Generic placeholder image

International Journal of Sensors, Wireless Communications and Control

Editor-in-Chief

ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

Research Article

Optimization of a Novel Nakagami-m Fading Affected Multiuser Cognitive Radio System Using a New Hybrid DE/BBO/SA

Author(s): Kiranjot Kaur*, Munish Rattan and Manjeet S. Patterh

Volume 10, Issue 6, 2020

Page: [976 - 988] Pages: 13

DOI: 10.2174/2210327910666200207115638

Price: $65

Abstract

Background and Objective: This communication introduces a multiple secondary user (SU) cognitive radio (CR) system in a dynamic fading environment, specifically Nakagami-m fading. The transmission parameters of the CR system are optimized to turn it into an optimal design.

Methods: For this purpose, a new hybrid of differential evolution, biogeography-based optimization with simulated annealing, namely DE/BBO/SA is proposed. The suggested method searches the best CR parameter values while accomplishing general communication objectives. Fitness functions of these objectives are modified to include fading as well as to accommodate multiple carriers.

Results and Conclusion: DE/BBO/SA generated results in terms of optimized parameters, fitness core and values of the objectives are compared with the ones acquired by other available techniques in the literature to show the efficacy of DE/BBO/SA in cognitive radio optimization. The performances are further validated by conducting a non-parametric statistical test to prove the use of proposed technique for solving CR problem.

Keywords: Cognitive Radio, designing, DE/BBO, Nakagami-m fading, optimization, SA, statistical test.

« Previous
Graphical Abstract

[1]
Mitola J, Maguire G. Cognitive radio: Making the software radios more personal. IEEE Pers Commun 1999; 6: 13-8.
[http://dx.doi.org/10.1109/98.788210]
[2]
Haykin S. Cognitive radio: Brain-empowered wireless communications. IEEE J Sel Areas Comm 2005; 23: 201-20.
[http://dx.doi.org/10.1109/JSAC.2004.839380]
[3]
Newman TR. Multiple objective fitness functions for cognitive radio adaptationDoctoral dissertation, University of Kansas: Kansas 2008.
[4]
Newman T, Rajbanshi R, Wyglinski A, Minden G. Population adaptation for genetic algorithm-based cognitive radios. Mob Netw Appl 2007; 13: 279-84.
[5]
Newman T, Barker B, Wyglinski A, Agah A. Cognitive engine implementation for wireless multicarrier transceivers. Wirel Commun Mob Comput 2006; 7: 1129-42.
[http://dx.doi.org/10.1002/wcm.486]
[6]
Khamy SE, Aboul-Dhab MA, Attia MM. A hybrid of particle swarm optimization and genetic algorithm for multicarrier cognitive radio. Natl Radio Sci Conf New Cairo Egypt 2009; pp. 1-7.
[7]
El-Saleh AA, Ismail M, Ali MA, Ng J. Development of a cognitive radio decision engine using multi-objective hybrid genetic algorithm.IEEE 9th Malaysia International Conference on Communications (MICC) 2009; pp. 343-347.
[http://dx.doi.org/10.1109/MICC.2009.5431527]
[8]
Zhao Z, Xu S, Zheng S, Shang J. Cognitive radio adaptation using particle swarm optimization. Wirel Commun Mob Comput 2009; 9: 875-81.
[http://dx.doi.org/10.1002/wcm.633]
[9]
Chen S, Newman TR, Evans JB, Wyglinski AM. Genetic algorithm-based optimization for cognitive radio networks. IEEE Sarnoff Symp 2010; pp1-6.
[http://dx.doi.org/10.1109/SARNOF.2010.5469780]
[10]
Pradhan PM, Panda G. Pareto optimization of cognitive radio parameters using multiobjective evolutionary algorithms and fuzzy decision making. Swarm Evol Comput 2011; 7: 7-20.
[http://dx.doi.org/10.1016/j.swevo.2012.07.001]
[11]
Pradhan PM. Design of cognitive radio engine using artificial bee colony algorithm. International Conference on Energy, Automation and Signal 2011; pp 1-4.
[http://dx.doi.org/10.1109/ICEAS.2011.6147139]
[12]
Pradhan PM, Panda G. Comparative performance analysis of evolutionary algorithm based parameter optimization in cognitive radio engine: A survey. Ad Hoc Netw 2014; 17: 129-46.
[http://dx.doi.org/10.1016/j.adhoc.2014.01.010]
[13]
Zhao N, Li S, Wu Z. Cognitive radio engine design based on ant colony optimization. Wirel Pers Commun 2012; 65: 15-24.
[http://dx.doi.org/10.1007/s11277-011-0225-7]
[14]
Yu Y, Tan X, Yin C, Ma L. Cognitive decision engine based on binary chaotic particle swarm optimization. J Harbin Inst Technol 2014; 46: 8-13.
[15]
Tan X, Zhang H, Hu J. A hybrid architecture of cognitive decision engine based on particle swarm optimization algorithms and case database. Ann Telecommun 2014; 69: 593-605.
[http://dx.doi.org/10.1007/s12243-013-0417-0]
[16]
You X, He X, Han X, Wu C, Jiang H. A novel cognitive radio decision engine based on chaotic quantum bee colony algorithm. J Inf Comput Sci 2015; 12: 2093-106.
[http://dx.doi.org/10.12733/jics20105768]
[17]
Rana V, Mundra PS. Simulation of QoS parameters in cognitive radio system using SMO algorithm. 2017 Intl Conf Inventive Commu Comput Technol (ICICCT) 2017; pp 292-297.
[http://dx.doi.org/10.1109/ICICCT.2017.7975206]
[18]
You X, He X, Han X. A novel Solution to the cognitive radio decision engine based on improved multi-objective artificial bee colony algorithm and fuzzy Reasoning. Intell Auto Soft Compt 2017; 23: 643-51.
[http://dx.doi.org/10.1080/10798587.2017.1316081]
[19]
Chen W, Li T, Yang T. Intelligent control of cognitive radio parameter adaption: Using evolutionary multi-objective algorithm based on user preference. Ad Hoc Netw 2015; 26: 3-16.
[http://dx.doi.org/10.1016/j.adhoc.2014.09.006]
[20]
Paraskevopoulos P, Dallas PI, Siakavara K, Goudos SK. Cognitive radio engine design for IoT using real-coded biogeography-based optimization and fuzzy decision making. Wirel Pers Commun 2017; 97: 1813-33.
[http://dx.doi.org/10.1007/s11277-017-4646-9]
[21]
Singh G, Rattan M, Gill SS, Mittal N. Hybridization of water wave optimization and sequential quadratic programming for cognitive radio system. Soft Compt 2018; pp. 1-21.
[22]
Kaur K, Rattan M, Patterh MS. Optimization of cognitive radio system using simulated annealing. Wirel Pers Commun 2013; 71: 1283-9.
[http://dx.doi.org/10.1007/s11277-012-0874-1]
[23]
Kaur K, Rattan M, Patterh MS. Biogeography based optimization of cognitive radio system. Int J Electron 2014; 101: 24-36.
[http://dx.doi.org/10.1080/00207217.2013.769183]
[24]
Kaur K, Rattan M, Patterh MS. Bat algorithm based optimization of multiuser cognitive radio system over Nakagami-m fading channels. In: International Conference on Soft Computing Applications in Wireless Communication 2017. 43-50.
[25]
Kaur K, Rattan M, Patterh MS. Cuckoo search based optimization of multiuser cognitive radio system under the effect of shadowing. Wirel Pers Commun 2018; 99: 1217-30.
[http://dx.doi.org/10.1007/s11277-017-5181-4]
[26]
Kaur K, Rattan M, Patterh MS. Cognitive radio design optimization over fading channels using PSO, GSA and hybrid PSOGSA. In: Second International Conference on Intelligent Computing and Control Systems (ICICCS) 2018. 1700-6.
[27]
Zhang S, Hafid AS, Zhao H, Wang S. Impact of heterogeneous fading channels in power limited cognitive radio networks. IEEE Trans Cognitive Commun Networking 2018; 4: 1-14.
[http://dx.doi.org/10.1109/TCCN.2017.2779858]
[28]
Sharma PK, Solanki S, Upadhyay PK. Outage analysis of cognitive opportunistic relay networks with direct link in Nakagami-m Fading. IEEE Commun Lett 2015; 19: 875-8.
[http://dx.doi.org/10.1109/LCOMM.2015.2413874]
[29]
Zhou Z, Beaulieu NC, Li Z, Si J, Qi P. Energy-efficient optimal power allocation for fading cognitive radio channels: ergodic capacity outage capacity and minimum-rate capacity. IEEE Trans Wirel Commun 2016; 15: 2741-55.
[http://dx.doi.org/10.1109/TWC.2015.2509069]
[30]
Hussein JA, Boussakta S, Ikki SS. Performance study of a UCRN over nakagami- m fading channels in the presence of CCI. IEEE Trans Cognitive Commun Networking 2017; 3: 752-65.
[http://dx.doi.org/10.1109/TCCN.2017.2768061]
[31]
Bhattacharjee S, Acharya T, Bhattacharya U. On green multicasting over cognitive radio fading channels. IEEE Trans Vehicular Technol 2018; 67: 5491-5.
[http://dx.doi.org/10.1109/TVT.2018.2818189]
[32]
Storn R, Price K. Differential evolution–a simple and efficient adaptive scheme for global optimization over continuous spaces: technical report TR-95-012. Intl Comp Sci Berkeley California 1995.
[33]
Simon D. Biogeography-based optimization. IEEE Trans Evol Comput 2008; 12: 702-13.
[http://dx.doi.org/10.1109/TEVC.2008.919004]
[34]
Kirkpatrick S, Gelatt CD Jr, Vecchi MP. Optimization by simulated annealing. Sci 1983; 220(4598): 671-80.
[http://dx.doi.org/10.1126/science.220.4598.671 PMID: 17813860]
[35]
Bhattacharya A, Chattopadhyay PK. Hybrid differential evolution with biogeography-based optimization for solution of economic load dispatch. IEEE Trans Power Syst 2010; 25: 1955-64.
[http://dx.doi.org/10.1109/TPWRS.2010.2043270]
[36]
Molga M, Smutnicki C. Test functions for optimization needs. Rober Marks 2005; 101: 48.www.robertmarks.org/Classes/ENGR5358/
[37]
Stevenson C, Chouinard G, Zhongding L, Wendong H, Shellhammer SJ, Caldwell W. IEEE 802.22: The first cognitive radio wireless regional area network standard. IEEE Commun Mag 2009; 47: 130-8.
[http://dx.doi.org/10.1109/MCOM.2009.4752688]
[38]
Farraj AK, Ekin S. Performance of cognitive radios in dynamic fading channels under primary outage constraint. Wirel Pers Commun 2013; 73: 637-49.
[http://dx.doi.org/10.1007/s11277-013-1207-8]
[39]
Gradshteyn S, Ryzhik IM, Jeffrey A. Special functions in table of integrals series and products. 5th ed. California: Academic Press 1994.
[40]
Friedman M. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 1937; 32: 675-701.
[http://dx.doi.org/10.1080/01621459.1937.10503522]
[41]
Friedman M. A comparison of alternative tests of significance for the problem of m rankings. Ann Math Stat 1940; 11: 86-92.
[http://dx.doi.org/10.1214/aoms/1177731944]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy