Artificial Neural Networks for Cognitive Radio Network: A Survey
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.
[1] J. Mitola, “Cognitive Radio: An Integrated Agent Architecture for
Software define Radio,” Ph.D. dissertation, Royal Institute of
Technology (KTH), Sweden, 2000.
[2] S. Haykin, "Cognitive Radio: Brain-Empowered Wireless
Communications," IEEE Journal on Selected Areas in Communications,
vol. 23, no. 2, pp. 201-220, February 2005.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] D.E. Rumelhart, G.E. Hinton and R.J. Williams, "Learning
representations by back -propagating errors," Nature (London), 323,
533-536, 1986.
[8] 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.
[9] Y. F. Yam and T.W.S. Chow, “Extended Back Propagation Algorithm,”
Electronics Letters, vol. 29(19), pp. 1701-1702, 1993.
[10] G. P. Drago, M. Morando and S. Ridella, “An Adaptive Momentum
Back Propagation, Neural Computing and Application,” vol. 3, pp. 213-
221, 1995.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
[1] J. Mitola, “Cognitive Radio: An Integrated Agent Architecture for
Software define Radio,” Ph.D. dissertation, Royal Institute of
Technology (KTH), Sweden, 2000.
[2] S. Haykin, "Cognitive Radio: Brain-Empowered Wireless
Communications," IEEE Journal on Selected Areas in Communications,
vol. 23, no. 2, pp. 201-220, February 2005.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] D.E. Rumelhart, G.E. Hinton and R.J. Williams, "Learning
representations by back -propagating errors," Nature (London), 323,
533-536, 1986.
[8] 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.
[9] Y. F. Yam and T.W.S. Chow, “Extended Back Propagation Algorithm,”
Electronics Letters, vol. 29(19), pp. 1701-1702, 1993.
[10] G. P. Drago, M. Morando and S. Ridella, “An Adaptive Momentum
Back Propagation, Neural Computing and Application,” vol. 3, pp. 213-
221, 1995.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
@article{"International Journal of Information, Control and Computer Sciences:68901", author = "Vishnu Pratap Singh Kirar", title = "Artificial Neural Networks for Cognitive Radio Network: A Survey", abstract = "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.
", keywords = "Artificial Neural Network, Cognitive Radio,
Cognitive Radio Networks, Back Propagation, Spectrum Sensing.", volume = "9", number = "1", pages = "224-6", }
