Secure semantic optical communication scheme based on the multi-head attention mechanism

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 × 10 270 . 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.