Adaptive Linearization for the Sub-Nyquist Photonic Receiver Based on Deep Learning

Abstract

Due to the nonlinear and aliasing effects, the sub-Nyquist photonic receiver for radio frequency (RF) signals with large instantaneous bandwidth suffers limited dynamic range and noise performance. We designated a deep residual network (Resnet) to realize adaptive linearization across 40 GHz bandwidth. In contrast to conventional linearization methods, the deep learning method achieves the suppression of multifactorial spurious distortions and the noise floor simultaneously. It does not require an accurate calculation of the nonlinear transfer function or prior signal information. The experiments demonstrated that the proposed Resnet could improve the spur-free dynamic range (SFDR) and the signal-to-noise ratio (SNR) significantly by testing with single-tone signals, dual-tone signals, wireless communication signals, and modulated radar signals.