A 7D Cellular Neural Network Based OQAM-FBMC Encryption Scheme for Seven Core Fiber
Shuaidong Chen, Bo Liu, Jianxin Ren, Yaya Mao, Rahat Ullah, Xiumin Song, Yu Bai, Lei Jiang, Shun Han, Jianye Zhao, Yibin Wan, Xu Zhu, Jiajia Shen
Abstract
This paper proposes a 7-dimensional ( <xref ref-type="disp-formula" rid="deqn6-deqn7" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7D</xref> ) Cellular Neural Network (CNN) based offset quadrature amplitude modulation filter bank multicarrier (OQAM-FBMC) encryption scheme for seven core fiber. The chaotic sequences generated by 7D CNN are applied to produce the masking vectors to encrypt the phase, carrier frequency, and time. In order to verify the performance of the encryption scheme, 70 Gb/s (7×10 Gb/s) encrypted OQAM-FBMC signal transmission over 2 km 7 core fiber is experimentally demonstrated. The key space of 7D CNN can reach 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1575</sup> and the scrambling degree can be maintained at 100% regardless of the number of symbols. The experimental results also show that when some keys are compromised, the system's bit error rate (BER) can still reach above 0.46, which effectively ensures the security of the system. Due to its good performance in security, the proposed scheme has important application prospects in future optical access network.