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Year 2020, Volume: 4 Issue: 3, 119 - 129, 28.09.2020

Abstract

References

  • [1] J.J. Popoola, and R. van Olst, “The performance evaluation of a spectrum sensing implementation using an automatic modulation classification detection methods with a universal software radio peripheral”, Expert Systems with Applications, Vol. 40, No. 6, pp. 2165-2173, 2013.
  • [2] J.J. Popoola, and R. van Olst, “A survey on dynamic spectrum access via cognitive radio: taxonomy, requirement, and benefits”, Universal Journal of Communications and Networks, Vol. 2, No. 4, pp. 70-80, 2014.
  • [3] C-G. Yang, J-D. Li, and Z. Tian, “Optimal power control for cognitive radio networks under coupled interference constraints: A cooperative game-theoretical perspective”, IEEE Transactions on Vehicular Technology, Vol. 59, No. 4, pp. 1696-1706, 2010.
  • [4] Z. Tabakovic, S. Grgic, and M. Grgic, “Dynamic spectrum access in cognitive radio”, Proceedings of IEEE 51st International Symposium ELMAR, Zadar, Croatia, pp. 245-248, 28-30 September 2009.
  • [5] W. Yu, G. Ginis, and J.M. Cioffi, “Distributed multiuser power control for digital subscriber lines”, IEEE Journal on Selected Areas in Communication, Vol. 20, No. 5, pp. 1105-1115, 2002.
  • [6] E. Hosseini, and A. Falahati, “Improving water filling algorithm to power control cognitive radio system based upon traffic parameters and QoS”, Wireless Personal Communications, Vol. 70, No. 4, pp. 1747-1759, 2013.
  • [7] Y. Xu, and X. Zhao, “Distributed power control for multiuser cognitive radio networks with quality of service and interference temperature constraints”, Wireless Communications and Mobile Computing, Vol. 15, No. 14, pp. 1773-1783, 2014.
  • [8] Y. Xu, and X. Zhao, “Optimal power allocation for multiuser underlay cognitive radio networks under QoS and interference temperature constraints, China Communications, Vol. 10, No. 10, pp. 91-100, 2013.
  • [9] B.S. Awoyemi, B.T. Maharaj, and A.S. Alfa, “Solving resource allocation problems in cognitive radio networks: a survey”, EURASIP Journal on Wireless Communications and Networking, Vol. 176, pp. 1-14, 2016.
  • [10] X, Luo, Z. He, L. Wang, W. Wang, H. Ning, J-H. Wang, and W. Zhao, “An effective Jaya algorithm for resource allocation in the cognitive-radio-networks-aided Internet of Things”, Proceedings of IEEE Conference on Internet of Things (iThings) and IEEE Green Computing and Communication (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Halifax, NS, Canada, pp. 118-125, 30 July-3 August, 2018.
  • [11] Z-Q, Luo, W. Yu, “An introduction to convex optimization for communications and ignal processing”, IEEE Journal on Selected Areas in Communications, Vol. 24, No. 8, pp. 1426-1438, 2006.
  • [12] F. Wang, and W. Wang, “Robust beam forming and power control for multiuser cognitive radio network”, Proceedings of IEEE Global Telecommunications Conference, Miami, FL, USA, 6-10 December 2010.
  • [13] X, Luo, Z. He, Z. Zhao, L. Wang, W. Wang, H. Ning, J-H. Wang, W. Zhao, and J. Zhang, “Resource allocation in the cognitive-radio-networks-aided Internet of Things for them Cyber-Physical-Social System: An efficient Jaya algorithm”, Sensors, Vol. 18, No. 11, 3649, pp. 1- 20, doi: 10.3390/s18113649.
  • [14] I. Stojanovic, I. Brajevic, P.S. Stnnimirovic, L.V. Kazakovtsev, and Z. Zdravev, “Application of heuristic and metaheuristic algorithms in solving constrained weber problem with feasible region bounded by arcs”, Mathematical Problems in Engineering, Vol. 2017, No. 2, pp. 1-14, 2017.
  • [15] W. Guo, and X. Hunag, “On coverage and capacity for disaster rea wireless networks using mobile relays”, EURASIP Journal on Wireless Communications and Networking, Vol. 2009, No. 1, 251314, pp. 1-17, 2009.
  • [16] Z. Mao, and X. Wang, “Efficient optimal and suboptimal radio resource allocation in OFDMA system”, IEEE Transactions on Wireless Communications, Vol. 7, No. 2, pp. 440-445, 2008.
  • [17] E. Driuoch, W. Ajib, and A.B. Dhaou, “A greedy spectrum sharing algorithm for cognitive radio networks, Proceedings of IEEE International Conference on Computing, Networking and Communication, Wireless Communications Symposium, Maui, Hawaii, USA, pp. 1010-1014, 2012.
  • [18] P.S. Bharathi, M. Balasarawathi, M. Jayekumar, and S. Padmapriya, “Resource allocation based on hybrid water filling algorithm for energy efficiency enhancement in cognitive radio networks”, Proceedings of IEEE International Conference on System, Computation, Automation and Networking, Pondicherry, India, 29-30 March 2019.
  • [19] P.M. Dayana, and S.D. Adline, “Dynamic power allocation for MC-CDMA system using iterative water filling algorithm”, International Journal of Engineering Science Invention, Vol. 4, No. 2, pp. 16-26, 2015.
  • [20] A.M. Wyglinski, M. Nekovee, and T. Hou, Cognitive radio communication and networks: Principles and Practice, Elsevier: Academic Press, 2009, pp. 1-29.
  • [21] C. Hus, P.L. Yeoh, and B.S. Krongold, “Successive convex approximation for rate maximasation in cooperative multiple-input-multiple-output-orthogonal frequency division multiplexing system”, IET Communications, Vol. 9, No. 14, pp. 1721-1729, 2015.
  • [22] O. Odeng, “Distributed transmit power control in cognitive radio networks using water-filling interfaced with game-theoretic learning, A Master Thesis in Electrical and Electronic Engineering, Department of Electrical and Information Engineering, University of Nairobi, Kenya, 2015.
  • [23] S. Haykin, An introduction to Analog and Digital Communications, 2nd ed., Wiley and Sons, Inc, 1989.
  • [24] N. Chengliang, L, Dongxin, Z. Tingxian, and L. Lihong, “Distributed power control algorithm based on game theory for wireless sensor network”, Journal of Systems Engineering and Electronics, Vol. 18, No. 3, pp. 622-627, 2007.
  • [25] J.J. Popoola, and R. van Olst, “Development and demonstration of graphical user interface spectrum algorithm using some wireless systems in South Africa”, Journal of Applied Science and Processing Engineering, Vol. 2, No. 2, pp. 44-63, 2015

Dynamic Power Control Strategy for Overlay Multi-Secondary Users in Cognitive Radio Environment

Year 2020, Volume: 4 Issue: 3, 119 - 129, 28.09.2020

Abstract

Current scarcity of radio spectrum availability for new wireless services and applications has made dynamic spectrum access a rich research area across the world. However, one of the problems confronting dynamic spectrum access via cognitive radio technology is optimal spectrum resource allocation in a cognitive radio environment (CRE). In proffering solution to this problem, the study presented in this paper applies the inherent capability of iterative water filling (IWF) algorithm to develop resource allocation strategies in an overlay CRE. The developed resource allocation IWF algorithm in this study was designed for multi-secondary users, consisting of two- and three-secondary users, in a CRE with an idle primary user. The developed algorithm was simulated in MATLAB® environment. The performance evaluation results of the developed resource allocation algorithm, using IWF, show that the multi-secondary users converge at different data rates. Also, the results of the performance evaluation test conducted shows that the number of the secondary users is inversely related to data rate convergence potential. Furthermore, the overall result of the study shows that effective and efficient radio spectrum resource allocation or sharing is achievable and affordable

References

  • [1] J.J. Popoola, and R. van Olst, “The performance evaluation of a spectrum sensing implementation using an automatic modulation classification detection methods with a universal software radio peripheral”, Expert Systems with Applications, Vol. 40, No. 6, pp. 2165-2173, 2013.
  • [2] J.J. Popoola, and R. van Olst, “A survey on dynamic spectrum access via cognitive radio: taxonomy, requirement, and benefits”, Universal Journal of Communications and Networks, Vol. 2, No. 4, pp. 70-80, 2014.
  • [3] C-G. Yang, J-D. Li, and Z. Tian, “Optimal power control for cognitive radio networks under coupled interference constraints: A cooperative game-theoretical perspective”, IEEE Transactions on Vehicular Technology, Vol. 59, No. 4, pp. 1696-1706, 2010.
  • [4] Z. Tabakovic, S. Grgic, and M. Grgic, “Dynamic spectrum access in cognitive radio”, Proceedings of IEEE 51st International Symposium ELMAR, Zadar, Croatia, pp. 245-248, 28-30 September 2009.
  • [5] W. Yu, G. Ginis, and J.M. Cioffi, “Distributed multiuser power control for digital subscriber lines”, IEEE Journal on Selected Areas in Communication, Vol. 20, No. 5, pp. 1105-1115, 2002.
  • [6] E. Hosseini, and A. Falahati, “Improving water filling algorithm to power control cognitive radio system based upon traffic parameters and QoS”, Wireless Personal Communications, Vol. 70, No. 4, pp. 1747-1759, 2013.
  • [7] Y. Xu, and X. Zhao, “Distributed power control for multiuser cognitive radio networks with quality of service and interference temperature constraints”, Wireless Communications and Mobile Computing, Vol. 15, No. 14, pp. 1773-1783, 2014.
  • [8] Y. Xu, and X. Zhao, “Optimal power allocation for multiuser underlay cognitive radio networks under QoS and interference temperature constraints, China Communications, Vol. 10, No. 10, pp. 91-100, 2013.
  • [9] B.S. Awoyemi, B.T. Maharaj, and A.S. Alfa, “Solving resource allocation problems in cognitive radio networks: a survey”, EURASIP Journal on Wireless Communications and Networking, Vol. 176, pp. 1-14, 2016.
  • [10] X, Luo, Z. He, L. Wang, W. Wang, H. Ning, J-H. Wang, and W. Zhao, “An effective Jaya algorithm for resource allocation in the cognitive-radio-networks-aided Internet of Things”, Proceedings of IEEE Conference on Internet of Things (iThings) and IEEE Green Computing and Communication (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Halifax, NS, Canada, pp. 118-125, 30 July-3 August, 2018.
  • [11] Z-Q, Luo, W. Yu, “An introduction to convex optimization for communications and ignal processing”, IEEE Journal on Selected Areas in Communications, Vol. 24, No. 8, pp. 1426-1438, 2006.
  • [12] F. Wang, and W. Wang, “Robust beam forming and power control for multiuser cognitive radio network”, Proceedings of IEEE Global Telecommunications Conference, Miami, FL, USA, 6-10 December 2010.
  • [13] X, Luo, Z. He, Z. Zhao, L. Wang, W. Wang, H. Ning, J-H. Wang, W. Zhao, and J. Zhang, “Resource allocation in the cognitive-radio-networks-aided Internet of Things for them Cyber-Physical-Social System: An efficient Jaya algorithm”, Sensors, Vol. 18, No. 11, 3649, pp. 1- 20, doi: 10.3390/s18113649.
  • [14] I. Stojanovic, I. Brajevic, P.S. Stnnimirovic, L.V. Kazakovtsev, and Z. Zdravev, “Application of heuristic and metaheuristic algorithms in solving constrained weber problem with feasible region bounded by arcs”, Mathematical Problems in Engineering, Vol. 2017, No. 2, pp. 1-14, 2017.
  • [15] W. Guo, and X. Hunag, “On coverage and capacity for disaster rea wireless networks using mobile relays”, EURASIP Journal on Wireless Communications and Networking, Vol. 2009, No. 1, 251314, pp. 1-17, 2009.
  • [16] Z. Mao, and X. Wang, “Efficient optimal and suboptimal radio resource allocation in OFDMA system”, IEEE Transactions on Wireless Communications, Vol. 7, No. 2, pp. 440-445, 2008.
  • [17] E. Driuoch, W. Ajib, and A.B. Dhaou, “A greedy spectrum sharing algorithm for cognitive radio networks, Proceedings of IEEE International Conference on Computing, Networking and Communication, Wireless Communications Symposium, Maui, Hawaii, USA, pp. 1010-1014, 2012.
  • [18] P.S. Bharathi, M. Balasarawathi, M. Jayekumar, and S. Padmapriya, “Resource allocation based on hybrid water filling algorithm for energy efficiency enhancement in cognitive radio networks”, Proceedings of IEEE International Conference on System, Computation, Automation and Networking, Pondicherry, India, 29-30 March 2019.
  • [19] P.M. Dayana, and S.D. Adline, “Dynamic power allocation for MC-CDMA system using iterative water filling algorithm”, International Journal of Engineering Science Invention, Vol. 4, No. 2, pp. 16-26, 2015.
  • [20] A.M. Wyglinski, M. Nekovee, and T. Hou, Cognitive radio communication and networks: Principles and Practice, Elsevier: Academic Press, 2009, pp. 1-29.
  • [21] C. Hus, P.L. Yeoh, and B.S. Krongold, “Successive convex approximation for rate maximasation in cooperative multiple-input-multiple-output-orthogonal frequency division multiplexing system”, IET Communications, Vol. 9, No. 14, pp. 1721-1729, 2015.
  • [22] O. Odeng, “Distributed transmit power control in cognitive radio networks using water-filling interfaced with game-theoretic learning, A Master Thesis in Electrical and Electronic Engineering, Department of Electrical and Information Engineering, University of Nairobi, Kenya, 2015.
  • [23] S. Haykin, An introduction to Analog and Digital Communications, 2nd ed., Wiley and Sons, Inc, 1989.
  • [24] N. Chengliang, L, Dongxin, Z. Tingxian, and L. Lihong, “Distributed power control algorithm based on game theory for wireless sensor network”, Journal of Systems Engineering and Electronics, Vol. 18, No. 3, pp. 622-627, 2007.
  • [25] J.J. Popoola, and R. van Olst, “Development and demonstration of graphical user interface spectrum algorithm using some wireless systems in South Africa”, Journal of Applied Science and Processing Engineering, Vol. 2, No. 2, pp. 44-63, 2015
There are 25 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Jide Popoola

Chinedu Olebu This is me

Publication Date September 28, 2020
Published in Issue Year 2020 Volume: 4 Issue: 3

Cite

IEEE J. Popoola and C. Olebu, “Dynamic Power Control Strategy for Overlay Multi-Secondary Users in Cognitive Radio Environment”, IJESA, vol. 4, no. 3, pp. 119–129, 2020.

ISSN 2548-1185
e-ISSN 2587-2176
Period: Quarterly
Founded: 2016
Publisher: Nisantasi University
e-mail:ilhcol@gmail.com