Ma Yilan, Ren Jianxin, Liu Bo, Mao Yaya, Wu Xiangyu, Chen Shuaidong, Ma Yiming, Jiang Lei, Wu Mengjie, Zhao Nan, Zhang Juntao, Wu Yongfeng, Ullah Rahat
Opt Lett. 2023 Aug 15;48(16):4408-4411. doi: 10.1364/OL.498997.
In this paper, an artificial-intelligence-based secure semantic optical communication scheme is proposed. The semantic features of the original text information are extracted using Transformer. Compared with other networks, Transformer reduces the complexity of the structure and the associated training cost by using the multi-head attention mechanism. To solve the security problem, the encryption scheme is applied to an orthogonal frequency division multiplexed passive optical network (OFDM-PON). The proposed scheme applies chaotic sequences to produce masking vectors. We encrypt the constellation and frequency, achieving a large key space of 1 × 10. To prove that Transformer can effectively extract the semantic features of text, we have computed the values of ROUGE-1, ROUGE-2, and ROUGE-L, which are 40.9, 18.02, and 37.17, respectively. An encrypted 16 quadrature amplitude modulation (16QAM) OFDM signal transmission over a 2 km seven-core fiber with a data rate of 78.5 Gbits/s was experimentally demonstrated. During the experiments, the bit error rate (BER) was analyzed and the results show that the proposed system improves efficiency and security in an OFDM-PON system.
本文提出了一种基于人工智能的安全语义光通信方案。使用Transformer提取原始文本信息的语义特征。与其他网络相比，Transformer通过使用多头注意力机制降低了结构的复杂度和相关的训练成本。为了解决安全问题，将加密方案应用于正交频分复用无源光网络（OFDM-PON）。所提出的方案应用混沌序列来生成掩码向量。我们对星座图和频率进行加密，实现了1×10的大密钥空间。为了证明Transformer能够有效提取文本的语义特征，我们计算了ROUGE-1、ROUGE-2和ROUGE-L的值，分别为40.9、18.02和37.17。通过实验演示了在2 km七芯光纤上以78.5 Gbit/s的数据速率传输加密的16正交幅度调制（16QAM）OFDM信号。在实验过程中，分析了误码率（BER），结果表明所提出的系统提高了OFDM-PON系统的效率和安全性。
