Deep Learning Aided Multi-Level Transmit Power Recognition in Cognitive Radio Networks
文献类型：期刊论文
| 作者 | Tan, Zhenyu3; Wang, Danyang3; Liu, Qi2 ; Li, Zan3; Zhang, Ning1; Abdel-Raheem, Esam1
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| 刊名 | IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
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| 出版日期 | 2023-04-01 |
| 卷号 | 9期号:2页码:332-344 |
| 关键词 | Sensors Feature extraction Computer architecture Interference Deep learning Convolutional neural networks Uncertainty Cognitive radio convolutional neural network multiple transmit power levels recognition |
| ISSN号 | 2332-7731 |
| DOI | 10.1109/TCCN.2023.3235738 |
| 通讯作者 | Wang, Danyang(dywang@xidian.edu.cn) |
| 英文摘要 | According to the regulations of the hybrid access strategy in cognitive radio network, the secondary user (SU) needs to identify the primary user's (PU) specific transmit power level to avoid unacceptable interference with the PU. However, the conventional transmit power recognition methods cannot accurately identify the transmit power in conditions with low signal-to-noise ratio, fading channels and the existence of noise uncertainty, since those methods are based on a fixed statistical theory to model the dynamic electromagnetic environment mathematically. To address these issues, a ResNet-based multi-level transmission power recognition (MTPR) architecture is presented in this paper. Furthermore, the proposed architecture is implemented in two cases with different observation data. In the first case, the received signal's covariance matrix (CM) containing rich energy information is used as the observation data of CM-MTPR scheme. To further improve the identification accuracy, in the second case, the in-phase and quadrature-phase (IQ) data sampled from the received signal that preserves more original information is configured as the observation data of IQ-MTPR scheme. The IQ-MTPR scheme, however, consumes additional computing resources which forms a trade-off between identification performance and computational consumption with the CM-MTPR scheme. Simulation results demonstrate the identification performance of the proposed schemes. |
| WOS关键词 | DYNAMIC SPECTRUM ACCESS ; WIRELESS NETWORKS ; MATCHING APPROACH ; UNDERLAY ; CNN |
| 资助项目 | National Key R&D Program of China[2021YFC2203503] ; National Key R&D Program of China[2022YFC3301300] ; National Natural Science Foundation of China[61901328] ; National Natural Science Foundation of China[11973077] ; National Natural Science Foundation of China[12003061] ; National Natural Science Foundation of China[61631015] ; Young Talent fund of University Association for Science and Technology in Shaanxi, China[20210111] ; National Natural Science Foundation for Distinguished Young Scholar[61825104] ; Innovative Research Groups of the National Natural Science Foundation of China[62121001] |
| WOS研究方向 | Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000967629300007 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key R&D Program of China ; National Natural Science Foundation of China ; Young Talent fund of University Association for Science and Technology in Shaanxi, China ; National Natural Science Foundation for Distinguished Young Scholar ; Innovative Research Groups of the National Natural Science Foundation of China |
| 源URL | [http://ir.xao.ac.cn/handle/45760611-7/5470]  |
| 专题 | 研究单元未命名 |
| 通讯作者 | Wang, Danyang |
| 作者单位 | 1.Univ Windsor, Dept Elect & Comp Engn, Windsor, ON N9B 3P4, Canada 2.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Peoples R China 3.Xidian Univ, Sch Telecommun Engn, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tan, Zhenyu,Wang, Danyang,Liu, Qi,et al. Deep Learning Aided Multi-Level Transmit Power Recognition in Cognitive Radio Networks[J]. IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING,2023,9(2):332-344. |
| APA | Tan, Zhenyu,Wang, Danyang,Liu, Qi,Li, Zan,Zhang, Ning,&Abdel-Raheem, Esam.(2023).Deep Learning Aided Multi-Level Transmit Power Recognition in Cognitive Radio Networks.IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING,9(2),332-344. |
| MLA | Tan, Zhenyu,et al."Deep Learning Aided Multi-Level Transmit Power Recognition in Cognitive Radio Networks".IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 9.2(2023):332-344. |
入库方式： OAI收割
来源：新疆天文台
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