Intelligent Spectrum Sensing Using Optimized Machine Learning Algorithms for Cognitive Radio in 5G Communication,ERICDATA高等教育知識庫
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熱門: 羅文君  簡淑芸  Yeonjoo Lim  Jong-Hyouk Lee  Beyond 5G  SDGs  
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篇名
Intelligent Spectrum Sensing Using Optimized Machine Learning Algorithms for Cognitive Radio in 5G Communication
並列篇名
Intelligent Spectrum Sensing Using Optimized Machine Learning Algorithms for Cognitive Radio in 5G Communication
作者 M. VarunC. Annadurai
英文摘要

Spectrum Sensing plays an important role in cognitive radio which is used to resolve the co-existence issue and to optimize the available spectrum efficiency. However, the upcoming 5G communication involves the diversified scenarios with distinguished characteristics which makes spectrum sensing more difficult to serve different application in terms of high performance and flexible implementation. Also motivated by this challenge, the paper proposes the new algorithm which implemented the novel bat optimized multi-layer extreme learning machine and works on different input vectors such as received signal strength, distance, Energy and channel ID and classifies the users for the better classification and sensing. Moreover, to prove the suitability of the proposed algorithm in terms of performance under 5G scenario for health care applications, we have compared the other spectrum sensing techniques and parameters such as sensitivity, selectivity and channel detection probability. Ultimately the results demonstrate that the proposed spectrum sensing has outperformed the other algorithms and shows its capability to adopt for various 5G scenario.

 

起訖頁 827-836
關鍵詞 Spectrum sensingBAT optimized extreme learning machinesCognitive radioHealth care applications
刊名 網際網路技術學刊  
期數 202207 (23:4期)
出版單位 台灣學術網路管理委員會
DOI 10.53106/160792642022072304017  複製DOI
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