Optimisation of Cognitive Engine Design using Cultural Algorithm

Objectives: To optimize the design of cognitive radio engine using Cultural Algorithm (CA). The simulated results are compared with commonly used Evolutionary Algorithms (EA) like Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA). Methods/Statistical Analysis: Use of CA has been proposed to find a suitable fitness score under varying channel conditions over multiple iterations. Matlab has been chosen as the platform for simulating various scenarios. An attempt has also been made to optimize the time of convergence. Findings: Simulations indicate that CA emerges as a potential candidate for designing of CR Engine (CRE) for deployment of a CR Network (CRN). Using CA, the fitness score has improved as compared to other EAs. Improvements: The algorithm shows faster convergence and improves its performance with each successive iteration.

[1]  H. Ahmadi,et al.  Evolutionary algorithms for orthogonal frequency division multiplexing-based dynamic spectrum access systems , 2012, Comput. Networks.

[2]  Joseph B. Evans,et al.  Population Adaptation for Genetic Algorithm-based Cognitive Radios , 2008, Mob. Networks Appl..

[3]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[4]  Robert G. Reynolds,et al.  Knowledge-based function optimization using fuzzy cultural algorithms with evolutionary programming , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[5]  Pin Luarn,et al.  A discrete version of particle swarm optimization for flowshop scheduling problems , 2007, Comput. Oper. Res..

[6]  Anni Cai,et al.  Evolutionary Schemes for Cognitive Radio Adaptation , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[7]  Mohamad Reza Tavakoli,et al.  A new simultaneous coordinated design of STATCOM controller and power system stabilizer for power systems using cultural algorithm , 2014, 2014 IEEE International Energy Conference (ENERGYCON).

[8]  Peter A. Wieringa,et al.  PREHEP: human error probability based process unit selection , 2001, IEEE Trans. Syst. Man Cybern. Syst..

[9]  Neelam Srivastava,et al.  A survey on energy detection schemes in cognitive radios , 2016, 2016 International Conference on Emerging Trends in Electrical Electronics & Sustainable Energy Systems (ICETEESES).

[10]  C. Chung.  Knowledge-based approaches to self-adaptation in cultural algorithms , 1997 .

[11]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[12]  Chang Wook Ahn,et al.  On the practical genetic algorithms , 2005, GECCO '05.

[13]  Robert G. Reynolds,et al.  Knowledge-based self-adaptation in evolutionary programming using cultural algorithms , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[14]  Jibendu Sekhar Roy,et al.  Dynamic Spectrum Allocation in Cognitive Radio Using Particle Swarm Optimization , 2014 .

[15]  Robert G. Reynolds,et al.  Cultural algorithms: theory and applications , 1999 .

[16]  Xuesong Yan,et al.  Cultural Algorithm for Engineering Design Problems , 2012 .

[17]  Neelam Srivastava,et al.  An overview on cooperative spectrum sensing in cognitive radios , 2016, Int. J. Wirel. Mob. Comput..

[18]  Ganapati Panda,et al.  Comparative performance analysis of evolutionary algorithm based parameter optimization in cognitive radio engine: A survey , 2014, Ad Hoc Networks.

[19]  Pandurangappa C,et al.  International Journal of Emerging Technology and Advanced Engineering , 2022 .

[20]  R. Reynolds.  AN INTRODUCTION TO CULTURAL ALGORITHMS , 2008 .

[21]  Allen B. MacKenzie,et al.  Cognitive networks: adaptation and learning to achieve end-to-end performance objectives , 2006, IEEE Communications Magazine.

[22]  Shiyu Xu,et al.  Cognitive radio adaptation using particle swarm optimization , 2009, Wirel. Commun. Mob. Comput..

[23]  Shantanu Sharma,et al.  A survey on 5G: The next generation of mobile communication , 2015, Phys. Commun..

[24]  Ian F. Akyildiz,et al.  CRAHNs: Cognitive radio ad hoc networks , 2009, Ad Hoc Networks.