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

Nawel Benghabrit; Mejdi Kaddour

doi:10.2478/ttj-2018-0011

Abstract

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.

References

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. 1-8.10.1109/SIS.2011.5952589
Search in Google Scholar Back to article
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. 141-148.10.1109/NRSC.2016.7450847
Search in Google Scholar Back to article
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.8093-8106.10.1109/TWC.2016.2612627
Open DOI Search in Google Scholar Back to article
4. Buddhikot, M. (2007) Understanding dynamic spectrum access: Models, taxonomy, IEEE DySPAN, pp.649-663.10.1109/DYSPAN.2007.88
Search in Google Scholar Back to article
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.37-42.10.1109/DYSPAN.2005.1542615
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
7. Goldberg, D. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Reading MA: Addison-Wesley.
Search in Google Scholar Back to article
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. 1-5.10.1109/VETECF.2010.5594299
Search in Google Scholar Back to article
9. Holland, J.H. (1975) Adaptation in Natural and Artificial Systems. Ann Arbor MI: The University of Michigan Press.
Search in Google Scholar Back to article
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. 29.10.1145/1330107.1330144
Search in Google Scholar Back to article
11. Mitola, J. and Maguire, G. Q. (1999) Cognitive radio: Making software radios. IEEE Pers. Commun, 6(4), pp.13-18.10.1109/98.788210
Open DOI Search in Google Scholar Back to article
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), 729-744.10.1109/SURV.2013.102313.00272
Search in Google Scholar Back to article
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. 234-238.10.1109/ICTEL.2010.5478746
Search in Google Scholar Back to article
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.1585-1594.10.1109/JSAC.2003.815016
Search in Google Scholar Back to article
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.11-15.10.1109/ICCWAMTIP.2015.7493897
Search in Google Scholar Back to article
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.1-4.10.1109/WICOM.2009.5303442
Search in Google Scholar Back to article
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.5058-5067.10.1109/TWC.2015.2431691
Search in Google Scholar Back to article

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

Abstract

Paradigm

My account