Spectrum sensing in cognitive femtocell network based on near-field source localisation using genetic algorithm

Cognitive femtocell has emerged as a promising technique for indoor users in wireless communication systems since they opt the cognitive and self-configuration capabilities. Spectrum sensing is a non-trivial approach in cognitive radio networks. A number of methods have been used for spectrum sensing in femtocells with a cognitive engine. In this work, the authors have proposed a near field source localisation technique through which the cognitive femtocell will be able to detect the active femtocells in the licensed band and will get additional information including the range, amplitude, angle and operating frequency of a certain femtocell.

[1]  O. Weck,et al.  A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND THE GENETIC ALGORITHM , 2005 .

[2]  Y. Hua,et al.  A weighted linear prediction method for near-field source localization , 2002, IEEE Transactions on Signal Processing.

[3]  Zouhair Guennoun,et al.  Cognitive radio spectrum allocation using genetic algorithm , 2016, EURASIP J. Wirel. Commun. Netw..

[4]  T. Yucek,et al.  Spectrum Characterization for Opportunistic Cognitive Radio Systems , 2006, MILCOM 2006 - 2006 IEEE Military Communications conference.

[5]  H. Urkowitz Energy detection of unknown deterministic signals , 1967 .

[6]  Jeffrey G. Andrews,et al.  Femtocell networks: a survey , 2008, IEEE Communications Magazine.

[7]  Hüseyin Arslan,et al.  A survey of spectrum sensing algorithms for cognitive radio applications , 2009, IEEE Communications Surveys & Tutorials.

[8]  H. Tang,et al.  Some physical layer issues of wide-band cognitive radio systems , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[9]  Ijaz Mansoor Qureshi,et al.  An application of hybrid differential evolution to 3-D near field source localization , 2014, Proceedings of 2014 11th International Bhurban Conference on Applied Sciences & Technology (IBCAST) Islamabad, Pakistan, 14th - 18th January, 2014.

[10]  Ijaz Mansoor Qureshi,et al.  Joint spectrum sensing for detection of primary users using cognitive relays with evolutionary computing , 2015, IET Commun..

[11]  Alagan Anpalagan,et al.  Pattern-Search-Based Nonconvex Cooperative Sensing in Multiband Cognitive Radio Systems , 2016, IEEE Systems Journal.

[12]  Payal Mishra,et al.  Survey on Optimization Methods For Spectrum Sensing in Cognitive Radio Network , 2015 .

[13]  Zhai Xuping,et al.  Energy-detection based spectrum sensing for cognitive radio , 2007 .

[14]  Z. Liu,et al.  Spectrum sensing in cognitive radios based on enhanced energy detector , 2012, IET Commun..

[15]  Jon F. Hauris Genetic Algorithm Optimization in a Cognitive Radio for Autonomous Vehicle Communications , 2007, 2007 International Symposium on Computational Intelligence in Robotics and Automation.

[16]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[17]  Chun-tong Liu,et al.  Modeling and analyzing interference signal in a complex electromagnetic environment , 2016, EURASIP J. Wirel. Commun. Netw..

[18]  Liang Dong,et al.  Utilizing OFDM Guard Interval for Spectrum Sensing , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[19]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[20]  Ding Liu,et al.  Passive Localization of Mixed Near-Field and Far-Field Sources Using Two-stage MUSIC Algorithm , 2010, IEEE Transactions on Signal Processing.