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| Deep Learning Aided Multi-Level Transmit Power Recognition in Cognitive Radio Networks | |
Tan, Zhenyu1; Wang, Danyang1; Liu, Qi2 ; Li, Zan1; Zhang, Ning3; Abdel-Raheem, Esam3
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| 2023-04-01 | |
| Source Publication | IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
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| ISSN | 2332-7731 |
| Volume | 9Issue:2Pages:332-344 |
| Contribution Rank | 2 |
| Abstract | 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. |
| Keyword | Sensors Feature extraction Computer architecture Interference Deep learning Convolutional neural networks Uncertainty Cognitive radio convolutional neural network multiple transmit power levels recognition |
| DOI | 10.1109/TCCN.2023.3235738 |
| Indexed By | SCI |
| Language | 英语 |
| WOS Keyword | DYNAMIC SPECTRUM ACCESS ; WIRELESS NETWORKS ; MATCHING APPROACH ; UNDERLAY ; CNN |
| Funding Project | 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 Research Area | Telecommunications |
| WOS Subject | Telecommunications |
| WOS ID | WOS:000967629300007 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Funding Organization | 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 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.xao.ac.cn/handle/45760611-7/5471 |
| Collection | 射电天文研究室_微波技术实验室 |
| Corresponding Author | Wang, Danyang |
| Affiliation | 1.Xidian Univ, Sch Telecommun Engn, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China 2.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Peoples R China 3.Univ Windsor, Dept Elect & Comp Engn, Windsor, ON N9B 3P4, Canada |
| Recommended Citation 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Tan-2023-Deep Learni（3048KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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