Integration of FBG Reflection and Transmission Spectra for Sensing Capacity Enhancement With Transformer CNN Demodulation

We present a novel FBG sensor system that employs both reflective and transmission wavelengths to significantly boost capacity within a single network. By integrating Free Space Optics (FSO) for remote communication, this architecture operates beyond physical constraints where laying fiber is not feasible, thereby enhancing deployment flexibility. To address the overlap of Bragg wavelengths commonly encountered in dense FBG networks, we utilize a Transformer Convolutional Neural Network (CNN) that leverages positional encoding to more effectively learn data patterns. Preliminary results demonstrate a significant enhancement in sensor capacity and detection accuracy. Moreover, the proposed Transformer CNN reduces both training and inference times by approximately 2.24× and 1.43×, respectively, compared to traditional Transformer models. By leveraging both reflective and transmission FBG readouts within a fixed spectral window, the system supports nearly double the number of sensors, offering a scalable and efficient solution well-suited for high-density, real-time industrial sensing applications.