The fronthaul network is an essential way to improve the comprehensive performance of beyond fifth generation (B5G) communication network. Space-wavelength division multiplexing (SWDM) can effectively improve its capacity, and quantum key distribution technique can provide unconditional information security for it. In quantum secured SWDM B5G fronthaul network, quantum signals are affected by noises generated by classical signals, such as spontaneous Raman scattering, four-wave mixing and inter-core crosstalk. Evaluating these noises in real-time will increase the time delay of the whole network. In this paper, we propose two machine learning (ML) models (XGBoost and LightGBM) to predict these noises. Simulation results show that the ML models can reduce the noise evaluation time by up to 98.8%. Besides, the available channel predicting accuracy rate is close to 100%. The minimum noise predicting accuracy rate increases with the increasement of the channel occupancy rate and can reach 100% when channel occupancy is higher than 80%.
The fronthaul network is an essential way to improve the comprehensive performance of beyond fifth generation (B5G) communication network. Space-wavelength division multiplexing (SWDM) can effectively improve its capacity, and quantum key distribution technique can provide unconditional information security for it. In quantum secured SWDM B5G fronthaul network, quantum signals are affected by noises generated by classical signals, such as spontaneous Raman scattering, four-wave mixing and inter-core crosstalk. Evaluating these noises in real-time will increase the time delay of the whole network. In this paper, we propose two machine learning (ML) models (XGBoost and LightGBM) to predict these noises. Simulation results show that the ML models can reduce the noise evaluation time by up to 98.8%. Besides, the available channel predicting accuracy rate is close to 100%. The minimum noise predicting accuracy rate increases with the increasement of the channel occupancy rate and can reach 100% when channel occupancy is higher than 80%.
To satisfy the urgent requirements of security for fifth generation (5G) fronthaul network, quantum key distribution (QKD) technique is taken into consideration as a promising way. In this paper, we first propose an architecture for quantum security beyond fifth generation (B5G) fronthaul optical network, which merges facilities of QKD into the architecture of existing 5G fronthaul network, enabling quantum signals to be transmitted with classical signals within the same fiber. Secondly, theoretical model analysis of interference with quantum signals caused by four wave mixing noise is performed. Moreover, the theory of secure key rate calculation under noise interference is introduced. Lastly, the QKD performance in the proposed architecture is evaluated, which leads to the conclusion that even when the quantity of B5G fronthaul optical channels for classical signals reaches up to 12, the secure transmission distance of QKD is still beyond 20km, which satisfies the demand of B5G fronthaul optical network.
Amidst the development of communication technologies, the limitations of 5G communication systems, particularly in security and resource management, have become increasingly apparent. As the 6G era approaches, there are growing demands for integrated systems that ensure robust security and optimize resource utilization. To address these challenges, this paper proposes a novel architecture that integrates classical communication channels and quantum key distribution (QKD) channels using wavelength-division multiplexing (WDM). This innovative approach allows classical and quantum channels to coexist within a single optical fiber, improving security and fiber utilization while reducing system complexity and cost. A key contribution of this work is introducing an AI-based prediction and management module, dynamically adjusting the quantum key generation rate according to real-time usage and forecasted demand. By leveraging machine learning, the module predicts quantum key consumption patterns, ensuring that the key generation rate aligns with the system’s security and performance requirements. This approach balances key generation and usage, minimizing resource waste and enhancing the system’s overall efficiency. The feasibility of the proposed architecture is validated through an analysis of spontaneous Raman scattering (SpRS), showing the architecture maintains high key generation rates and low quantum bit error rates (QBER) under optimal system conditions. Furthermore, an analysis reveals that the AI module meets the requirement of predicting key usage and managing the key generation rate. Lastly, the feasibility is further validated by an experiment. Overall, this study presents a comprehensive framework for integrating classical and quantum channels in future 6G networks, providing enhanced security, efficiency, and scalability.
Integrating quantum key distribution (QKD) with existing optical networks is highly desired to reduce the deployment costs and achieve efficient resource utilization, and some point-to-point transmitting experiments have verified its feasibility. Nevertheless, there are still many problems in the realistic scenario where QKD coexists with dynamic data traffics. On the one hand, the conventional static channel allocation schemes cannot guarantee the quality of quantum channels in the presence of the time-varying noises. On the other hand, considering the complex noise generation caused by dynamic classical data traffics with variable characters, it is challenging to achieve online high-performance quantum channel assignments. To address these problems, we propose a machine learning based noise-suppressing channel allocation (ML-NSCA) scheme. In this scheme, the LightGBM based ML framework is trained to predict the optimal channel allocations with lowest noise impacts, according to which, the quantum channels are periodically reallocated to guarantee high secure key rate. To improve the accuracy and scalability of the ML framework, we also optimize the method of feature extraction during the training process. The performance evaluation results indicate that the proposed scheme can effectively resist the dynamic noise impacts in the realistic optical networks and obtain higher secure key rate with less operation complexity than the previous schemes.
In this paper, we propose a coupled theoretical model used for noise processing at nodes of hybrid core fiber with single core fiber (SMF)-multicore fiber (MCF), and intra-core noises and inter-core noises are considered in the model. Then, we propose an on-demand quantum secured future fronthaul architecture and a channel allocation algorithm over hybrid core fibers, and the proposed coupled theoretical model is used to calculate the noises in architecture. For fronthaul optical signals, wavelength modes and the number of transceiver modules in the distributed unit (DU) can be dynamically adjusted according to requirements, and the function of load aggregation is adopted. For quantum key distribution (QKD), quantum signals are transmitted in a dedicated core, and wavelength-time multiplexing is adopted to increase the number of quantum secured base station (BSs) while improving wavelength utilization. Finally, simulation results show that proposed channel allocation algorithm can suppress noise about 60% compared to First-Fit. The architecture can save up to 83.3% modules in 7-core feeder fiber, and it is feasible to support 128 BSs. The experimental and simulation results show that the proposed coupled theoretical model matches well with the experimental results, and the quantum bit error rates in experiment are both lower than 2.5%. The results show that the architecture has the advantages of allocating time-space-frequency resources on-demand, saving power consumption, and expanding the number of users. Therefore, it provides a feasible solution for future optical fronthaul networks secured by QKD.
This study investigate the integration of discrete modulated coherent state (DMCS) continuous variable quantum key distribution (CV-QKD) with wavelength division multiplexing (WDM) in 5G fronthaul optical networks. Firstly, an architecture of quantum-classical co-propagation in C-band DWDM-based 5G fornthaul network is constructed and the wavelength assignment scheme for classical/quantum channels is designed. By assigning the quantum channels in band of low Raman scattering coefficient and setting the quantum-classical channel space to be 200 GHz, the proposed scheme can effectively reduce the spontaneous Raman scattering (SpRS) and four-wave-mixing (FWM) noise. Then, the secret key rate (SKR) of DMCS protocol and the excess noise induced by SpRS are analyzed theoretically to furtherly character the co-propagation architecture. Lastly, numerical simulation is carried out to evaluate the impact of SpRS noise on the CV-QKD performance. The simulation results show that by employing the proposed wavelength assignment scheme, the co-propagation system can support positive SKR at a maximum distance of approximately 41 km for 8 classical channels use and 30 km for 16 classical channels use, respectively. Our work can provide a reference for practical deployment of quantum secured 5G fronthaul optical networks.
The Industrial Internet of Things (IIoT) is reshaping modern industries by enabling interconnected, intelligent systems that support real-time monitoring, predictive maintenance, and automated control. However, the growing complexity and scale of IIoT networks expose them to increasingly severe cybersecurity threats that traditional methods struggle to address. To meet these challenges, this paper proposes a novel architecture that integrates Quantum Key Distribution (QKD) with passive Wavelength Division Multiplexing (WDM) technology to secure 5G fronthaul communications between Active Antenna Units (AAUs) and Distributed Units (DUs). In this architecture, AAUs and DUs act as quantum communication entities, exchanging secure keys over optical fibers by allocating quantum and classical signals across different wavelengths. This hybrid approach not only enhances bandwidth efficiency but also significantly improves resistance to eavesdropping and tampering, which are critical vulnerabilities in IIoT environments. To evaluate system performance, simulations were conducted by varying key parameters, including attenuation coefficient, detection efficiency, and dark count rate. Simulation results demonstrate that lowering attenuation coefficients and dark count rates substantially improves the secure key generation rate and reduces the quantum bit error rate (QBER), while increased detection efficiency, although beneficial for signal acquisition, introduces trade-offs by potentially raising the QBER under noisy conditions. Figures presented provide clear illustrations of these trends. By merging quantum communication into IIoT frameworks, this study offers a forward-looking, scalable solution that ensures data confidentiality, integrity, and high-speed reliable communication, paving the way for the secure evolution of industrial networks in the era of Industry 4.0.