Joint Cooperative Spectrum Sensing and Resource Allocation in Dynamic Wireless Energy Harvesting Enabled Cognitive Sensor Networks
Subject Areas : Network Management
Keywords: Cognitive Sensor Network, Transmission Rate, Mobility Model, Decode-and-Forward (DF) Protocol, Energy Consumption. ,
Abstract :
Due to the limitations of the natural frequency spectrum, dynamic frequency allocation is required for wireless networks. Spectrum sensing of a radio channel is a technique to identify the spectrum holes. In this paper, we investigate a dynamic cognitive sensor network, in which the cognitive sensor transmitter has the capability of the energy harvesting. In the first slot, the cognitive sensor transmitter participates in spectrum sensing and in the existence of the primary user, it harvests the energy from the primary signal, otherwise the sensor transmitter sends its signal to the corresponding receiver while in the second slot, using the decode-and-forward (DF) protocol, a part of the bandwidth is used to forward the signal of the primary user and the remained bandwidth is used for transmission of the cognitive sensor. Therefore, our purposed algorithm is to maximize the cognitive network transmission rate by selection of the suitable cognitive sensor transmitters subject to the rate of the primary transmission and energy consumption of the cognitive sensors according to the mobility model of the cognitive sensors in the dynamic network. Simulation results illustrate the effectiveness of the proposed algorithm in performance improvement of the network as well as reducing the energy consumption.
[1] J. Mitola and G. Q. Maguire, "Cognitive radio: Making software radios more personal, " IEEE Pers. Commun., Vol. 6, No. 4, pp. 13-18, Aug. 1999.
[2] M. Najimi, A. Ebrahimzadeh, S. M. H. Andargoli, and A. Fallahi, "A novel sensing nodes and decision node selection method for energy efficiency of cooperative spectrum sensing in cognitive sensor networks," IEEE Sensors J., Vol. 13, No. 5, pp. 1610-1621, May 2013.
[3] A. Ebrahimzadeh, M. Najimi, S. M. H. Andargoli, and A. Fallahi, "Sensor selection and optimal energy detection threshold for ef_cient cooperative spectrum sensing, " IEEE Trans. Veh. Technol., Vol. 64, No. 4, pp. 1565-1577, Apr. 2015.
[4] A. Bagheri, A. Ebrahimzadeh, and M. Najimi, "Sensor selection for extending lifetime of multi-channel cooperative sensing in cognitive sensor networks " Phys. Commun., Vol. 26, pp. 96_105, Feb. 2018.
[5] S. Kisseleff, X. Chen, I. F. Akyildiz, and W. H. Gerstacker, "Efficient charging of access limited wireless underground sensor networks," IEEE Trans. Commun., Vol. 64, No. 5, pp. 2130-2142, May 2016.
[6] A. Mehrabi, K. Kim, "General framework for network throughput maximization in sink-based energy harvesting wireless sensor networks, " IEEE Trans. Mobile Computing, Vol. 16, No. 7, pp. 1881-1896, July,2017.
[7] G. Zheng, Z. Ho, E. A. Jorswieck, and B. Ottersten, "Information and energy cooperation in cognitive radio networks," IEEE Trans. Signal Process., Vol. 62, No. 9, pp. 2290-2303, May 2014.
[8] J. Yan, Y. Liu, "A dynamic SWIPT approach for cooperative cognitive radio networks," IEEE Trans. Vehicular Technology, Vol. 66, No. 12, pp. 1122-1136, Dec., 2017.
[9] J. R. Birge and F. Louveaux, Introduction to Stochastic Programming 2nd ed. New York, NY, USA: Springer, Jun. 2011.
[10] R. Caballero, E. Cerda, M. M. Muñoz, and L. Rey, "Analysis and comparisons of some solution concepts for stochastic programming problems, " Top, Vol. 10, No. 1, pp. 101_123, Jun. 2002.
[11] H. Kaschel, K. Toledo, J. Torres Gomez and M. Julia Fernandez- Getino Garcia, "Energy-efficient cooperative spectrum sensing base on stochastic programming in dynamic cognitive radio sensor networks, " IEEE Access Journal, Vol.9, pp.720-732, Dec.2020.
[12] W. Lu, T. Nan, Y. Gong, M. Qin, X. Lui, Zh. Xu and Zh. Na, " Joint resource allocation for wireless energy harvesting enabled cognitive sensor networks, " IEEE Access Journal, Vol.6, pp.22480-22488,2018. [13] M. Karimi, S.M.S. Sadough and M.Torabi, "Improved joint spectrum sensing and power allocation for cognitive radio networks using probabilistic spectrum access, " IEEE Syst. Journal, Vol.13, No.4, pp. 3716-3723, Jan.2019.
[14] A. Pakmehr and A. Ghaffari , "Coverage improving with energy efficient inwireless sensor networks, "Journal of Information Systems and Telecommunication (JIST), Vol.5, No.1, 2017.
[15] M.R. Thaghva, R. Hamlbarani Haghi, A. Hanifi and K. Feizi, "Clustering for reduction of energy consumption in wireless sensor networks by AHP method," Journal of Information Systems and Telecommunication (JIST), Vol.6, No.1, 2018.
[16] M. Bavaghar, A. Mohajer and Sara Taghavi Motlagh, "Energy efficient clkustring algorithm for wireless sensor networks," Journal of Information Systems and Telecommunication (JIST), Vol.7, No. 4 , 2019.
[17] Zh. Liu, M. Zhao, Y. Yuan and X. Guan, "Subchannel and resource allocation in cognitive radio sensor network with wireless energy harvesting , " Computer Networks, Vol.167, Feb. 2020.
[18] M.Sharifi and M. Mohassel Feghhi, "Joint energy and throughput optimization in energy harvesting cognitive sensor networks, " 29th Iranian Conference on Electrical Engineering (ICEE), Tehran, Iran, May 2021.
[19] S. Ebrahimi Mood and M.M. Javadi, "Energy-efficient clustering method for wireless sensor networks using modified gravitational search algorithm, "Evolving Systems Journal, Vol.11, pp.575-578, 2020.
[20] J-C Charr , K. Deschinkel, R. Haj Mansour and M. Hakem, " Lifetime optimization for partial coverage in heterogeneous sensor networks, " Ad hoc Networks, Vol. 107, 2020.
[21] X. Deng, P. Guan, C. Hei, F. Li, J. Liu and N. Xiong, "An intelligent resource allocation scheme in energy harvesting cognitive wireless sensor networks, "IEEE Transactions on Network Science and Engineering, Vol.8, No.2, 1900-1912, 2021.
[22] X. Yan, Ch. Huang, J. Gan and X. Wu, "Game theory-based energy efficient clustering algorithm for wireless sensor networks, " Sensors Journal, Vol. 22, No.2, 2022. [23] A. Bagheri, A. Ebrahimzadeh and M. Najimi, "Lifetime maximization by dynamic threshold and sensor selection in multi-channel cognitive sensor networks, " Journal of Information Systems and Telecommunication (JIST), Vol.5, No.4, pp.225-235, 2017.
[24] M.Najimi, " Cooperative game approach for mobile primary user localization based on compressive sensing in multi-antenna cognitive sensor networks, " Journal of Information Systems and Telecommunication (JIST), Vol.7, No.2, pp.134-143, 2019.
[25] M. Monemian and M. Mahdavi, "Analysis of a new energy-based sensor selection method for cooperative spectrum sensing in cognitive radio networks, " IEEE Sensors J., Vol. 14, No. 9, pp. 3021_3032, Sep. 2014.
[26] B. Sklar, "Rayleigh fading channels in mobile digital communication systems part1:Characterization, " IEEE Commun. Mag., Jul. 1997.
[27] Y. Ma, D. I. Kim, Zh. Wu, "Optimization of ofdm-based cellular cognitive radio networks, " IEEE Trans. on Communications. Vol. 58, No.8, Aug.2010.
[28] S. Maleki, A. Pandharipande, and G. Leus, "Energy-efficient distributed spectrum sensing for cognitive sensor networks, " in Proc. 35th Annu. Conf. IEEE Ind. Electron. Soc., Nov. 2009, pp. 2642–2646.
[29] S. Maleki, A. Pandharipande, and G. Leus, "Energy efficient distributed spectrum sensing with convex optimization, " in Proc. 3rd Int. Workshop Comput. Advances in Multi-Sensor Adaptive Processing, Nov.2009, pp. 396–399.