# Adaptive Linearization for the Sub-Nyquist Photonic Receiver Based on Deep Learning
proposed a digital postprocessing linearization technique to efficiently suppress dynamic distortions added to a wideband signal in an analog optical link, which achieved suppression of the IMD by up to 35 dB over multiple octaves of signal bandwidth [24]. [PERSON] et al. proposed a digital linearization by directly acquiring the output third-order intercept point from the system hardware, which improved the SFDR by using a high-performance pre- and post-amplifier [25]. These linearization schemes generally required construction of a well-designed distortion-matched hardware path with careful synchronization between the distortion and compensation signals [26], or the addition of further optical devices [27]. Moreover, most of them could only eliminate the nonlinearities caused by the EOM. The nonlinear distortions caused by other devices (like amplifiers), and the noise interferences are rarely coped with simultaneously. Therefore, it is still a great challenge to automatically suppress the nonlinearity and noise of various sources in sub-Nyquist photonic receivers for wideband signals. In recent years, deep learning (DL) has made remarkable achievements in image denoising [28; 29], speech enhancement [30; 31], optical image reconstruction [32; 33] and other related fields [34; 35]. Applying deep neural networks to photonics processing that are difficult to realize by traditional processing algorithms exhibits tremendous attractive perspectives. Deep neural networks can automatically extract the characteristics of signals and analyze the extracted features by establishing the mapping relationship between inputs and outputs in a regression task. By training the models in a way of approaching the minimum value of the loss function with adequate datasets, the deep neural models could adaptively convert the untrained input to the expected target output and dispense with the complicated mathematical operation. Recent developments in applying deep learning to photonic links have attracted much attention. [PERSON] et al. proposed an artificial neural network (ANN) equalizer to mainly mitigate the nonlinear impairments of optical fibers [36]. [PERSON] et al. proposed a microwave photonic instantaneous frequency measurement scanning receiver, in which deep neural network frequency estimation was used to deal with system defects and improve accuracy [37]. [PERSON] et al. proposed two deep neural networks to recover the nonlinear effect and channel mismatch for a two-channel photonic ADC, which improved the effective number of bits (ENOB) of the single-tone signals with an input frequency of 23.332 GHz [38]. Furthermore, they applied the modified photonic ADC in real scenarios with complicated signals utilizing the same setup, which improved the SFDR by ~18 dB [39]. Subsequently, [PERSON] et al. proposed a transfer-learning network in a 20 GS/s photonic system for denoising multiband signals [40]. Besides, [PERSON] et al. investigated a simplified convolutional recurrent autoencoder (CRAE) network for the mismatch compensation of a channel-interleaved photonic analog-to-digital converter (PADC), which improved both the frame rate and the dynamic range [41]. In this paper, we applied a deep residual network (Resnet) in a sub-Nyquist photonic receiver to realize the adaptive linearization and lower the noise floor at the same time. Compared to the conventional linearization methods, the deep learning method requires no complicated hardware setups or miscellaneous digital processing steps, and it is suitable for dealing with multi-source nonlinear spurious distortions. To our knowledge, this is the first attempt that realizes adaptive linearization for a sub-Nyquist photonic receiver with RF signals ranging up to 40 GHz, which are available to single-tone signals, multi-carrier signals, wireless communication signals, and linear frequency modulation (LFM) signals.