i-manager Publications

Channel Parameters Analysis Using Cognitive Radio Wireless Sensor Network (CR-WSN) for Wireless Communication

0*, M. Murugan**, P. S. Meena Sri***, V. Pradeepa****

* Research Scholar, Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, Chennai, Tamil Nadu, India & Senior Assistant Professor, Tagore Engineering College, Chennai, Tamil Nadu, India.

** Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, Chennai, Tamil Nadu, India.

***-**** Department of Electronics and Communication Engineering, Tagore Engineering College, Chennai, Tamil Nadu, India.

Periodicity:July - September'2019
DOI : https://doi.org/10.26634/jwcn.8.2.16796

Abstract

Wireless communication ensures well-organized spectrum utilization. It uses various resources allocation such as channel, frequency, bandwidth, etc., to make communication swiftly and easier. Cognitive Radio (CR) is an emerging technology that deals with the challenges to facilitate a feasible access to the spectrum. Hence, data losses and delay are the main issues in the Cognitive Radio Network (CRN). Many protocols and algorithms have been proposed, although the issues have not yet been completely addressed. Cognitive radio allows utilizing unused spectrum, so we have to check if it uses channel estimation techniques. Cognitive Radio Wireless Sensor Network (CR-WSN) coupled with cooperative communication minimizing the time delay for users. In this work, spectrum sensing and spectrum sharing is used to solve the problem of resource allocation and to ensure better management. Another method used in CR to improving wireless communication is Road Side Units (RSUs), which have limited coverage area and high deployment cost. The opportunistic encounters between mobile nodes increases the data transfer rate by carrier sensing and chunk scheduling using hand over process. Our objective is to control the data loss and increase the signal strength. A new approach is proposed in mobile node through carry and forward transfers to minimize the time delay and it is implemented by NS2 tool.

Keywords

Cognitive Radio, Access Point, Cognitive Radio Wireless Sensor Network, Local Area Network, Road Side Units.

How to Cite this Article?

Porselvi, R., Murugan, M., Sri, P. S. M., and Pradeepa, V. (2019). Channel Parameters Analysis Using Cognitive Radio Wireless Sensor Network (CR-WSN) for Wireless Communication. i-manager’s Journal on Wireless Communication Networks , 8(2), 20-28. https://doi.org/10.26634/jwcn.8.2.16796

References

[1]. Chen, K. C., Kung, L. H., Shiung, D., Prasad, R., & Chen, S. (2007, December). Self-organizing terminal architecture for cognitive radio networks. In the 10^th International Symposium on Wireless Personal Multimedia Communications (pp. 926-931).

[2]. Chen, W. H., Lin, W. R., Tsao, H. C., & Lin, C. (2016). Probabilistic power allocation for cognitive radio networks with outage constraints and one-bit side information. IEEE Transactions on Signal Processing, 64(4), 867-881. https://doi.org/10.1109/TSP.2015.2494852

[3]. Cordeiro, C., Challapali, K., Birru, D., & Shankar, S. (2005, November). IEEE 802.22: The first worldwide wireless standard based on cognitive radios. In 1^st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. (pp. 328-337). IEEE https://doi.org/10.1109/DYSPAN.2005. 1542649.

[4]. Holland, O., Attar, A., Olaziregi, N., Sattari, N., & Aghvami, A. H. (2006, September). A universal resource awareness channel for cognitive radio. In 2006 IEEE 17^th International Symposium on Personal, Indoor and Mobile Radio Communications (pp. 1-5). IEEE. https://doi.org/ 10.1109/PIMRC.2006.254338

[5]. Hung, S. C., Hung, S. Y., Wu, E. H. K., & Chen, G. H. (2006, September). A decentralized CR system algorithm for cognitive borrowing scheme from primary users. In 2006 IEEE 17^th International Symposium on Personal, Indoor and Mobile Radio Communications (pp. 1-5). IEEE. https://doi.org/10.1109/PIMRC.2006.254337

[6]. Kang, X., Zhang, R., Liang, Y. C., & Garg, H. K. (2011). Optimal power allocation strategies for fading cognitive radio channels with primary user outage constraint. IEEE Journal on Selected Areas in Communications, 29(2), 374-383. https://doi.org/10.1109/JSAC.2011.110210

[7]. Kolodzy, P. (2003, March). Spectrum policy task force: findings and recommendations. In International Symposium on Advanced Radio Technologies (ISART) (Vol. 5, No. 12, pp. 459-465).

[8]. Li, L., Zhou, X., Xu, H., Li, G. Y., Wang, D., & Soong, A. (2010). Simplified relay selection and power allocation in cooperative cognitive radio systems. IEEE Transactions on Wireless Communications, 10(1), 33-36. https://doi.org/ 10.1109/TWC.2010.101810.100311

[9]. Manolakos, A., Noam, Y., & Goldsmith, A. J. (2015). Null space learning in cooperative MIMO cellular networks using interference feedback. IEEE Transactions on Wi reless Communications, 14 (7), 3961-3977. https://doi.org/10.1109/TWC.2015.2415483

[10]. Mansukhani, J., Ray, P., & Varshney, P. K. (2016). Coupled detection and estimation based censored spectrum sharing in cognitive radio networks. IEEE Transactions on Wireless Communications, 15(6), 4206- 4217. https://doi.org/10.1109/TWC.2016.2536731

[11]. Noam, Y., & Goldsmith, A. J. (2013). Blind null-space learning for MIMO underlay cognitive radio with primary user interference adaptation. IEEE Transactions on Wireless Communications, 12 (4), 1722-1734. https://doi.org/10.1109/TWC.2013.021213.120643

[12]. Rezki, Z., & Alouini, M. S. (2012). Ergodic capacity of cognitive radio under imper fect channel-state information. IEEE Transactions on Vehicular Technology, 61(5), 2108-2119. https://doi.org/10.1109/TVT.2012. 2195042

[13]. Wang, L. C., Wang, C. W., Lu, Y. C., & Liu, C. M. (2007, March). A concurrent transmission MAC protocol for enhancing throughout and avoiding spectrum sensing in cognitive radio. In 2007 IEEE Wireless Communications and Networking Conference (pp. 121-126). IEEE.

[14]. Wang, Q., Ren, K., Ning, P., & Hu, S. (2015). Jamming-resistant multiradio multichannel opportunistic spectrum access in cognitive radio networks. IEEE Transactions on Vehicular Technology, 65(10), 8331- 8344. https://doi.org/10.1109/TVT.2015.2511071

[15]. Wu, Q., Wu, D., Xu, Y., & Wang, J. (2014). Demandaware multichannel opportunistic spectrum access: A local interaction game approach with reduced information exchange. IEEE Transactions on Vehicular Technology, 64(10), 4899-4904. https://doi.org/10.1109/ TVT.2014.2369484

[16]. Zhang, R., Gao, F., & Liang, Y. C. (2010). Cognitive beamforming made practical: Effective interference channel and learning-throughput tradeoff. IEEE Transactions on Communications, 58(2), 706-718. https://doi.org/10.1109/TCOMM.2010.02.080476

[17]. Zhang, L., Xiao, M., Wu, G., Zhao, G., Liang, Y. C., & Li, S. (2016). Proactive cross-channel gain estimation for spectrum sharing in cognitive radio. IEEE Journal on Selected Areas in Communications, 34(10), 2776-2790. https://doi.org/10.1109/JSAC.2016.2605298

[18]. Zhang, L., Zhao, G., Wu, G., & Chen, Z. (2012, December). Proactive channel gain estimation for coexistence between cognitive and primary users. In 2012 IEEE Global Communications Conference (GLOBECOM) (pp. 1410-1415). IEEE. https://doi.org/10. 1109/GLOCOM.2012.6503311

[19]. Zhu, X., Shen, L., & Yum, T. S. P. (2007). Analysis of cognitive radio spectrum access with optimal channel reservation. IEEE Communications Letters, 11(4), 304- 306. https://doi.org/10.1109/LCOM.2007.348282

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	35	35	200	15
Pdf & Online	35	35	400	25

Channel Parameters Analysis Using Cognitive Radio Wireless Sensor Network (CR-WSN) for Wireless Communication

Abstract

Keywords

How to Cite this Article?

References

If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Options for accessing this content: