PID5407653.pdf (749.74 kB)
Single-shot sub-Nyquist RF signal reconstruction based on deep learning network
conference contribution
posted on 2019-06-11, 11:02 authored by S Liu, CK Mididoddi, H Zhou, B Li, W Xu, C WangReal-time detection of high-frequency RF signals requires sophisticated hardware with large bandwidth and high sampling rates. Existing microwave photonic methods have enabled sub-Nyquist sampling for bandwidth-efficient RF signal detection but fall short in single-shot reconstruction. Here we report a novel single-shot sub-Nyquist RF signal detection method based on a trained deep neural network. In a proof-of-concept demonstration, our system successfully reconstructs high frequency multi-toned RF signals from 5x down-sampled singleshot measurements by utilizing a deep convolutional neural network. The presented approach is a powerful digital accelerator to existing hardware detectors to significantly enhance the detection capability.
Funding
This work was supported in part by the EU FP7 Marie-Curie Career Integration Grant (631883), in part by the Royal Society (IE170007), in part by National Natural Science Foundation of China (Projects 61771148, 61571211 and U1501251), and in part by Guangzhou Science and Technology Plan (Project 201607010290)
History
Citation
2018 International Topical Meeting on Microwave Photonics (MWP), 2018Author affiliation
/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of InformaticsSource
2018 International Topical Meeting on Microwave Photonics (MWP), Toulouse, FranceVersion
- AM (Accepted Manuscript)