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.
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:69082", author = "Swapnil Singhal and Santosh Kumar Singh", title = "Cognitive Radio Spectrum Management", 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.", volume = "8", number = "12", pages = "1922-5", }
