Cognitive Radio Spectrum Management
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32797
Cognitive Radio Spectrum Management

Authors: Swapnil Singhal, Santosh Kumar Singh

Abstract:

The emerging Cognitive Radio is combo of both the technologies i.e. Radio dynamics and software technology. It involve wireless system with efficient coding, designing, and making them artificial intelligent to take the decision according to the surrounding environment and adopt themselves accordingly, so as to deliver the best QoS. This is the breakthrough from fixed hardware and fixed utilization of the spectrum. This software-defined approach of research is centralized at user-definition and application driven model, various software method are used for the optimization of the wireless communication. This paper focused on the Spectrum allocation technique using genetic algorithm GA to evolve radio, represented by chromosomes. The chromosomes gene represents the adjustable parameters in given radio and by using GA, evolving over the generations, the optimized set of parameters are evolved, as per the requirement of user and availability of the spectrum, in our prototype the gene consist of 6 different parameters, and the best set of parameters are evolved according to the application need and availability of the spectrum holes and thus maintaining best QoS for user, simultaneously maintaining licensed user rights. The analyzing tool Matlab is used for the performance of the prototype.

Keywords: ASDR, Cognitive Radio, QoS, Spectrum.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1099322

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2013

References:


[1] Joseph Mitola III, Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio, PhD dissertation, Royal Institute of Technology (KTH) Stockholm, Sweden, 8 May, 2000
[2] S. Haykin, “Cognitive Radio: Brain-empowered wireless communications” IEEE Journal on Selected areas in Communications, vol. 23, no. 2, pp. 201–220, February 2005.
[3] Holland, J.H., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975.
[4] Schaffer, J.D. Multiple Objective optimization with vector evaluated genetic algorithms.in International Conference on Genetic Algorithm and their applications. 1985.
[5] S. Kandeepan et al., Project Report-’D2.1.1:Spectrum Sensing and Monitoring, EUWB Integrated Project, European Commission funded project (EC: FP7-ICT-215669), May 2009, ,http://www.euwb.eu
[6] The practical handbook of genetic algorithms, applications / edited by Lance D. Chambers. 2nd ed.p. cm. Includes bibliographical references and index. ISBN 1-58488-2409-9 (alk. paper)1. Genetic algorithms. I. Chambers, Lance.QA402.5 .P72 2000 519.7—dc21
[7] A Fast and Elitist Multiobjective Genetic Algorithm: NSGAIIKalyanmoy Deb, Associate Member, IEEE, AmritPratap, Sameer Agarwal, and T. Meyarivan
[8] D. Goldfarb and S. Ma, “Convergence of fixed point continuation algorithms for matrix rank minimization,” Technical Report, Department of IEOR, Columbia University, 2009.
[9] http://arxiv.org/abs/1101.4445
[10] S. Kandeepan et al., “Spectrum Sensing for Cognitive Radios with Primary User Transmission Statistics: Considering Linear Frequency Sweeping”, To Appear on EURASIP-JWCN, Special Issue on DSA: From Concept to Implementation,2010
[11] H. Kim and K. G. Shin, “Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks,” IEEE Trans. OnMobile Computing, vol. 7, no. 5, pp. 533–545, May 2008
[12] IEEE 802.11Working Group, IEEE P802.11n/D1.0 Draft Amendment to Standard for Information Technology-Telecommunications and Information Exchange between Systems-Local and Metropolitan Networks-Specific Requirements-Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Enhancements for Higher Throughput, March 2006.
[13] M. Fornasier and H. Rauhut,“Recovery algorithms for vector-valued data with joint sparsity constraints,” SIAM Journal on Numerical Analysis, vol. 46, no. 2, pp. 577–613, March 2008.
[14] Z. Tian, “Compressed wideband sensing in cooperative cognitive radio networks,” in Proc. of IEEE GLOBAL Communications Conference (GLOBECOM’08), pp. 1–5, New Orleans, USA, December 2008
[15] K. Deb, Multiobjective Optimization Using Evolutionary Algorithms. Chichester, U.K.: Wiley, 2001.
[16] Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation, 6(2):182{197
[17] Multi-objective optimization using genetic algorithms: A tutorial Abdullah Konaka, David W. Coitb, Alice E. Smithc
[18] What is a Spectrum holes and what does it take to recognize one: R. tandra; S.M Mishra; a. sahai.