Optimizing the Capacity of Cognitive Radio Networks with Power Control and Variable Spectrum Allocation

  • 1 LITIO Laboratory, Department of computer science, University of , Oran, Algeria
  • 2 Ahmed Ben Bella BP 1524El M'nouer, , Oran, Algeria


Cognitive Radio Networks (CRN) were introduced as a means to more efficiently reuse the licensed radio frequency spectrum. One of their salient features is the ability of unlicensed nodes to dynamically adapt their radio parameters according to their needs. This paper investigates the resource allocation problem in CRN by jointly considering power control and bandwidth for a set of secondary users (SU) transmitting simultaneously with a set of licensed users (PU), which transmissions must remain unaltered. The proposed allocation scheme is based on a Genetic Algorithm (GA) where the chromosome's genes represent the reconfigurable interface radio parameters, by defining genetic operations the GA is empowered to find a set of radio parameters that maximize the overall network capacity and under the physical interference model enforced to the transmissions of both PU’s and SU’s. The numerical results illustrate the prominent effect of adjusting jointly multiple radio parameters on the network capacity.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 1. Ashrafinia, S., Pareek, U., Naeem, M. and Lee, D. (2011) Biogeography-based optimization for joint relay assignment and power allocation in cognitive radio systems. IEEE Symposium on Swarm Intelligence, pp. 1-8.

  • 2. Benaya, A.M., Rosas, A.A. and Shokair, M. (2016) Maximization of minimal throughput using genetic algorithm in MIMO underlay cognitive radio networks. 33rd National Radio Science Conference (NRSC), pp. 141-148.

  • 3. Bhardwaj, P., Panwar, A., Ozdemir, O., Masazade, E., Kasperovich, I., Drozd, A.L., Mohan, C.K., Varshney, P.K.. (2016) Enhanced Dynamic Spectrum Access in Multiband Cognitive Radio Networks via Optimized Resource Allocation. IEEE Transactions on Wireless Communications, 15(12), pp.8093-8106.

  • 4. Buddhikot, M. (2007) Understanding dynamic spectrum access: Models, taxonomy, IEEE DySPAN, pp.649-663.

  • 5. Clemens, N. and Rose, C. (2005) Intelligent power allocation strategies in an unlicensed spectrum. First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 1, pp.37-42.

  • 6. FCC. Spectrum Policy Task Force. (2002) Report of the spectrum efficiency working group (Tech. Rep. ET Docket No. 02-135). Fed. Commun. Comm. Washington, DC: Fed. Commun. Comm.

  • 7. Goldberg, D. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Reading MA: Addison-Wesley.

  • 8. He, C., Feng, Z., Zhang, Q., Zhang, Z. and Xiao, H. (2010) A Joint Relay Selection, Spectrum Allocation and Rate Control Scheme in Relay-Assisted Cognitive Radio System. 2010 IEEE 72nd Vehicular Technology Conference, pp. 1-5.

  • 9. Holland, J.H. (1975) Adaptation in Natural and Artificial Systems. Ann Arbor MI: The University of Michigan Press.

  • 10. Mishra, A., Rozner, E., Banerjee, S. and Arbaugh, W. (2005) Exploiting Partially Overlapping Channels in Wireless Networks: Turning a Peril into an Advantage. Proceedings of the 5th ACM SIGCOMM Conference on Internet Measurement, p. 29.

  • 11. Mitola, J. and Maguire, G. Q. (1999) Cognitive radio: Making software radios. IEEE Pers. Commun, 6(4), pp.13-18.

  • 12. Naeem, M., Anpalagan, A., Jaseemuddin, M. and LEE, D.C. (2014) Resource Allocation Techniques in Cooperative Cognitive Radio Networks. IEEE Communications Surveys Tutorials, 16(2), 729-744.

  • 13. Salehinejad, H., Talebi, S. and Pouladi, F. (2010) A metaheuristic approach to spectrum assignment for opportunistic spectrum accesS. 2010 17th International Conference on Telecommunications, pp. 234-238.

  • 14. Tao, S. and Zhisheng, N. (2003) Uplink capacity optimization by power allocation for multimedia CDMA networks with imperfect power control. IEEE Journal on Selected Areas in Communications, 21(10), pp.1585-1594.

  • 15. Wang, Q., Zhang, H., Chen, Q. and Fan, M. (2015) A modified genetic algorithm for resource allocation in OFDM-based cognitive radio systems. 2015 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp.11-15.

  • 16. Wang, W., Lv, T., Ren, Z., Gao, L. and Liu, W. (2009) A Novel Spectrum Sharing Algorithm Based on the Throughput in Cognitive Radio Networks. 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing, pp.1-4.

  • 17. Yousefvand, M., Ansari, N. and Khorsand, S. (2015) Maximizing Network Capacity of Cognitive Radio Networks by Capacity-Aware Spectrum Allocation. IEEE Transactions on Wireless Communications, 14(9), pp.5058-5067.


Journal + Issues