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China Communications

Blind nonlinearity equalization by machine-learning-based clustering for QAM-based quantum noise stream cipher transmission

Yajie Li, Shoudong Liu, Yongli Zhao, Chao Lei, Jie Zhang

Published Aug 1, 2022• 2 citations
Publisher

Abstract

In quantum noise stream cipher (QNSC) systems, it is difficult to compensate fiber nonlinearity by digital signal processing (DSP) due to interactions between chromatic dispersion (CD), amplified spontaneous emission (ASE) noise from erbium-doped fiber amplifier (EDFA) and Kerr nonlinearity. Nonlinearity equalizer (NLE) based on machine learning (ML) algorithms have been extensively studied. However, most NLE based on supervised ML algorithms have high training overhead and computation complexity. In addition, the performance of these algorithms have a lot of randomness. This paper proposes two clustering algorithms based on Fuzzy-logic C-Means Clustering (FLC) to compensate the fiber nonlinearity in quadrature amplitude modulation (QAM)-based QNSC system, including FLC based on subtractive clustering (SC) and annealing evolution (AE) algorithm. The performance of FLC-SC and FLC-AE are evaluated through simulation and experiment. The proposed algorithms can promptly obtain suitable initial centroids and choose optimal initial centroids of the clusters to achieve the global optimal initial centroids especially for high order modulation scheme. In the simulation, different parameter configurations are considered, including fiber length, optical signal-to-noise ratio (OSNR), clipping ratio and resolution of digital to analog converter (DAC). Furthermore, we measure the Q-factor of transmission signal with different launched powers, DAC resolution and laser linewidth in the optical back-to-back (BTB) experiment with 80-km single mode fiber. Both simulation and experimental results show that the proposed techniques can greatly mitigate the signal impairments.

DOI
10.23919/jcc.2022.08.010

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In quantum noise stream cipher (QNSC) systems, it is difficult to compensate fiber nonlinearity by digital signal processing (DSP) due to interactions between chromatic dispersion (CD), amplified spontaneous emission (ASE) noise from erbium-doped fiber amplifier (EDFA) and Kerr nonlinearity. Nonlinearity equalizer (NLE) based on machine learning (ML) algorithms have been extensively studied. However, most NLE based on supervised ML algorithms have high training overhead and computation complexity. In addition, the performance of these algorithms have a lot of randomness. This paper proposes two clustering algorithms based on Fuzzy-logic C-Means Clustering (FLC) to compensate the fiber nonlinearity in quadrature amplitude modulation (QAM)-based QNSC system, including FLC based on subtractive clustering (SC) and annealing evolution (AE) algorithm. The performance of FLC-SC and FLC-AE are evaluated through simulation and experiment. The proposed algorithms can promptly obtain suitable initial centroids and choose optimal initial centroids of the clusters to achieve the global optimal initial centroids especially for high order modulation scheme. In the simulation, different parameter configurations are considered, including fiber length, optical signal-to-noise ratio (OSNR), clipping ratio and resolution of digital to analog converter (DAC). Furthermore, we measure the Q-factor of transmission signal with different launched powers, DAC resolution and laser linewidth in the optical back-to-back (BTB) experiment with 80-km single mode fiber. Both simulation and experimental results show that the proposed techniques can greatly mitigate the signal impairments.
This letter first reports a digital coherent quantum noise stream cipher (QNSC) experiment over 300km fiber without intermediate amplifiers. With the help of Raman fiber amplifiers, ultra-low loss fiber, and the optimized fuzzy C-means clustering algorithm, the results show the system can carry 10.2 Tbit/ s·km data, which is significantly improved in the single polarization and single wavelength digital coherent context without intermediate amplifiers. We further analyze the transmission and security performances under the 16 quadrature amplitude modulation (QAM) scenario in detail. The number of masked signals of 16 QAM/QNSC is 3843.2.
Abstract Data security is a key feature of future communications networks. Physical layer introduces rich physical mechanisms to increase the complexity of deciphering and provides extensive protection, but faces challenges in compatibility with commercial systems. Quantum noise stream cipher (QNSC) has been proposed as a promising solution to overcome this problem by fusing the stream cryptography regime with the quantum noise masking physical mechanism. However, it has limitations in terms of digital to analog conversion and clock data synchronization of ultra-high-order ciphertext as well as flexible control of masking noise. Here we report a 147.9-Gbps device-compatible quadrature amplitude modulation (QAM) QNSC secure scheme over 75-km fiber. Thanks to delta-sigma modulator, the transmission of 220 × 220-order QAM-QNSC signal are established through the low-order digital signal. We develop a theoretical model for flexibly regulating the transmission rate and security performance. Broadband optical chaos introduces true randomness and acts on the masking noise.
A probabilistic shaping (PS) quadrature amplitude modulation (QAM) based on Y-00 quantum noise stream cipher (QNSC) has been proposed. We experimentally demonstrated this scheme with data rate of 201.6Gbit/s over a 1200-km standard single mode fiber (SSMF) under a 20% SD-FEC threshold. Accounting for the 20% FEC and 6.25% pilot overhead, the achieved net data rate is ∼160Gbit/s. In the proposed scheme, a mathematical cipher (Y-00 protocol) is utilized to convert the original low-order modulation PS-16 (2 2 × 2 2 ) QAM into ultra-dense high-order modulation PS-65536 (2 8 × 2 8 ) QAM. Then, the physical randomness of quantum (shot) noise at photodetection and amplified spontaneous emission (ASE) noise from optical amplifiers are employed to mask the encrypted ultra-dense high-order signal for further improving the security. We further analyze the security performance by two metrics known in the reported QNSC systems, namely the number of masked signals (NMS) of noise and the detection failure probability (DFP). Experimental results show it is difficult or even impossible to extract transmission signals from quantum or ASE noise for an eavesdropper (Eve). We believe that the proposed PS-QAM/QNSC secure transmission scheme has the potential to be compatible with existing high-speed long-distance optical fiber communication systems.
In the era of large models and big data, the security of optical fiber communication backbone networks has garnered significant attention. Quantum noise stream cipher (QNSC) stands as a crucial method for safeguarding the physical layer security of optical fiber communications, yet the current schemes lag behind the rate capabilities of existing 400G optical fiber backbone networks. In this paper, we introduce deep learning into QNSC and propose an end-to-end quantum noise stream cipher (E2E-QNSC) scheme, which encrypts 16 quadrature amplitude modulation (QAM) into E2E-65536QAM/QNSC. Our experiments successfully demonstrate secure optical communication with a single-channel rate of 400 Gbps, a total capacity of 8.4 Tbps, and a transmission distance of 1520 km. Even in the most extreme scenarios, the detection failure probability (DFP) of the scheme remains at an excellent level greater than 0.9999, proving the security of the approach. The experimental results presented herein represent the highest rate-distance product record of QNSC secure transmission systems, to the best of our knowledge.
Quantum noise stream cipher based on quadrature-amplitude-modulation (QAM/QNSC) is a kind of physical layer encryption technology. However, the additional encryption penalty will significantly affect the practical deployment of QNSC, especially in the high capacity and long-haul transmission system. With our research, the encryption process of QAM/QNSC degrades the transmission performance of plaintext information. In this paper, we quantitatively analyze the encryption penalty of QAM/QNSC based on the proposed concept of effective minimum Euclidean distance. We calculate the theoretical signal-to-noise ratio sensitivity and encryption penalty of QAM/QNSC signals. A modified feedforward pilot-aided two-stage carrier phase recovery scheme is used to reduce the effect of laser phase noise and the encryption penalty. Experimental results achieve single-channel 205.9 Gbit/s 640km transmission with single carrier polarization-diversity-multiplexing 16-QAM/QNSC signal.
We propose and experimentally study a novel, to the best of our knowledge, quantum noise stream cipher (QNSC) secure transmission scheme based on the delta-sigma modulation (DSM) technique. The cooperation of the QNSC and DSM mechanisms makes it possible to transmit an ultrahigh-order encrypted signal in the non-return-to-zero (NRZ) on-off keying (OOK) format. The delivery of the NRZ OOK waveform over the fiber link allows us to send and receive signals using digital ports, instead of high-speed and high-resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) in conventional QNSC systems. Meanwhile, clock synchronization can be achieved by using a simple clock data recovery algorithm. The extra clock signal transmission link in conventional QNSC systems is no longer needed. The proposed scheme is also compatible with wavelength division multiplexing (WDM) systems. In this work, 4 × 12.9 Gbit/s plaintext is encrypted to a 65,536-level QNSC signal and then transmitted over a 10-km standard single-mode fiber. The transmitter and receiver are established by commercial 100G QSFP28 optical modules with clock data recovery. This proposed scheme can be easily deployed in commercial systems due to its minimalist implementation architecture and relatively low hardware cost.