Accurate vibration recognition enabled by synergistic sensing based on both phase and SOP for stable IM-DD optical interconnects

With the recent advancements in large language models, high-speed optical interconnects are required between multiple servers in data centers to support model training. The stability of optical interconnects is critical to the model training. Integrated sensing and communication over fiber enable the effective utilization of existing communication fibers for vibration sensing, thereby achieving intelligent operation and maintenance of optical networks. Compared to coherent systems, vibration sensing and event recognition integrated into intensity-modulation and direct-detection (IM-DD) systems are still in the early stages, and the recognition accuracy is limited by the singular sensing source. In this paper, we propose a sensing hardware system that simultaneously monitors vibration-induced state of polarization (SOP) and phase variations for IM-DD systems. By fusing the phase-based and SOP-based sensing features using a software convolutional neural network (CNN) model, accuracy enhancement in vibration event recognition is achieved. In the hardware system, 10% of the received optical signal is tapped for sensing. This tapped portion is then split, with 90% allocated to phase-based sensing using an unbalanced Mach–Zehnder interferometer with a 3 × 3 coupler for fading-free phase demodulation and the remaining 10% to SOP-based sensing utilizing a polarization beam splitter (PBS) for SOP variation monitoring. Within the software CNN model for vibration recognition, the SOP-based and phase-based sensing features are concatenated along the channel dimension and adaptively fused using a channel attention mechanism. Experimental results demonstrate that the proposed scheme achieves vibration sensing within a frequency range of 100 Hz to 5 kHz, even at an extremely low received optical power (ROP) of -20 dBm. Furthermore, the model with the fusion of SOP-based and phase-based features improves the average event recognition accuracy by 3% compared to using the SOP data alone and by 14.8% compared to relying solely on the phase data. The recognition accuracy improvement arises from the complementarity of phase-based and SOP-based sensing features under different vibration events. This accurate vibration recognition can effectively improve link maintenance efficiency and promises to enhance the reliability of optical networks.