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A Digital Signal Recovery Technique Using DNNs for LEO Satellite Communication Systems
Alternative Title20202308787290
Zhang, Yufeng1; Wang, Zhugang2; Huang, Yonghui3; Wei, Wei4; Pedersen, Gert Frolund5; Shen, Ming6
Source PublicationIEEE Transactions on Industrial Electronics
2020
DOI10.1109/TIE.2020.2994873
ISSN02780046
Language英语
KeywordDeep neural networks - Efficiency - Gaussian noise (electronic) - Orbits - Polynomials - Power amplifiers - Radio frequency amplifiers - Radio transmission - Satellite communication systems - Satellites - Vector spaces - White noise Additive white Gaussian noise channel - Error vector magnitude - High power efficiencies - Low earth orbit satellites - Radio frequency power amplifiers - Robustness against noise - Saturation region - Signal power variations
AbstractThis paper proposes a new digital signal recovery (DSR) technique for next-generation power efficient low Earth orbit (LEO) satellite-to-ground communication systems, which feature additive white Gaussian noise (AWGN) channel and significant power variation. This technique utilizes the prior knowledge (i.e., nonlinearities of radio frequency power amplifiers (RF-PAs)) of space-borne transmitters to improve the quality of the signal received at ground stations by modeling and mitigating the imperfection using deep neural networks (DNNs). Benefiting from its robustness against noise and power variation, the proposed DNN based DSR technique (DNN-DSR), can correct high signal distortions caused by the nonlinearities and hence allows RF-PAs to work close to their saturation region, leading to a high power efficiency of the LEO satellites. This work has been validated by both simulations and experiments, in comparison with the power back-off technique as well as memory polynomial based DSR solutions. Experimental results show that the DNN-DSR technique can increase the drain efficiency of the space-borne RF-PA from 32.6% to 45% while maintaining the same error vector magnitude as the power back-off technique. It has also been demonstrated that the proposed DNN-DSR technique can handle a signal power variation of 12 dB, which is challenging for conventional solutions. IEEE
Indexed ByEI
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Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/7471
Collection中国科学院国家空间科学中心
Affiliation1.National Space Science Center Chinese Academy of Sciences, 53016 Beijing, Beijing China (e-mail: zzs0315@126.com);
2.National Space Science Center Chinese Academy of Sciences, 53016 Beijing China (e-mail: wangzg@nssc.ac.cn);
3.National Space Science Center Chinese Academy of Sciences, 53016 Beijing China (e-mail: yonghui@nssc.ac.cn);
4.Aalborg Universitet, 1004 Aalborg Denmark (e-mail: weiweisucceed@gmail.com);
5.Electronic systems, Aalborg University, 1004 Aalborg Denmark (e-mail: gfp@es.aau.dk);
6.Aalborg Universitet, 1004 Aalborg Denmark (e-mail: mish@es.aau.dk)
Recommended Citation
GB/T 7714
Zhang, Yufeng,Wang, Zhugang,Huang, Yonghui,et al. A Digital Signal Recovery Technique Using DNNs for LEO Satellite Communication Systems[J]. IEEE Transactions on Industrial Electronics,2020.
APA Zhang, Yufeng,Wang, Zhugang,Huang, Yonghui,Wei, Wei,Pedersen, Gert Frolund,&Shen, Ming.(2020).A Digital Signal Recovery Technique Using DNNs for LEO Satellite Communication Systems.IEEE Transactions on Industrial Electronics.
MLA Zhang, Yufeng,et al."A Digital Signal Recovery Technique Using DNNs for LEO Satellite Communication Systems".IEEE Transactions on Industrial Electronics (2020).
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