A RELATIONSHIP BETWEEN COGNITIVE INFORMATION PROCESSING IN LEARNING THEORY AND MACHINE LEARNING TECHNIQUES IN COGNITIVE RADIOS
DOI:
https://doi.org/10.17770/sie2018vol1.3191Keywords:
Cognitive Information Processing, Cognitive Radio, Cognitive Radio Learning Algorithms, Cognitivism, Learning Theory, Machine LearningAbstract
The relationship between cognitivism as learning theory in education and machine learning is characterized in this survey paper. The cognitivism describes how learning occurs through internal processing of information and thus leads to understanding and retention. Cognitive information processing plays an active role to understand and process information that learner receives and relates it to already known and stored within learner’s memory. Thus, the cognitive approach defines learning as a change in knowledge which is stored in learner’s memory, and not a change in learner’s behaviour. In regard with importance of various learning problems to designing cognitive communications systems the two main classification categories of learning techniques are explained. Furthermore, the cognitive radio learning algorithms that have been proposed are described. Finally, the similarities and differences among the principles of learning theories and machine learning are discussed.References
Abbas, N., Nasser, Y., & El Ahmad, K. (2015). Recent advances on artificial intelligence and learning techniques in cognitive radio networks. EURASIP Journal on Wireless Communications and Networking, 2015(1), 174.
Bkassiny, M., Li, Y., & Jayaweera, S. K. (2013). A survey on machine-learning techniques in cognitive radios. IEEE Communications Surveys & Tutorials, 15(3), 1136-1159.
Federal Communications Comission (FCC) (2005). ET Docket No 03-108: Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies.
Ginsburg, H. P., & Opper, S. (1988). Piaget's theory of intellectual development. Prentice-Hall, Inc.
Gross, R. (2015). Psychology: The science of mind and behaviour 7th edition. Hodder Education.
Huitt, W. (2003). The information processing approach to cognition. Educational psychology interactive, 3(2), 53-67.
Khattab, A., Perkins, D., & Bayoumi, M. (2012). Cognitive radio networks: from theory to practice. Springer Science & Business Media.
Mitola Iii, J. (2001). Cognitive radio for flexible mobile multimedia communications. Mobile Networks and Applications, 6(5), 435-441.
Mitola, J. (2000). Cognitive radio---an integrated agent architecture for software defined radio.
Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: making software radios more personal. IEEE personal communications, 6(4), 13-18.
Poole, D., Mackworth, A., & Goebel, R. (1998). Computational intelligence: a logical approach.
Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of research and development, 3(3), 210-229.
Thilina, K. M., Choi, K. W., Saquib, N., & Hossain, E. (2013). Machine learning techniques for cooperative spectrum sensing in cognitive radio networks. IEEE Journal on selected areas in communications, 31(11), 2209-2221.
Tirri, K., & Nokelainen, P. (2012). Measuring multiple intelligences and moral sensitivities in education (Vol. 5). Springer Science & Business Media.