Share This Article:

A Self-Optimization of the Dynamic Resource Management Based on the Cognitive Radio

Abstract Full-Text HTML Download Download as PDF (Size:300KB) PP. 87-92
DOI: 10.4236/wet.2011.22012    4,854 Downloads   8,785 Views   Citations
Author(s)    Leave a comment

ABSTRACT

This paper describes a novel self-optimized approach for resource management based on the cognitive radio in the cellular networks. The cognitive radio techniques offer several features like autonomy, sensing and negotiation. The use of cognitive radio approach gives greater autonomy to the base stations in the cellular networks. This autonomy allows an increase in flexibility to deal with new situations in the traffic load. The negotiation strategy is used to avoid conflicts in the resource allocation. The goal of the cognitive radio scheme is to achieve a high degree of resource usage and a low rate of call blocking in the cellular systems.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

J. Raiyn, "A Self-Optimization of the Dynamic Resource Management Based on the Cognitive Radio," Wireless Engineering and Technology, Vol. 2 No. 2, 2011, pp. 87-92. doi: 10.4236/wet.2011.22012.

References

[1] S. K. S. Gupta and P. K. Srimani, “UpdateSearch: A New Dynamic Channel Allocation Scheme for Mobile Networks That can Adjust to System Loads,” The Journal of Supercomputing, Vol. 17, No. 1, August 2000, pp. 47-65. doi:10.1023/A:1008119705225
[2] C. W. Leong, W. Zhuang, Y. Cheng and L. Wang, “Optimal Resource Allocation and Adaptive Call Admission Control for Voice/Data Integrated Cellular Networks,” IEEE Transactions on Vehicular Technology, Vol. 55, No. 2, March 2006, pp. 654-669. doi:10.1109/TVT.2005.858186
[3] L. Cong, et al., “Performance Analysis of Dynamic Channel Assignment Algorithms in Cellular Mobile Systems with Hand-off,” International Journal of Communication Systems, Vol. 15, No. 8, 2002, pp. 683-700. doi:10.1002/dac.558
[4] S. Anand, A. Sridharan and K. N. Sivarajan, “Performance Analysis of Channlized Cellular Systems with Dynamic Channel Allocation,” IEEE Transactions on Vehicular Technology, Vol. 52, No. 4, July 2003, pp. 847- 859. doi:10.1109/TVT.2003.814231
[5] M. Bublin, M. Kongegger and P. Slanina, “A Cost-Func- tion-Based Dynamic Channel Allocation and Its Limits,” IEEE Transactions on Vehicular Technology, Vol. 56, No. 4, July 2007, pp. 2286-2295. doi:10.1109/TVT.2007.897657
[6] C. Politis, “Managing the Radio Spectrum,” IEEE Vehicular Technology Magazine, Vol. 4, No. 1, March 2009, pp. 20-26. doi:10.1109/MVT.2008.931628
[7] M. Zafer and E. Modiano, “Blocking Probability and Channel Assignment in Wireless Networks,” IEEE Trans- actions on Wireless Communications, Vol. 5, No. 4, April 2006, pp. 869-879. doi:10.1109/TWC.2006.1618936
[8] B. Friedlander and S. Scherzer, “Beamforming versus Transmit Diversity in the Downlink of a Cellular Communication System,” IEEE Transactions on Vehicular Technology, Vol. 53, No. 4, July 2004, pp. 1023-1034. doi:10.1109/TVT.2004.830980
[9] T. Ren and R. J. La, “Downlink Beamforming Algorithms with Inter-Cell Interference in Cellular Networks,” IEEE Transactions on Wireless Communications, Vol. 5, No. 10, October 2006, pp. 2814-2823.
[10] N. D. Tripathi, J. H. Reed and H. F. Vanl, “Handoff in Cellular Systems,” IEEE Personal Communications, Vol. 5, No. 6, December 1998, pp. 26-37. doi:10.1109/98.736475
[11] N. D. Tripathi, et al., “Handoff in Cellular Systems,” IEEE Personal Communications, Vol. 5, No. 6, December 1998, pp. 26-37.
[12] S. Hykin, J. D. Thomson and H. J. Reed, “Spectrum Sensing for Cognitive Radio,” Proceedings of the IEEE, Vol. 97, No. 5, May 2009, pp. 849-877. doi:10.1109/JPROC.2009.2015711
[13] A. F. Molisch, L. J. Greenstein and M. Shafi, “Propagation Issues for Cognitive Radio,” Proceedings of the IEEE, Vol. 97, No. 5, May 2009, pp.787-804. doi:10.1109/JPROC.2009.2015704
[14] Z.-P. Li, H. Yu, Y.-C. Liu and F.-Q. Liu, “An Improved Adaptive Exponential Smoothing (IAES) Model for Short-Term Travel Time Forecasting of Urban Arterial Street,” Acta Automatica Sinica, Vol. 34, No. 11, November 2008, pp. 1404-1409.
[15] R. E. Turochy and M. Asce, “Enhancing Short-Term Traffic Forecasting with Traffic Condition Information,” Journal of Transportation Engineering, Vol. 132, No. 6, June 2006, pp. 469-474. doi:10.1061/(ASCE)0733-947X(2006)132:6(469)

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.