Artificial Neural Networks for Cognitive Radio Network: A Survey
Authors: Vishnu Pratap Singh Kirar
The main aim of a communication system is to achieve maximum performance. In Cognitive Radio any user or transceiver has ability to sense best suitable channel, while channel is not in use. It means an unlicensed user can share the spectrum of a licensed user without any interference. Though, the spectrum sensing consumes a large amount of energy and it can reduce by applying various artificial intelligent methods for determining proper spectrum holes. It also increases the efficiency of Cognitive Radio Network (CRN). In this survey paper we discuss the use of different learning models and implementation of Artificial Neural Network (ANN) to increase the learning and decision making capacity of CRN without affecting bandwidth, cost and signal rate.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1098958Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3610
 J. Mitola, “Cognitive Radio: An Integrated Agent Architecture for Software define Radio,” Ph.D. dissertation, Royal Institute of Technology (KTH), Sweden, 2000.
 S. Haykin, "Cognitive Radio: Brain-Empowered Wireless Communications," IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201-220, February 2005.
 D. Cabric and R. W. Brodersen, "Physical Layer Design Issues Unique to Cognitive Radio Systems," in Proc. of PIMRC-2005, pp. 759-763, September 2005.
 M. Venkatesan, A. V. Kulkarni, “Soft Computing based Learning for Cognitive Radio,” international journal on Recent Trends in Engineering and Technology, vol. 10, issue 1, pp. 112-119, January 2014.
 K. Burse, A. Mishra, A. Somkuwar, “Convergence Analysis of Complex Valued Multiplicative Neural Network for various Activation Functions,” IEEE International Conference on Computational Intelligence and Communication System (CICN 2011), pp. 279-282, October 2011.
 V. P. S. Kirar, K. Burse, R. N. Yadav, S. C. Srivastav, “A Compact Pi Network for Reducing Bit Error Rate in Dispersive FIR Channel Noise Model,” Proceedings of World Academy of Science, Engineering and Technology,” vol. 38, pp. 235-238, Ferbuary 2009.
 D.E. Rumelhart, G.E. Hinton and R.J. Williams, "Learning representations by back -propagating errors," Nature (London), 323, 533-536, 1986.
 Yahya H. Zweiri, Lakmal D. Seneviratne, and Kaspar Althoefer. 2005. Stability analysis of a three-term backpropagation algorithm. Neural Netw. 18, 10 (December 2005), 1341-1347.
 Y. F. Yam and T.W.S. Chow, “Extended Back Propagation Algorithm,” Electronics Letters, vol. 29(19), pp. 1701-1702, 1993.
 G. P. Drago, M. Morando and S. Ridella, “An Adaptive Momentum Back Propagation, Neural Computing and Application,” vol. 3, pp. 213- 221, 1995.
 V. P. S. Kirar, K. Burse, M. Manoria, “Improved Back Propagation Algorithm for Complex Multiplicative Neuron Model,” Proceedings of Springer conference, Information Technology and Mobile Communication, Communication in Computer and Information Science (CCIS), vol. 147, pp. 67-73, April 2011.
 N. Baldo and M. Zorzi, “Learning and Adaptation in Cognitive Radios using Neural Networks,” 5th IEEE Consumer Communications and Networking Conference (CCNC 2008), pp. 998-1003, january 2008.
 G. Bianchi, “Performance Analysis of the IEEE 802.11 Distributed Coordination Function,” IEEE Journal on Selected Areas in Communications, vol. 18, no. 3, pp. 535-547, March 2000.
 Z. Zhang and X. Xie, “Intelligent Cognitive Radio: Research on Learning and Evaluation of CR Based on Neural Network,” Proceedings ITI 5th International Conference on Information and Communications Technology (ICICT 2007), pp. 33-37, December 2007.
 C. J. Rieser, T. W. Rondeau, C. W. Bostian, and T. M. Gallagher. “Cognitive RadioTest bed: Further Details and Testing of a Distributed Genetic Algorithm Based Cognitive Engine for Programmable Radios,” IEEE MILCOM, October 2004.
 X. Zhu, Y. Liu, W. Weng, and D. Yuan, “Channel Sensing Algorithm based on Neural Network for Cognitive Wireless Mesh Network,” in Proceedings of IEEE International Conference on Wireless Communications (WiCom), pp. 1-4, 2008.
 V. K. Tumuluru, P. Wang, and D. Niyato, “A Neural Network Based Spectrum Prediction Scheme for Cognitive Radio,” In IEEE International Conference on Communication (ICC), Cape Town, South Africa, pp. 1-5, 2010.
 A. Akbar and W. H. Tranter, “Dynamic Spectrum Allocation in Cognitive Radio using Hidden Markov Models: Poisson Distributed Case,” in Proceedings of IEEE SoutheastCon, pp. 196-201, March 2007.
 C. H. Park, S. W. Kim, S. M. Lim and M. S. Song, “HMM based Channel Status Predictor for Cognitive Radio,” in Proceedings of Asia- Pacific Microwave Conference (APMC), pp. 1-4, December 2007.
 Q. Cai, S. Chen, X. Li, N. Hu, H. He, Y.-D. Yao, and J. Mitola, “An Integrated Incremental Self-Organizing Map and Hierarchical Neural Network Approach for Cognitive Radio Learning,” The 2010 International Joint Conference on in Neural Networks (IJCNN), pp. 1-6, July 2010.
 Yu-Jie Tang, Qin-Yu Zhang, Wei Lin, “Artificial Neural Network based Spectrum Sensing Method for Cognitive Radio,” IEEE conference on wireless communications and mobile computing, pp. 1-4, September 2010.
 N. Shamsi, A. Mousavinia, H. Amirpour, “A Channel State Prediction for Multi-Secondary users in a Cognitive Radio based on Neural Network,” International Conference on Electronics, Computer and Computation (ICECCO)2013, pp. 200-203, November 2013.
 X. Tan, H. Huang, L. Ma, “Frequency Allocation with Artificial Neural Networks in Cognitive Radio System,” IEEE TENCON Spring Conference 2013, pp. 366-370, April 2013.
 T. Zhang, M. Wu. C. Liu, “Cooperative Spectrum Sensing based on Artificial Neural Network for Cognitive Radio System,” 8th International Conference on Wireless Communication, Networking and Mobile Computing (WiCOM) 2012, pp. 1-5, September 2012.
 V. Gatla, M. Venkatesan, A. V. Kulkarni, “Feed Forward Neural Network based learning scheme for cognitive radio systems,” Third International Conference on Computational Intelligance and Information technology, CIIT 2013, pp. 25-31, October 2013.