CNN-Enabled Multiple Power-Levels Identification in Cognitive Radio Networks
文献类型：会议论文
| 作者 | Tan, Zhenyu4; Liu, Qi1 ; Li, Zan4; Wang, Danyang4; Zhang, Ning3; Dai, Hong-Ning2
|
| 出版日期 | 2023-01-11 |
| 会议日期 | Dec 04-08, 2022 |
| 会议地点 | Rio de Janeiro, BRAZIL |
| 关键词 | cognitive radio convolutional neural network multiple transmit power levels identification non-gaussian signal |
| DOI | 10.1109/globecom48099.2022.10001297 |
| 页码 | 1881-1886 |
| 国家 | BRAZIL |
| 英文摘要 | Spectrum sensing with transmit power identification can greatly facilitate the application of the hybrid spectrum access strategy in cognitive radio (CR) networks. Conventional model-driven methods suffer from severe performance degradation in low signal-to-noise ratio (SNR) regime. In this paper, we propose a multiple transmit power levels identification network (TPIN) which consists of three components. In the data preprocessing components, the covariance matrix (COV) of the received data is first employed as the observation data. Then, the residual network (ResNet) based feature extractor components is used to construct the test statistic by extracting high-dimensional features of the observation data. Furthermore, the likelihood ratio test (LRT) criterion is leveraged to design the cost function for obtaining the maximum posterior probability in the classifier components. Different from the assumption in conventional method, the prior probability of each transmit power levels is unknown to the TPIN, and the array of training set is randomly disturbed. In addition, in order to verify the ability of TPIN in data features extraction, a comparison reference experiment using a general test statistic (e.g., higher-order cumulative) as the observation data is introduced. Finally, simulation results demonstrate the identification performance of the COV-based (COV-TPIN) scheme. |
| 产权排序 | 2 |
| 会议录 | IEEE Global Communications Conference (GLOBECOM), 04-08 December 2022
 |
| 会议录出版者 | Ieee |
| 会议录出版地 | NEW YORK |
| 语种 | 英语 |
| ISBN号 | 978-1-6654-3540-6 |
| WOS记录号 | WOS:000922633501150 |
| 源URL | [http://ir.xao.ac.cn/handle/45760611-7/6702]  |
| 专题 | 微波接收机技术实验室 |
| 作者单位 | 1.Xinjiang Astronomical Observatory, Chinese Academy of Sciences, Urumqi, 830011; 2.Lingnan University, Hong Kong 3.Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, N9B 3P4, Canada; 4.State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, 710071, China; |
| 推荐引用方式 GB/T 7714 | Tan, Zhenyu,Liu, Qi,Li, Zan,et al. CNN-Enabled Multiple Power-Levels Identification in Cognitive Radio Networks[C]. 见:. Rio de Janeiro, BRAZIL. Dec 04-08, 2022. |
入库方式： OAI收割
来源：新疆天文台
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