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Proceedings Paper

Design of cognitive engine for cognitive radio based on the rough sets and radial basis function neural network
Author(s): Yanchao Yang; Hong Jiang; Congbin Liu; Zhongli Lan
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Paper Abstract

Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.

Paper Details

Date Published: 14 March 2013
PDF: 7 pages
Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 876809 (14 March 2013); doi: 10.1117/12.2010549
Show Author Affiliations
Yanchao Yang, Southwest Univ. of Science and Technology (China)
Hong Jiang, Southwest Univ. of Science and Technology (China)
Congbin Liu, Southwest Univ. of Science and Technology (China)
Zhongli Lan, Southwest Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 8768:
International Conference on Graphic and Image Processing (ICGIP 2012)
Zeng Zhu, Editor(s)

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