中心硕士生汪锐的工作——Ultra-wideband signal acquisition by use of channel-interleaved photonic analog-to-digital converter under the assistance of dilated fully convolutional network的相关成果近期被Chinese Optics Letters期刊接收，该工作得到了国家重点研发计划（2019YFB2203700）、国家自然科学基金（61822508，61571292）的部分资助，解决了通道交织型光子模数转换系统在获取宽带信号时出现的信号混叠问题。通过将光子模数转换系统与深度学习算法结合，提出一种“扩张全卷积网络（Dilated Fully Convolutional Network, DFCN）”，成功实现对宽带混合信号的分离，有效缓解了宽带信号间的干扰。另外，将DFCN应用在数字调制信号中进行了定量分析，相较于分离前，平均误比特率提高了三个数量级。
摘要：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 (BER) by nearly three orders compared with that without the algorithm.