DOI: https://doi.org/10.15587/1729-4061.2017.108445

Improvement of control method over the environment of cognitive radio system using a neural network

Yaroslav Obikhod, Volodymyr Lysechko, Yuliia Sverhunova, Oleksandr Zhuchenko, Oleksiy Progonniy, Georgiy Kachurovskiy, Viacheslav Tretijk, Volodymyr Malyuga, Valeriy Voinov

Abstract


In the course of present research, we examined a method to control the environment of a cognitive radio using a PNN neural network as a decision-making system. As a result of research into the WRAN environment control architecture using a neural network, a flow chart of the environment control algorithm has been developed. Its special feature is that a neural network is located at each base station and interacts with other WRANs according to the IEEE 802.22 standard. The cognitive radio environment control architecture has been improved using a PNN network. This is achieved by applying a special case of radial basis networks ‒ a probabilistic neural network and a hybrid learning system, as well as a hybrid form of error correction and accumulating the experience of past iterations.

To simulate a PNN neural network, the MATLAB software package was selected using standard functions of "Neural" and "Simulink" sections. To determine the two measurable vectors of the input set, four domains of input vectors with a normal distribution law with arbitrary values have been created. As a result of the network simulation, a connectivity matrix corresponding to the input vector has been generated.

A PNN neural network simulation showed statistically confirmed results. The network has one competing layer and a layer for receiving and splitting the attributes of the input vector. This ensures the use of a small number of network neurons and, accordingly, the fast learning ability of the network – 1200 ms, which is 1.67 times faster than the required value, which is achieved by employing parallel processing of information.

Moreover, the improved method provides the ability to work in the presence of a large number of uninformative, noise input signals, as well as the adaptation to environmental changes

Keywords


cognitive radio; architecture; radio frequency resource; neural network; probabilistic neural network

References


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GOST Style Citations


Mitola, J. Cognitive radio: making software radios more personal [Text] / J. Mitola, G. Q. Maguire // IEEE Personal Communications. – 1999. – Vol. 6, Issue 4. – P. 13–18. doi: 10.1109/98.788210 

Arslan, H. Cognitive Radio, Software Defined Radio and Adaptive Wireless Systems [Text] / H. Arslan. – Springer, 2007. doi: 10.1007/978-1-4020-5542-3 

Bloem, M. A Stackelberg game for power control and channel allocation in cognitive radio networks [Text] / M. Bloem, T. Alpcan, T. Basar // Proceedings of the 2nd International ICST Conference on Performance Evaluation Methodologies and Tools, 2007. – P. 49–53. doi: 10.4108/gamecomm.2007.2040 

Pavlov, I. Y. Analiz tsentralizovannykh i detsentralizovannykh sistem avtomatizirovannogo upravleniya [Text] / I. Y. Pavlov, V. L. Koloskov, E. V. Ivanov // Novye Informatsionnye tekhnologii v avtomatizirovannykh sistemakh. – 2016. – Vol. 19. – P. 338–340.

Bacchus, R. Long-term, Wide-Band Spectral Monitoring in Support of Dynamic Spectrum Access Networks [Text] / R. Bacchus, A. Fertner, C. Hood, D. Roberson // 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2008. – P. 257–259. doi: 10.1109/dyspan.2008.39 

Burbank, J. L. Security in cognitive radio networks: the required evolution in approaches to wireless network security [Text] / J. L. Burbank // 2008 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008), 2008. – P. 1–7. doi: 10.1109/crowncom.2008.4562536 

Petcu, A. A distributed, complete method foe multiagent constraint optimization [Text] / A. Petcu, B. Faltings // Fifth International Workshop on Distributed Constraint Reasoning, 2004. – P. 266–271.

Ranganathan, R. Radio for Smart Grid: Theory, Algorithms, and Security [Text] / R. Ranganathan, R. Qiu, Z. Hu // International Journal of Digital Multimedia Broadcasting, 2011. – P. 14.

Li, A. A Sensitive Secondary Users Selection Algorithm for Cognitive Radio Ad Hoc Networks [Text] / A. Li, G. Han, L. Wan, L. Shu // Sensors. – 2016. – Vol. 16, Issue 4. – P. 445. doi: 10.3390/s16040445 

Shiang, H.-P. Distributed resource management in multi-hop cognitive radio networks for delay sensitive transmission [Text] / H.-P. Shiang, M. van der Schaar // IEEE Transactions on Vehicular Technology. – 2009. – Vol. 58, Issue 2. – P. 941–953. doi: 10.1109/tvt.2008.925308 

Rana, A. R. NDN-CRAHNs: Named Data Networking for Cognitive Radio Ad Hoc Networks [Text] / A. R. Rana, J. Kim, B-S Kim // Mobile Information Systems, 2015. – P. 1–12. doi: 10.1155/2015/281893 

Tang, J. Joint spectrum allocation and scheduling for fair spectrum sharing in cognitive radio wireless networks [Text] / J. Tang, S. Misra, G. Xue // Computer Networks. – 2008. – Vol. 52, Issue 11. – P. 2148–2158. doi: 10.1016/j.comnet.2008.03.010 

Haynikin, S. Neyronnye seti. 2nd edition [Text] / S. Haynikin. – Moscow: Vilyams, 2006. – P. 371–378.

Gorban, А. N. Obuchenie neyronnyih setey [Text] / А. N. Gorban. – Moscow: USSR-USA «ParaGraph», 1990. – 160 p.







Copyright (c) 2017 Yaroslav Obikhod, Volodymyr Lysechko, Yuliia Sverhunova, Oleksandr Zhuchenko, Oleksiy Progonniy, Georgiy Kachurovskiy, Viacheslav Tretijk, Volodymyr Malyuga, Valeriy Voinov

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ISSN (print) 1729-3774, ISSN (on-line) 1729-4061