基于量子噪声随机加密的抗截获传输方案
Communications Technology(2023)
中国人民解放军 31155 部队 | 中国人民解放军陆军工程大学 | 中国人民解放军 32046 部队 | 中国人民解放军61623部队
Abstract
为解决光缆网端到端安全传输问题,提出了一种基于量子噪声随机加密(Quantum Noise Randomized Cipher,QNRC)的抗截获传输方案.首先介绍了QNRC的加密原理和实现方法;其次采取光域解密、相干检测的方法,通过实验实现了 50 km传输距离下 10 Gbit/s速率的PSK-QNRC安全传输系统.实验结果表明,所提方案具有较强的可行性,能够保证合法收发双方以较高的安全性完成高速传输.
MoreTranslated text
Key words
quantum noise randomized cipher,anti-interception transmission,decryption in optical domain,optical fiber communication
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest