CSIT: channel state and idle time predictor using a neural network for cognitive LTE-Advanced network

Cognitive radio (CR) is a novel methodology that facilitates unlicensed users to share a licensed spectrum without interfering with licensed users. This intriguing approach is exploited in the Long Term Evolution-Advanced (LTE-A) network for performance improvement. Although LTE-A is the foremost mobile communication standard, future underutilization of the spectrum needs to be addressed. Therefore, dynamic spectrum access is explored in this study. The performance of CR in LTE-A can significantly be enhanced by employing predictive modeling. The neural network-based channel state and idle time (CSIT) predictor is proposed in this article as a learning scheme for CR in LTE-A. This predictive-based learning is helpful in two ways: sensing only those channels that are predicted to be idle and selecting the channels for CR transmission that have the largest predicted idle time. The performance gains by exploiting CSIT prediction in CR LTE-A network are evaluated in terms of spectrum utilization, sensing energy, channel switching rate, packet loss ratio, and average instantaneous throughput. The results illustrate that significant performance is achieved by employing CSIT prediction in LTE-A network.

[1]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[2]  An He,et al.  A Survey of Artificial Intelligence for Cognitive Radios , 2010, IEEE Transactions on Vehicular Technology.

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

[4]  Dusit Niyato,et al.  A Neural Network Based Spectrum Prediction Scheme for Cognitive Radio , 2010, 2010 IEEE International Conference on Communications.

[5]  Liang Yin,et al.  Spectrum behavior learning in Cognitive Radio based on artificial neural network , 2011, 2011 - MILCOM 2011 Military Communications Conference.

[6]  Ryan E. Irwin,et al.  The effects of a Dynamic Spectrum Access overlay in LTE-Advanced networks , 2011, 2011 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[7]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[8]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[9]  Panagiotis Demestichas,et al.  Neural network-based learning schemes for cognitive radio systems , 2008, Comput. Commun..

[10]  Mischa Dohler,et al.  Learning from experts in cognitive radio networks: The docitive paradigm , 2010, 2010 Proceedings of the Fifth International Conference on Cognitive Radio Oriented Wireless Networks and Communications.

[11]  Sudharman K. Jayaweera,et al.  A Survey on Machine-Learning Techniques in Cognitive Radios , 2013, IEEE Communications Surveys & Tutorials.

[12]  Shuji Hashimoto,et al.  Learning from imperfect data , 2007, Appl. Soft Comput..

[13]  Mischa Dohler,et al.  Docitive networks: an emerging paradigm for dynamic spectrum management [Dynamic Spectrum Management] , 2010, IEEE Wireless Communications.

[14]  Muhammad Imran Taj,et al.  Cognitive Radio Spectrum E volution Prediction using A rtificial Neural Networks based Multivariate T ime Series Modelling , 2011, EW.

[15]  Timothy J. O'Shea,et al.  Applications of Machine Learning to Cognitive Radio Networks , 2007, IEEE Wireless Communications.

[16]  Hossein Shokri Ghadikolaei,et al.  Intelligent Sensing Matrix Setting in Cognitive Radio Networks , 2012, IEEE Communications Letters.

[17]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[18]  Yang Yang,et al.  Carrier aggregation for LTE-advanced mobile communication systems , 2010, IEEE Communications Magazine.

[19]  Yang Yang,et al.  Performance Analysis of Selective Opportunistic Spectrum Access With Traffic Prediction , 2010, IEEE Transactions on Vehicular Technology.

[20]  Xavier Gelabert,et al.  Implementing opportunistic spectrum access in LTE-advanced , 2012, EURASIP J. Wirel. Commun. Netw..

[21]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[22]  Klaus I. Pedersen,et al.  Carrier aggregation for LTE-advanced: functionality and performance aspects , 2011, IEEE Communications Magazine.

[23]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[24]  A. Flitman,et al.  Using neural networks to predict binary outcomes , 1997, 1997 IEEE International Conference on Intelligent Processing Systems (Cat. No.97TH8335).