Optical Fiber Communication System Based on Intelligent Joint Source-Channel Coded Modulation
Hongyu Huang, Liming Cheng, Zhenming Yu, Wei Zhang, Yueqiu Mu, Kun Xu
Abstract
To improve the information transmission ability of point-to-point optical fiber communication systems, we propose and experimentally demonstrate an optical fiber communication system based on intelligent joint source-channel coded modulation (JSCCM-OFC). Instead of encoding information into bits by source coding and employing channel coding, the JSCCM-OFC system codes and modulates the information source into discrete-time analog symbols jointly using deep learning. The generated discrete-time analog symbols are then directly transmitted through an optical fiber. We designed language attention and dual-attention residual networks as the JSCCM for text and image transmission, respectively. For interaction between the information layer and the optical physical layer, we incorporated a convolutional neural network into the joint demodulation and decoding (JDD) network and implemented joint optimization in the receiver. Compared with the bit-based structure, the JSCCM-OFC system achieved higher information compression and obtained a more stable performance, especially in the low-received optical power regime. Moreover, JSCCM enhanced the robustness of the system against optical link impairments.