Title: Robust End-to-End FSO Transmission with Joint Coding Modulation and BiLSTM-Based Channel Modeling under Atmospheric Turbulence Show Chinese title

Title: 在大气湍流下具有联合编码调制和基于BiLSTM的信道建模的鲁棒端到端自由空间光传输

Abstract: Free space optical (FSO) communication is considered a promising solution in next_generation communication networks. However, its performance is significantly influenced by atmospheric turbulence. To enhance system robustness to turbulence, we propose a turbulence_robust end_to_end FSO communication system (TRFSO) that integrates a data-driven channel model with joint source_channel coding modulation (JSCCM). Specifically, a bidirectional long short-term memory (BiLSTM)_based channel model is developed and trained on data collected over a physical FSO link under varying turbulence conditions. This model accurately captures real_world channel distortions, achieving a minimum Kullback_Leibler (KL) divergence of 0.0019 in amplitude distribution matching. Experimental results show that the TRFSO system trained with the BiLSTM_based channel model outperforms the same architecture trained under the additive white Gaussian noise (AWGN) channel, achieving an average 3.5 dB improvement in multi-scale structural similarity (MS_SSIM) under strong atmospheric turbulence. These results demonstrate the effectiveness of the proposed TRFSO in achieving robust and reliable transmission under dynamic atmospheric turbulence. Show Chinese abstract

Abstract: 自由空间光(FSO)通信被认为是下一代通信网络的一个有前景的解决方案。 然而，其性能显著受到大气湍流的影响。 为了提高系统对湍流的鲁棒性，我们提出了一种湍流鲁棒的端到端FSO通信系统(TRFSO)，该系统结合了数据驱动的信道模型与联合源信道编码调制(JSCCM)。 具体来说，开发并训练了一个基于双向长短期记忆(BiLSTM)的信道模型，该模型在不同湍流条件下的物理FSO链路采集的数据上进行训练。 该模型准确捕捉了现实世界的信道失真，在幅度分布匹配中实现了最小的Kullback-Leibler(KL)散度0.0019。 实验结果表明，使用基于BiLSTM的信道模型训练的TRFSO系统优于在加性高斯白噪声(AWGN)信道下训练的相同架构，在强大气湍流下平均多尺度结构相似性(MS_SSIM)提高了3.5 dB。 这些结果证明了所提出的TRFSO在动态大气湍流下实现鲁棒可靠传输的有效性。