计算机科学
太比特
加密
可扩展性
传输(电信)
数据传输
计算机网络
波分复用
频道(广播)
多路复用
光谱效率
安全通信
电信
波长
光学
物理
数据库
作者
Zekun Niu,Yunhao Xie,Guozhi Xu,Chenhao Dai,Hang Yang,Chuyan Zeng,Minghui Shi,Lyv Li,Guoqing Pu,Weisheng Hu,Lilin Yi
摘要
Abstract As we enter the big data and artificial intelligence (AI) era, integrating security and communication over optical fibre has become a critical challenge. This urgency is driven by the need to protect vast amounts of sensitive data, ensuring privacy security across global high-capacity optical networks. Traditional secure communication methods often struggle to maintain high-capacity transmission performance while providing robust security. Here we propose an integrated encryption and communication (IEAC) framework, designed to maximize mutual information (MI) for legal users while minimizing it for potential eavesdroppers. Enabled by the end-to-end deep learning, this holistic framework trains a random number-selected geometric constellation shaping scheme to optimize encryption processes and transmission quality simultaneously. The IEAC experiment system achieves a groundbreaking single-channel transmission rate of 1 Terabit per second (Tb/s) over a 1200-km fibre link, employing a 26-channel, 3.9 THz bandwidth, full C-band wavelength division multiplexing (WDM) configuration. The MI for eavesdropper is under 0.2 bit per symbol where the regular value is near 4.0, ensuring the secure transmission. The IEAC scheme offers a scalable, promising solution to meet the escalating demand for high-throughput, secure data transmission in the face of advancing big data and AI computational technologies.
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