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The past communication behaviors that guide the system communication in the future to satisfy the requirements of users and adapt to the changes of environment are the core part of cognitive radio system. In this paper, a cognitive engine based on Bayesian network is proposed to solve the parameters self-adaptive-adjusting problem of cognitive radio system under the complicated and highly varying radio environment and user requirement. Through structure learning and parameter learning of the sample data from the past communication behaviors, cognitive engine is established. The states of radio environment and requirements of users are made as inference evidences by data preprocessing, and the cognitive engine is used to make decision of the configuration parameters of communication system, and then the reconfiguration system is completed. A mobile wireless network is modeled to finish reconfiguration simulation using OPNET tool in this paper. Simulation results show that the proposed cognitive engine can make the wireless mobile network adapt to environment and effectively improve end-to-end communication performance. The feasibility of the method to model cognitive engine with Bayesian network is validated in this paper.
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Keywords:
- cognitive radio /
- bayesian network /
- reconfiguration /
- end-to-end performance
[1] Joseph M III 2000 Ph. D. Dissertation (Royal Institute of Technology)
[2] Haykin S 2005 IEEE Journal on Selected Areas in Communications 23 201
[3] Thomas W R Ph. D. Dissertation (Virginia Polytechnic Institute and State University)
[4] Zhao Z J, Zheng S L, Shang J N, Kong X Z 2007 Acta Phys. Sin. 56 6760 (in Chinese) [赵知劲, 郑仕链, 尚俊娜, 孔宪正 2007 物理学报 56 6760]
[5] Jiao C H, Wang K R 2010 Systems Engineering and Electronics 32 1083 (in Chinese) [焦传海, 王可人 2010 系统工程与电子技术 32 1083]
[6] Zhao Z J, Xu S Y, Zheng S L, Yang X N 2009 Acta Phys. Sin. 58 5118 (in Chinese) 赵知劲, 徐世宇, 郑仕链, 杨小牛 2009 物理学报 58 5118]
[7] Katidiotis A, Tsagkaris K, Demestichas P 2010 Computers & Electrical Engineering 36 518
[8] Feng W J, Liu Z, Qin C L 2011 Pattern Recognition and Artificial Intelligence 24 401 (in Chinese) [冯文江, 刘震, 秦春玲 2011 模式识别与人工智能 24 401]
[9] Jiang H, Ma J H, Huang Y H, Wu C 2011 Journal of Xi Dian University 38 139 (in Chinese) [江虹, 马景辉, 黄玉清, 伍春 2011 西安电子科技大学学报 38 139]
[10] Jiang H, Wu C, Ma J H 2011 Journal of University of Electronic Science and Technology of China 40 41 (in Chinese) [江虹, 伍春, 马景辉 2011 电子科技大学学报 40 41]
[11] Asma A, Badr B, Francine K, Fethi T B 2012 International Journal of Distributed and Parallel Systems 3 91
[12] Jensen F V, Nielsen T D 2007 Bayesian Networks and Decision Graphs (New York: Springer Verlag)
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[1] Joseph M III 2000 Ph. D. Dissertation (Royal Institute of Technology)
[2] Haykin S 2005 IEEE Journal on Selected Areas in Communications 23 201
[3] Thomas W R Ph. D. Dissertation (Virginia Polytechnic Institute and State University)
[4] Zhao Z J, Zheng S L, Shang J N, Kong X Z 2007 Acta Phys. Sin. 56 6760 (in Chinese) [赵知劲, 郑仕链, 尚俊娜, 孔宪正 2007 物理学报 56 6760]
[5] Jiao C H, Wang K R 2010 Systems Engineering and Electronics 32 1083 (in Chinese) [焦传海, 王可人 2010 系统工程与电子技术 32 1083]
[6] Zhao Z J, Xu S Y, Zheng S L, Yang X N 2009 Acta Phys. Sin. 58 5118 (in Chinese) 赵知劲, 徐世宇, 郑仕链, 杨小牛 2009 物理学报 58 5118]
[7] Katidiotis A, Tsagkaris K, Demestichas P 2010 Computers & Electrical Engineering 36 518
[8] Feng W J, Liu Z, Qin C L 2011 Pattern Recognition and Artificial Intelligence 24 401 (in Chinese) [冯文江, 刘震, 秦春玲 2011 模式识别与人工智能 24 401]
[9] Jiang H, Ma J H, Huang Y H, Wu C 2011 Journal of Xi Dian University 38 139 (in Chinese) [江虹, 马景辉, 黄玉清, 伍春 2011 西安电子科技大学学报 38 139]
[10] Jiang H, Wu C, Ma J H 2011 Journal of University of Electronic Science and Technology of China 40 41 (in Chinese) [江虹, 伍春, 马景辉 2011 电子科技大学学报 40 41]
[11] Asma A, Badr B, Francine K, Fethi T B 2012 International Journal of Distributed and Parallel Systems 3 91
[12] Jensen F V, Nielsen T D 2007 Bayesian Networks and Decision Graphs (New York: Springer Verlag)
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