%0 Journal Article %1 msmithravenkatesan2014computing %A Ms.Mithra Venkatesan, Dr.A.V.Kulkarni %D 2014 %E Hope, Dr.Martin %J Int. J. on Recent Trends in Engineering and Technology, %K Cognitive_ Computing_ Radio_ Soft techniques %N 1 %P 8 %T Soft Computing based Learning for Cognitive Radio %U http://searchdl.org/public/journals/2014/IJRTET/10/1/24.pdf %V 10 %X Over the last decade the world of wireless communications has been undergoing some crucial changes, which have brought it at the forefront of international research and development interest, eventually resulting in the advent of a multitude of innovative technologies and associated products such as WiFi, WiMax, 802.20, 802.22, wireless mesh networks and Software Defined Radio. Such a highly varying radio environment calls for intelligent management, allocation and usage of a scarce resource, namely the radio spectrum. One of the most prominent emerging technologies that promise to handle such situations is Cognitive Radio. Cognitive Radio systems are based on Software Defined Radio technology and utilize intelligent software packages that enrich their transceivers with the highly attractive properties of self-awareness, adaptability and capability to learn. The Cognitive Engine, the intelligent system behind the Cognitive Radio, combines sensing, learning, and optimization algorithms to control and adapt the radio system from the physical layer and up the communication stack. The integration of a learning engine can be very important for improving the stability and reliability of the discovery and evaluation of the configuration capabilities. To this effect, many different learning techniques are available and can be used by a Cognitive Radio ranging from pure lookup tables to arbitrary combinations of soft Computing techniques, which include among others: Artificial Neural Networks, evolutionary/Genetic Algorithms, reinforcement learning, fuzzy systems, Hidden Markov Models, etc. The proposed work contributes in this direction, aiming to develop a learning scheme and work towards solving problems related to learning phase of Cognitive Radio systems. Interesting scenarios are to be mobilized for the performance assessment work, conducted in order to design and use an appropriate structure, while indicative results need to be presented and discussed in order to showcase the benefits of incorporating such learning schemes into Cognitive Radio systems. Subsequently feasibility of such learning schemes could be tested with simulations. In the near future, such learning schemes are expected to assist a Cognitive Radio system to compare among the whole of available, candidate radio configurations and finally select the best one to operate in.

@article{msmithravenkatesan2014computing, abstract = {Over the last decade the world of wireless communications has been undergoing some crucial changes, which have brought it at the forefront of international research and development interest, eventually resulting in the advent of a multitude of innovative technologies and associated products such as WiFi, WiMax, 802.20, 802.22, wireless mesh networks and Software Defined Radio. Such a highly varying radio environment calls for intelligent management, allocation and usage of a scarce resource, namely the radio spectrum. One of the most prominent emerging technologies that promise to handle such situations is Cognitive Radio. Cognitive Radio systems are based on Software Defined Radio technology and utilize intelligent software packages that enrich their transceivers with the highly attractive properties of self-awareness, adaptability and capability to learn. The Cognitive Engine, the intelligent system behind the Cognitive Radio, combines sensing, learning, and optimization algorithms to control and adapt the radio system from the physical layer and up the communication stack. The integration of a learning engine can be very important for improving the stability and reliability of the discovery and evaluation of the configuration capabilities. To this effect, many different learning techniques are available and can be used by a Cognitive Radio ranging from pure lookup tables to arbitrary combinations of soft Computing techniques, which include among others: Artificial Neural Networks, evolutionary/Genetic Algorithms, reinforcement learning, fuzzy systems, Hidden Markov Models, etc. The proposed work contributes in this direction, aiming to develop a learning scheme and work towards solving problems related to learning phase of Cognitive Radio systems. Interesting scenarios are to be mobilized for the performance assessment work, conducted in order to design and use an appropriate structure, while indicative results need to be presented and discussed in order to showcase the benefits of incorporating such learning schemes into Cognitive Radio systems. Subsequently feasibility of such learning schemes could be tested with simulations. In the near future, such learning schemes are expected to assist a Cognitive Radio system to compare among the whole of available, candidate radio configurations and finally select the best one to operate in.}, added-at = {2014-02-01T07:28:53.000+0100}, author = {Ms.Mithra Venkatesan, Dr.A.V.Kulkarni}, biburl = {https://www.bibsonomy.org/bibtex/25cd08b6999fd030a9b7acac1326e1659/idescitation}, editor = {Hope, Dr.Martin}, interhash = {20b279e1395201c984264d6013e35d4c}, intrahash = {5cd08b6999fd030a9b7acac1326e1659}, journal = {Int. J. on Recent Trends in Engineering and Technology,}, keywords = {Cognitive_ Computing_ Radio_ Soft techniques}, month = {Jan 2014}, number = 1, pages = 8, timestamp = {2014-02-01T07:28:53.000+0100}, title = {Soft Computing based Learning for Cognitive Radio}, url = {http://searchdl.org/public/journals/2014/IJRTET/10/1/24.pdf}, volume = 10, year = 2014 }