# Adaptive Linearization for the Sub-Nyquist Photonic Receiver Based on Deep Learning
## 4 Conclusions  
In conclusion, it is inevitable that inherent hardware imperfections cause the transmitted signal to deviate from its ideal representation in sampled analog photonic links. Particularly, the HD and IMD distortions introduced by MZMs and LNAs severely deteriorate the quality of the received signals. To solve these problems, a deep Resnet was designated to adaptively mitigate the multifactorial spurious distortions and noises, while simultaneously maintaining the rare patterns of signals.  
The experimental results validated that the deep learning linearization method could automatically suppress spurious distortions with frequencies of 0-40 GHz. It was available for different types of signal generators and signal modulation types, including single-tone signals, dual-tone signals, wireless modulated communication signals, and LFM wideband signals. The proposed method could improve the \(SSR\) and \(SNR\) by 42.15 dB and \(\sim\)15 dB, respectively.  
In contrast to the previous linearization methods, the deep learning method could synchronously handle the multi-source induced spurious distortions and noise interference that are intractable for traditional digital signal processing techniques. Meanwhile, it could save miscellaneous hardware resources, and is expected to lower the SWaP of the receiver.  
Figure 7: Examples of spurious elimination for the quadrature phase-shift keying (QPSK) communication signal. **(a)** Comparison of the time-domain waveform before and after rectification by the Resnet model; **(b)** Elimination of intricate spurious spectral components and reduction of the noise floor in the frequency domain. Examples of spurious suppression for the linear frequency modulation (LFM) radar signal; **(c)** Comparison of the time-domain waveform before and after rectification by the Resnet model; **(d)** Elimination of intricate spurious spectral components and the reduction of the noise floor.  
The capabilities shown in the high-frequency and multi-carrier signals processing are also promising for ultra-wideband (UWB) microwave signal processing.  
In the future, the proposed deep learning algorithms could be ported to embedded applications, such as field-programmable gate arrays (FPGA), for real-time signal processing. Besides, we could explore the possibility of applying the deep learning linearization method to other photonic links as well.  
Conceptualization, J.Z.; methodology, L.Z. and J.Z; software, L.Z.; validation, L.Z. and D.S.; formal analysis, J.Z. and K.Y.; investigation, L.Z.; resources, Y.P. and X.Z.; data curation, H.N.; writing--original draft preparation, L.Z.; writing--review and editing, L.H., Z.Z., M.S.; visualization, L.H.; supervision, J.Z.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China under Grant (grant number: 2020 YFB2205804), the National Natural Science Foundation of China (grant number: 62075240) and the National Key R&D Program of China (2021 ZD0140301).  
Not applicable. Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors declare no conflict of interest.