Efficient and Robust Spectrum Allocation Algorithm for underlay Cognitive Radio Network under imperfect Channel State Information | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 11, Volume 27, Issue 1, January 2018, Page 197-212 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2018.64531 | ||||
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| Authors | ||||
| Kyrillos Youssef1; Nagy Messiha2; Mohammed Abd-Elnaby2 | ||||
| 1Dept. of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menouf, Menoufia University, Egypt. | ||||
| 2Dept. of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menouf, Menoufia University, Egypt | ||||
| Abstract | ||||
| Designing efficient radio resource allocation scheme for Underlay Cognitive radio (CR) has drawn many interests. Most of the existing works assumed perfect channel state information (CSI). In this paper, a spectrum allocation algorithm for underlay multi-user orthogonal frequency division multiplexing (MU-OFDM) based cognitive radio systems under imperfect CSI is proposed to maximize the throughput performance. During the first phase, the receiver estimates the channel and sends a feedback to the transmitter. During the second phase, the proposed scheme efficiently distributes the available subcarriers among cognitive users to maximize the cognitive network throughput while preserving the QoS of the primary users. The proposed algorithm considers the conventional Interference Power Constraint (IPC) to preserve the QoS of the primary users. The simulations results demonstrate that the proposed scheme significantly outperform the conventional IPC based scheme in terms of the achieved CR network throughput and the robustness against estimation error of CSI. Also, it is shown that the achieved CR throughput by the proposed algorithm is enhanced by 102% than that of the conventional IPC based schemes. | ||||
| Keywords | ||||
| Channel state information (CSI); cognitive radio; Interference Constraints; OFDM; spectrum sharing; subcarrier allocation | ||||
| References | ||||
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xt-stroke-width: 0px; "> [1] X. Kang, H. K. Garg, Y. Liang, and R. Zhang, '' Optimal power allocation for OFDM-based cognitive radio with new primary transmission protection criteria," IEEE Transactions on Wireless Communications, vol. 9, no. 6, pp. 2066-2075, June 2010. [2] R. Engelman, and C. K. Abrokwah, “Spectrum policy task force,” Federal Communications Commission, ET Docket No. 02-135, Tech. Rep., Nov. 2002. [3] W. Yu and Q. Liu, "Dynamic spectrum management with interference constraint and proportional fairness," IEEE Tencon – Spring, pp. 520 – 524, 2013. [4] J. P. Hong and W. Choi, “Gains and Limits of Diversity Techniques Cognitive Radio Systems,” Journal of Communications and Networks, vol. 19, no. 2, pp. 97-104, April 2017. [5] G. Bansal, Md. J. Hossain, and V. K. Bhargava, “Adaptive power loading for OFDM-based cognitive radio systems,” in Proc. of IEEE International Conference on Communications. ICC’07, pp. 5137-5142, June 2007. [6] J. Mitola III and G. Q. Maguire Jr., “Cognitive radio: making software radios more personal,” IEEE Personal Communications, vol. 6, no. 4, pp. 13–18, 1999. [7] J. Mitola, Cognitive Radio Architecture: The Engineering Foundations of Radio XML, Wiley-Interscience, New York, NY, USA, 2006 . ebkit-text-size-adjust: auto; -webkit-[8] S. Sasipriya and R.Vigneshram , “An Overview of Cognitive Radio in 5G Wireless Communications,” IEEE International Conference on Computational Intelligence and Computing Research, pp. 1–5, 2016. [9] T. A. Le and K. Navaie, “On the Interference Tolerance of the Primary System in Cognitive Radio Networks,” IEEE Wireless Communications Letters, pp. 1–4, 2015. [10] G. Sharma and R. Sharma, “A Review on recent advances in spectrum sensing, Energy Efficiency and Security Threats in Cognitive Radio Networks,” International Conference on Microwave, Optical and Communications Engineering, IIT Bhubaneswar, India, pp. 1–4, December 2015. [11] M. G. Kibria, F. Yuan and F. Kojima, “Feedback Bits Allocation for Interference Minimization in Cognitive Radio Communications,” IEEE Wireless Communications Letters, pp. 1–4, 2015. [12] Y. Li, Z. Chen, and Y. Gong, “Optimal Power Allocation for Coordinated Transmission in Cognitive Radio Networks,” IEEE Trans. Wireless Commun., pp. 1–5, 2015. [13] J. Zou, Q. Wu, H. Xiong, and C. W. Chen, “Dynamic Spectrum Access and Power Allocation for Cooperative Cognitive Radio Networks,” IEEE Transactions on Signal Processing, pp. 1–33,2015. [14] Y. Xu, X. Zhao and F. Hu, “Interference minimization based power allocation for Cognitive radio networks with imperfect spectrum sensing,” IEEE Wireless Communications and Networking Conference, pp. 1–6, 2016. [15] G. Bansal, J. Hossain, V. K. Bhargava, and T. Le_Ngoc, “Subcarrier and power allocation for OFDMA_based cognitive radio systems with joint overlay and underlay spectrum access mechanism,” IEEE Trans. Veh. Tech_ nol., 62, pp. 1111–1122, 2013. [16] R. K. Jangir, “Power Allocation Schemes for OFDM-Based Cognitive Radio Networks,” RAECS UIET Panjab University Chandigarh, pp. 1-6, December 2015. [17] A. Sultana, L. Zhao, and X. Fernando, “Power Allocation using Geometric Water Filling for OFDM-based Cognitive Radio Networks,” IEEE, pp. 1–5, 2016. [18] A.Ahmad, S. Ahmad, M. H. Rehmani and N. Hassan, “A Survey on Radio Resource Allocation in Cognitive Radio Sensor Networks”, IEEE Communications Surveys & Tutorials, pp. 1-32, 2015. [19] R. Masmoudi , E. V. Belmega , I. Fijalkow, and N. Sellami, “Joint Scheduling and Power Allocation in Cognitive Radio Systems IEEE ICC, pp. 1- 6, 2015. [20] E. Bedeer, O. A. Dobre, M. H. Ahmed, and K. E. Baddour, " A novel algorithm for rate/power allocation in OFDM-based cognitive radio systems with statistical interference constraints," Signal processing for communications ; "> symposium, IEEE Global Communications Conference (GLOBECOM), pp. 3504-3509, 2013. [21] R. Bouraoui and H. Besbes, "Dynamic resource allocation for cognitive OFDMA networks based on “two witnesses rule” for cooperative spectrum sensing," IEEE 22nd International Symposium on personal, Indoor and mobile radio communications, pp. 359-363, 2013. [22] A. Sabbah and M. Ibnkahla, "Optimizing dynamic spectrum allocation for cognitive radio networks using hybrid access scheme," IEEE Wireless Communications and Networking Conference (WCNC), pp. 1-6, 2016. | ||||
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