Ultra-wideband signal acquisition by use of channel-interleaved photonic analog-to-digital converter under the assistance of dilated fully convolutional network

Abstract

We demonstrate a photonic architecture to enable the separation of ultra-wideband signals. The architecture consists of a channel-interleaved photonic analog-to-digital converter (PADC) and a dilated fully convolutional network (DFCN). The aim of the PADC is to perform ultra-wideband signal acquisition, which introduces the mixing of signals between different frequency bands. To alleviate the interference among wideband signals, the DFCN is applied to reconstruct the waveform of the target signal from the ultra-wideband mixed signals in the time domain. The channel-interleaved PADC provides a wide spectrum reception capability. Relying on the DFCN reconstruction algorithm, the ultra-wideband signals, which are originally mixed up, are effectively separated. Additionally, experimental results show that the DFCN reconstruction algorithm improves the average bit error rate by nearly three orders of magnitude compared with that without the algorithm.