Hybrid Deep Photonic Spiking Neural Network for Automatic Modulation Recognition

Spiking neural networks (SNNs) possess remarkable capabilities in processing spatial and temporal information. Photonic SNNs, combining the advantages of high-bandwidth and low-latency of photonics, are highly-efficient, high-speed neural processors. However, training a deep photonic SNN, or even a deep SNN, for many complex tasks is challenging. In this work, we propose a hybrid deep photonic SNN (HDPSNN) for the recognition of automatic modulation recognition (AMR). The proposed HDPSNN integrates existing convolutional and long short-term memory layers for feature extraction, SNNs for recognition, and photonic SNN for accelerating the recognition process. In the HDPSNN, a two-layers photonic SNN is demonstrated experimentally using vertical-cavity surface-emitting lasers neurons and Fabry–Pérot laser with an embedded saturable absorber neuron. For the hybrid deep SNN (HDSNN), numerical results show that the maximum accuracy reaches 89.59% with the signal-to-noise ratios (SNRs) of +18dB. Additionally, the inference accuracy reaches 80% for 100 randomly selected samples at SNR of 0dB. For the HDPSNN, experimental results indicate AMR accuracy of 70% for the inference of the same 100 randomly selected samples. Thus, the results shown that the proposed HDPSNN can achieve AMR with minor accuracy loss. These findings suggest a method to combine deep neural network, SNN and photonics to achieve complex tasks in optical SNN. It is significant to pursue ultra-low delay and solutions for complex tasks, design neural structures of photonic SNNs, and realize hardware-algorithm collaborative computing.