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Chaotic Equilibrium Optimization Algorithm Based Cooperative Spectrum Sensing and Energy Efficient Cognitive Radio Networks


Affiliations
1 Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, Sharnbasva University, India
     

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The need for wireless communication in the present and the future is for green communication. The cognitive radio network must meet the requirements for green communication in order to be the next-generation communication network. So improving energy efficiency is a must for the development of cognitive radio networks. However, sensor performance must be reduced in order to improve energy efficiency. In order to consider the two key indicators of sensing performance and energy efficiency, this research suggests a Chaotic Equilibrium Optimization (CEO) method that may effectively boost energy efficiency while enhancing spectrum sensing performance. The algorithm first learns the initial reliability value of the nodes by training, sorts them based on highest reliability, selects an even number of nodes with highest reliability, divides the chosen nodes into two groups, and then alternates the operation of the two groups of nodes. While they wait for additional instructions from the fusion center, the other nodes that are not now participating in cooperative spectrum sensing are in a state of silence. Experimental demonstrations are effectuated and analyzed the performances of the performances of the proposed work. The proposed work effectively senses the spectrum than the other approaches.

Keywords

Cognitive Radio, Spectrum Sensing, Chaotic Equilibrium Optimization, Energy Efficiency.
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  • J. Yuan, and E.G. Larsson, “Intelligent Reflecting Surface-Assisted Cognitive Radio System”, IEEE Transactions on Communications, Vol. 69, No. 1, pp. 675-687, 2020.

  • R. Alghamdi, A. Shams and N. Saeed, “Intelligent Surfaces for 6G Wireless Networks: A Survey of Optimization and Performance Analysis Techniques”, IEEE Access, Vol. 8, pp. 202795-202818, 2020.

  • M.M. Vijay and D. Shalini Punithavathani, “A Memory-Efficient Adaptive Optimal Binary Search Tree Architecture for IPV6 Lookup Address”, Mobile Computing and Sustainable Informatics, Vol. 2022, pp. 749-764, 2022.

  • M. Singh, N. Kumar and A. Garg, “Deep-Learning-Based Blockchain Framework for Secure Software-Defined Industrial Networks”, IEEE Transactions on Industrial Informatics, Vol. 17, No. 1, pp. 606-616, 2020.

  • C. Saju and T. Jarin, “Modeling and Control of a Hybrid Electric Vehicle to Optimize System Performance for Fuel Efficiency”, Sustainable Energy Technologies and Assessments, Vol. 52, pp. 1-12, 2022.

  • B. Soni and M. Lopez Benítez, “Long Short-Term Memory based Spectrum Sensing Scheme for Cognitive Radio using Primary Activity Statistics”, IEEE Access, Vol. 8, pp. 97437-97451, 2022.

  • W. Miao and H.V. Poor, “DC Arc-Fault Detection based on Empirical Mode Decomposition of Arc Signatures and Support Vector Machine”, IEEE Sensors Journal, Vol. 21, No. 5, pp. 7024-7033, 2020.

  • H. Golpira, B. Francois and H. Bevrani, “Optimal Energy Storage System-Based Virtual Inertia Placement: A Frequency Stability Point of View”, IEEE Transactions on Power Systems, Vol. 35, No. 6, pp. 4824-4835, 2020.

  • H. Kaschel and M.J.F.G. Garcia, “Energy-Efficient Cooperative Spectrum Sensing Based on Stochastic Programming in Dynamic Cognitive Radio Sensor Networks”, IEEE Access, Vol. 9, pp. 720-732, 2020.

  • J. Hu and L. Gou, “Energy-Efficient Cooperative Spectrum Sensing in Cognitive Satellite Terrestrial Networks”, IEEE Access, Vol. 8, pp. 161396-161405, 2020.

  • R. Wan, L. Hu and H. Wang, “Energy-Efficient Cooperative Spectrum Sensing Scheme based on Spatial Correlation for Cognitive Internet of Things”, IEEE Access, Vol. 8, pp. 139501-139511, 2020.

  • W. Yin and H. Chen, “Decision-Driven Time-Adaptive Spectrum Sensing in Cognitive Radio Networks”, IEEE Transactions on Wireless Communications, Vol. 19, No. 4, pp. 2756-2769, 2020.

  • H. Lin and Y. Liu, “Soft Decision Cooperative Spectrum Sensing with Entropy Weight Method for cognitive radio sensor networks. IEEE Access, 8, pp.109000-109008, 2020.

  • H. Ding, Y. Ma and Y. Fang, “Energy-Efficient Channel Switching in Cognitive Radio Networks: A Reinforcement Learning Approach”, IEEE Transactions on Vehicular Technology, Vol. 69, No. 10, pp. 12359-12362, 2020.

  • M. Awasthi, M.J. Nigam and V. Kumar, “Optimal Sensing and Transmission of Energy Efficient Cognitive Radio Networks”, Wireless Personal Communications, Vol. 111, No. 2, pp. 1283-1294, 2020.

  • J. Bala Vishnu and M.A. Bhagyaveni, “Energy Efficient Cognitive Radio Sensor Networks with Team-based Hybrid Sensing”, Wireless Personal Communications, Vol. 111, No. 2, pp. 929-945, 2020.

  • A. Faramarzi, and Seyedali Mirjalili, “Equilibrium Optimizer: A Novel Optimization Algorithm”, Knowledge-Based Systems, Vol. 191, pp. 1-14, 2020.

  • M.M. Vijay and D. Shalini Punithavathani, “Implementation of Memory-Efficient Linear Pipelined IPv6 Lookup and its Significance in Smart Cities”, Computers and Electrical Engineering, Vol. 67, pp. 1-14, 2018.

  • Tangsen Huang, Xiang Dong Yin and Xiao Wu Li, “Energy-Efficient and Intelligent Cooperative Spectrum Sensing Algorithm in Cognitive Radio Networks”, International Journal of Distributed Sensor Networks, Vol. 18, No. 9, pp. 1-12, 2022.


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  • Chaotic Equilibrium Optimization Algorithm Based Cooperative Spectrum Sensing and Energy Efficient Cognitive Radio Networks

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Authors

Praveen Hipparge
Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, Sharnbasva University, India
Shivkumar S. Jawaligi
Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, Sharnbasva University, India

Abstract


The need for wireless communication in the present and the future is for green communication. The cognitive radio network must meet the requirements for green communication in order to be the next-generation communication network. So improving energy efficiency is a must for the development of cognitive radio networks. However, sensor performance must be reduced in order to improve energy efficiency. In order to consider the two key indicators of sensing performance and energy efficiency, this research suggests a Chaotic Equilibrium Optimization (CEO) method that may effectively boost energy efficiency while enhancing spectrum sensing performance. The algorithm first learns the initial reliability value of the nodes by training, sorts them based on highest reliability, selects an even number of nodes with highest reliability, divides the chosen nodes into two groups, and then alternates the operation of the two groups of nodes. While they wait for additional instructions from the fusion center, the other nodes that are not now participating in cooperative spectrum sensing are in a state of silence. Experimental demonstrations are effectuated and analyzed the performances of the performances of the proposed work. The proposed work effectively senses the spectrum than the other approaches.

Keywords


Cognitive Radio, Spectrum Sensing, Chaotic Equilibrium Optimization, Energy Efficiency.

References