications Letters, 5(2), 136–139.CrossRef
18.
Zhang, R., & Ho, C. K. (2013). MIMO broadcasting for simultaneous wireless information and power transfer. IEEE Transactions on Wireless Communications, 12(5), 1989–2001.CrossRef
19.
Das, G. C., Prasad, B., Bhowmick, A., Roy, S. D., & Kundu, S. (2018). Performance of an energy harvesting cooperative cognitive radio network with hybrid spectrum access scheme. Wireless Personal Communications, 99(4), 1503–1520.CrossRef
20.
Nguyen, T. N., Quang Minh, T. H., Tran, P. T., & Vozňák, M. (2018). Energy harvesting over Rician fading channel: a performance analysis for half-duplex bidirectional sensor networks under hardware impairments. Sensors, 18(6), 1781.CrossRef
21.
Le, Q. N., Bao, V. N. Q., & An, B. (2018). Full-duplex distributed switch-and-stay energy harvesting selection relaying networks with imperfect CSI: Design and outage analysis. Journal of Communications and Networks, 20(1), 29–46.CrossRef
22.
Nguyen, D. K., Jayakody, D. N. K., Chatzinotas, S., Thompson, J. S., & Li, J. (2017). Wireless energy harvesting assisted two-way cognitive relay networks: Protocol design and performance analysis. IEEE Access, 5, 21447–21460.CrossRef
23.
Katti, A., & Lobiyal, D. K. (2020). Sensing coverage for wireless sensor networks in shadowed rician fading environments. Wireless Personal Communications, 110(3), 1109–1125.CrossRef
24.
Alimohammad, A., Fard, S. F., Cockburn, B. F., & Schlegal, C. (2009). Compact Rayleigh and Rician fading simulator based on random walk processes. IET Communications, 3(8), 1333–1342.CrossRef
25.
Oloffsson, T., Ahlen, A., & Gidlund, M. (2016). Modelling of the fading statistics of wireless sensor network channels in industrial environments. IEEE Transactions on Signal Processing, 64(12), 3021–3034.MathSciNetCrossRefMATH
26.
Zhao, F., Lin, H., Zhong, C., Hadzi-Velkov, Z., Karagiannidis, G. K., & Zhang, Z. (2017). On the capacity of wireless powered communication systems over Rician fading channels. IEEE Transactions on Communications, 66(1), 404–417.CrossRef
27.
Sahimi, M.K., MacCartney, G.R., Sun, S., & Rappaport, T.S. (2016). 28 GHz millimetre-wave ultrawideband small-scale fading models in wireless channels. IEEE 83rd Vehicular Technology Conference (VTC Spring), pp. 1–6.
28.
Zhu, Y., Xin, Y., & Kam, P.Y. (2006). Outage probability of Rician fading relay channels. IEEE Military Communications conference (MILCOM), pp. 1–6.
29.
Fernandes, C. A. R., da Costa, D. B., & de Almeida, A. L. (2014). Performance analysis of cooperative amplify-and-forward orthogonal frequency division multiplexing systems with power amplifier non-linearity. IET Communications, 8(18), 3223–3233.CrossRef
30.
Ghareeb, I., & Tashman, D. (2020). Statistical analysis of cascaded Rician fading channels. International Journal of Electronics Letters, 8(1), 46–59.CrossRef
31.
Tong, H. A., & Le, A. T. (2021). Improving spectrum efficiency in CR-NOMA over Rician Fading Channel Under Imperfection CSI. 10th International Conference on Information and Automation for Sustainability (ICIAfS), pp. 477–482. IEEE.
32.
Farzamnia, A., Mariappan, M., Moung, E., and Thangasalvam, R. (2022). BER Performance Evaluation of M-PSK and M-QAM Schemes in AWGN, Rayleigh and Rician Fading Channels. In: Mariappan, M., Arshad, M.R., Akmeliawati, R., Chong, C.S. (eds) Control Engineering in Robotics and Industrial Automation. Studies in Systems, Decision and Control, vol 371. https://​doi.​org/​10.​1007/​978-3-030-74540-0_​11
33.
Ericsson, A.B. (2015). 5G energy performance. Ericsson White Paper.
34.
Corp, ZTE. (2014). Driving the convergence of the physical and digital world-whitepaper on next generation mobile technology. In Technical Report.
35.
Telecom, S.K. (2014). SK Telecom 5G white paper, SK Telecom’s view on 5G vision, architecture, technology, and spectrum. In Technical Report.
36.
Kazmierski, T. J., & Beeby, S. (2014). Energy harvesting systems (p. 2011). Springer.
37.
Shi, Z., Teh, K. C., & Li, K. H. (2013). Energy-efficient joint design of sensing and transmission durations for protection of primary user in cognitive radio systems. IEEE Communications Letters, 17(3), 565–568.CrossRef
38.
Bhowmick, A., Yadav, K., Roy, S. D., & Kundu, S. (2017). Throughput of an energy harvesting cognitive radio network based on prediction of primary user. IEEE Transactions on Vehicular Technology, 66(9), 8119–8128.CrossRef
39.
Xing, X., Jing, T., Cheng, W., Huo, Y., & Cheng, X. (2013). Spectrum prediction in cognitive radio networks. IEEE Wireless Communications, 20(2), 90–96.CrossRef
40.
Le Borgne, Y. A., Santini, S., & Bontempi, G. (2007). Adaptive model selection for time series prediction in wireless sensor networks. Signal Processing, 87(12), 3010–3020.CrossRefMATH
41.
Singh, A., Bhatnagar, M. R., & Mallik, R. K. (2015). Performance of an improved energy detector in multihop cognitive radio networks. IEEE transactions on Vehicular technology, 65(2), 732–743.CrossRef
42.
Simon, M. K., & Alouini, M.-S. (2004). Digital communication over fading channels (2nd ed.). John Wiley and Sons.CrossRef
43.
Chandra, A. (2011). Performance analysis of diversity combining techniques for digital signals in wireless fading channels (PhD thesis). Jadavpur University.
44.
Nallagonda, S., Chandra, A., Roy, S. D., Kundu, S., Kukolev, P., & Prokes, A. (2016). Detection performance of cooperative spectrum sensing with hard decision fusion in fading channels. International Journal of Electronics, 103(2), 297–321.CrossRef
45.
Gradshteyn, I.S., & Ryzhik, I.M. (2014). Table of integrals, series, and products. Academic press.
46.
Arif, W., Hoque, S., Sen, D., & Baishya, S. (2015). A comprehensive analysis of spectrum handoff under different distribution models for cognitive radio networks. Wireless Personal Communications, 85(4), 2519–2548.CrossRef
Metadaten
Titel
Performance Analysis of a Prediction-Sensing Based Cooperative Energy Harvesting CRN Over Rician Fading Channels
verfasst von
Banani Talukdar
Deepak Kumar
Wasim Arif
Publikationsdatum
16.07.2022
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2022
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-022-09935-0

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