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Chin. Phys. B, 2016, Vol. 25(12): 128403    DOI: 10.1088/1674-1056/25/12/128403
INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY Prev   Next  

Cognitive radio adaptation for power consumption minimization using biogeography-based optimization

Pei-Han Qi(齐佩汉)1, Shi-Lian Zheng(郑仕链)1,2, Xiao-Niu Yang(杨小牛)1,2, Zhi-Jin Zhao(赵知劲)3
1. School of Telecommunications Engineering, Xidian University, Xi'an 710071, China;
2. Science and Technology on Communication Information Security Control Laboratory, Jiaxing 314033, China;
3. School of Telecommunications, Hangzhou Dianzi University, Hangzhou 310018, China
Abstract  

Adaptation is one of the key capabilities of cognitive radio, which focuses on how to adjust the radio parameters to optimize the system performance based on the knowledge of the radio environment and its capability and characteristics. In this paper, we consider the cognitive radio adaptation problem for power consumption minimization. The problem is formulated as a constrained power consumption minimization problem, and the biogeography-based optimization (BBO) is introduced to solve this optimization problem. A novel habitat suitability index (HSI) evaluation mechanism is proposed, in which both the power consumption minimization objective and the quality of services (QoS) constraints are taken into account. The results show that under different QoS requirement settings corresponding to different types of services, the algorithm can minimize power consumption while still maintaining the QoS requirements. Comparison with particle swarm optimization (PSO) and cat swarm optimization (CSO) reveals that BBO works better, especially at the early stage of the search, which means that the BBO is a better choice for real-time applications.

Keywords:  cognitive radio      power consumption      adaptation      optimization  
Received:  29 January 2015      Revised:  24 July 2016      Accepted manuscript online: 
PACS:  84.40.Ua (Telecommunications: signal transmission and processing; communication satellites)  
Fund: 

Project supported by the National Natural Science Foundation of China (Grant No. 61501356), the Fundamental Research Funds of the Ministry of Education, China (Grant No. JB160101), and the Postdoctoral Fund of Shaanxi Province, China.

Corresponding Authors:  Shi-Lian Zheng     E-mail:  lianshizheng@126.com

Cite this article: 

Pei-Han Qi(齐佩汉), Shi-Lian Zheng(郑仕链), Xiao-Niu Yang(杨小牛), Zhi-Jin Zhao(赵知劲) Cognitive radio adaptation for power consumption minimization using biogeography-based optimization 2016 Chin. Phys. B 25 128403

[1] Haykin S 2005 IEEE Journal on Selected Areas in Communications 23 201
[2] Filin S, Harada H, Murakami H and Ishizu K 2011 IEEE Commun. Mag. 49 82
[3] Rondeau T W, Rieser C J and Bostian C W 2004 Software Defined Radio Forum Technical Conference, November 11-14, 2004, Virginia, USA, p. 3
[4] Zhao Z J, Zheng S L, Shang J N and Kong X Z 2007 Acta Phys. Sin. 56 6760 (in Chinese)
[5] Zu Y X and Zhou J 2012 Chin. Phys. B 21 019501
[6] Hauris J F 2007 Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, June 20-23, 2007, Jacksonville, USA, p. 427
[7] Newman T R, Barker B A, Wyglinski A M, Agah A, Evans J B and Minden G J 2007 Wireless Communications and Mobile Computing 7 1129
[8] Baynast A D, Mähönen P and Petrova M 2008 Computer Networks 52 778
[9] Fathy R A, AbdelHafez A A and Zekry A 2013 Int. J. Comput. Appl. 64 53
[10] Zhao Z, Xu S, Zheng S and Shang J 2009 Wireless Communications and Mobile Computing 9 875
[11] Chen J C and Wen C K 2010 IEEE Commun. Lett. 14 629.
[12] Pradhan P M 2011 International Conference on Energy, Automation and Signal, December 28-30, 2011, Bhubaneswar, India, p. 1
[13] Pradhan P M and Panda G 2014 Ad Hoc Networks 17 129
[14] Clancy C, Hecker J, Stuntebeck E and O'Shea T 2007 IEEE Wireless Commun. 14 47
[15] He A, Gaeddert J, Bae K, Newman T R, Reed J H, Morales L and Pard C 2009 ACM Mobile Comput. Commun. Rev. 13 37
[16] Volos H I and Michael Buehrer R 2010 IEEE Trans. Wireless Commun. 9 2902
[17] He A, Ammanna A, Tsou T, Chen X, Datla D, Gaeddert J, Newman T R, Hasan S, Volos H I, Reed J H and Bose T 2011 J. Commun. 6 340
[18] Gür G and Alagöz F 2011 IEEE Network 25 50
[19] Cao S, Qian L, Vaman D R and Qu Q 2007 IEEE International Conference on Communications, June 24-28, 2007, Glasgow, Scotland, p. 3980
[20] Naeem M, Illanko K, Karmokar A, Anpalagan A and Jaseemuddin M 2013 IET Commun. 7 1279
[21] He A, Srikanteswara S, Reed J H, Chen X, Tranter W H, Bae K K and Sajadieh M 2008 IEEE Internatioanl Performance, Computing and Communications Conference, December 7-9, 2008, Austin, USA, p. 372
[22] He A, Srikanteswara S, Bae K K, Reed J H, Tranter W H 2010 IEEE Trans. Cosumer Electron. 56 1814
[23] Yucek T and Arslan H 2009 IEEE Communications Surveys & Tutorials 11 116
[24] Sun H, Nallanathan A, Wang C X and Chen Y 2013 IEEE Wireless Communications 20 74
[25] Simon D 2008 IEEE Transactions on Evolutionary Computation 12 702
[26] Simon D 2011 Appl. Soft Comput. 11 5652
[27] Ma H 2010 Inform. Sci. 180 3444
[28] Goldberg D 1989 Genetic Algorithms in Search, Optimization, and Machine Learning (Massachusetts:Addison-Wesley) pp.141-150
[29] Cordeiro C, Challapali K, Birru D and Sai Shankar N 2005 Proceedings of 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, November 2005, Baltimore, USA, p. 328
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