Multilayer Feed-forward Neural Network learning based Dynamic Chinese restaurant model for dynamic spectrum access in cognitive radio networks

As an effective approach to improve spectrum efficiency, cognitive radio network make it possible for secondary users (SU) to share the spectrum with primary users (PU), on the condition that the primary users have preemptive priority. In this paper, we applied the Dynamic Chinese restaurant game, which ideally modeled the spectrum sensing and access in cognitive network. We propose the use of Multilayer Feed-forward Neural Networks (MFNN) as an effective method for users to learn the network state, which can be regarded as how a customer learn the table state in the restaurant. Moreover, in order to select an optimal table for a secondary user, subsequent secondary users' sequential decisions are considered. The effectiveness and efficiency of the proposed scheme is verified in the simulation.

[1]  M. Zorzi,et al.  Learning and Adaptation in Cognitive Radios Using Neural Networks , 2008, 2008 5th IEEE Consumer Communications and Networking Conference.

[2]  Masoumeh Nasiri-Kenari,et al.  Analytical and learning-based spectrum sensing time optimisation in cognitive radio systems , 2011, IET Commun..

[3]  Yuhong Yang,et al.  Information Theory, Inference, and Learning Algorithms , 2005 .

[4]  K. J. Ray Liu,et al.  Cognitive Radio Networking and Security: A Game-Theoretic View , 2010 .

[5]  Halim Yanikomeroglu,et al.  Access Strategies for Spectrum Sharing in Fading Environment: Overlay, Underlay, and Mixed , 2010, IEEE Transactions on Mobile Computing.

[6]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[7]  J. Sjöberg.  Neural networks for modelling and control of dynamic systems: M. Nørgaard, O. Ravn, N. K. Poulsen and L. K. Hansen. Springer-Verlag, London Berlin Heidelberg, 2000, pp. xiv+246 , 2004 .

[8]  Licheng Jiao,et al.  Fast Sparse Approximation for Least Squares Support Vector Machine , 2007, IEEE Transactions on Neural Networks.

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

[10]  K. J. Ray Liu,et al.  Advances in cognitive radio networks: A survey , 2011, IEEE Journal of Selected Topics in Signal Processing.

[11]  K. J. Ray Liu,et al.  Dynamic Chinese Restaurant Game: Theory and Application to Cognitive Radio Networks , 2014, IEEE Transactions on Wireless Communications.

[12]  Rich Caruana,et al.  Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping , 2000, NIPS.

[13]  Niels Kjølstad Poulsen,et al.  Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner’s Handbook , 2000 .

[14]  K. J. Ray Liu,et al.  Chinese Restaurant Game , 2012, IEEE Signal Processing Letters.