Free space optical (FSO) communication is a promising solution for next-generation communication networks. Atmospheric turbulence, however, severely degrades its performance. We thus propose a novel turbulence-robust end-to-end FSO communication system (TRFSO) that performs joint training across the entire transmission process from source to transmission channel to destination. To introduce physical FSO channel impairments into the training loop, we developed a bidirectional long short-term memory (BiLSTM)-based FSO channel model. Through end-to-end joint training, the system achieved high quality and robust transmission under dynamic atmospheric turbulence. Trained on experimental data collected over a physical FSO link under varying turbulence conditions, the proposed model accurately reproduced real-world channel distortions, achieving a minimum Kullback–Leibler (KL) divergence of 0.0019 nats in amplitude distribution matching. Our experimental results revealed that TRFSO significantly outperformed conventional separate coding modulation schemes in FSO links. Moreover, under strong turbulence, TRFSO achieved a 3.5 dB gain in average multi-scale structural similarity (MS-SSIM) compared with the same network architecture trained without the channel model.

Achieving efficient point cloud (PC) transmission is a fundamental requirement for immersive holographic-type communication. However, traditional optical fiber communication (TOFC), based on separated coding modulation for PC transmission, faces challenges related to massive data transmission and heavy computational resource requirements. To achieve lightweight and efficient PC transmission, we propose and experimentally demonstrate a joint coding-modulation optical fiber communication system for PC transmission (JCMPC). A joint encoding-modulation (JEM) network based on 3D convolution is designed to encode the PC into symbols for transmission directly. At the receiver, a joint decoding-demodulation (JDD) network is used to reconstruct the signals transmitted through the communication channel into the received PC. The experimental results indicate that the proposed JCMPC outperforms transmission schemes based on separate coding modulation and exhibits gradual performance degradation with the deterioration of channel conditions. We evaluate the decoding computational complexity of our proposed JCMPC scheme against the separate transmission schemes using Geometry-based Point Cloud Compression (G-PCC) and Low-Density Parity-Check (LDPC) codes. The results demonstrate that JCMPC reduces the decoding computational operations by over 80% compared to G-PCC+LDPC.