  Following the explosive growth of global data, there is an ever-increasing
demand for high-throughput optical fiber communication (OFC) systems to perform
massive data transmission and processing. Existing OFC methods mainly rely on
electronic circuits for data processing, which severely limits the
communication throughput. Though considered promising for the next-generation
high-speed fiber communication, all-optical OFC remains unachievable due to
serious challenges in effective optical computing, system modeling and
configuring. Here we propose an end-to-end photonic encoder-decoder (PED)
processor which maps the physical system of OFC into an optical generative
neural network. By modeling the OFC transmission process as the variation in
the constructed optical latent space, the PED learns noise-resistant coding
schemes via unsupervised optimization. With multi-layer parametric diffractive
neural networks, the PED establishes a large-scale and high-throughput optical
computing framework that integrates the main OFC computations including coding,
encryption and compression to the optical domain. The whole system improves the
latency of computation in OFC systems by five orders of magnitude compared with
the state-of-the-art device. On benchmarking datasets, the PED experimentally
achieves up to 32% reduction in transmission error ratio (ER) than on-off
keying (OOK), one of the mainstream methods with the lowest ER in general
transmission. As we demonstrate on medical data, the PED increases the
transmission throughput by two orders of magnitude than 8-level pulse amplitude
modulation (PAM-8). We believe the proposed photonic encoder-decoder processor
not only paves the way to the next-generation all-optical OFC systems, but also
promotes a wide range of AI-based physical system designs.
