文章基本信息

标题：Deep Learning Enabled Spectrum Sensing Radio for Opportunistic Usage
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作者：Muneeb Aalam Khan ; Aamir Zeb Shaikh ; Shabbar Naqv 等
期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2019
卷号：19
期号：11
页码：179-183
出版社：International Journal of Computer Science and Network Security
摘要：Radio spectrum is becoming overcrowded especially after the roll out of social media applications, live broadcasting, video calling and related applications. Additionally, under the umbrella of 5G and 5G+ wireless standards, these applications will require higher speed and higher bandwidth. Under the static allocation of frequencies regime, it is really difficult to accommodate the next generation users using the classic wireless technology. Thus, the current conditions in RF usage suggests the use of Cognitive Radio technology for using the RF spectrum in opportunistic fashion. This technology advocates the use of spectrum fashion such that the primary users are not faced with harmful interference. This paper presents the implementation of Deep Learning algorithm i.e. ADAM and Levenberg Merquardt algorithm (LMA) for prediction of spectral holes into Karachi city. Root Mean Square Error (RMSE) values computed for 1.9 GHz comes out to be 0.00063683 and for 3.4GHz the RMSE becomes 0.0010649, whereas RMSE for LMA is 2.91183e-03 at 1.9 GHz, appearing at epoch 5 and 6.0607e-3 at epoch 5 for 3.4GHz RMSE
关键词：Deep Learning (DL); Machine learning (ML); Cognitive Radio (CR); Levenberg Merquardt Algorithm (LMA); ADAM