Xu Zhang

Fiber-optic networks are the data transmission foundation of modern communications. Traditional C-band transmission can no longer meet the escalating communication demands, prompting the commercialization of C+L-band fiber-optic networks as a solution. Accurate modeling of physical-layer impairments (PLIs) is crucial for optimizing the performance of backbone fiber-optic networks. The widely-applied inter-channel stimulated Raman scattering-Gaussian noise (ISRS-GN) model has been developed to analytically estimate the accumulation of noise considering the ISRS effects during C+L-band signal transmission in a state-dependent manner. However, this model assumes that signals have rectangular spectra, leading to significant estimation errors when this assumption is violated. In practice, signal spectra are not rectangular; they are typically root-raised-cosine-shaped at transmission and are affected by various channel impacts, such as filtering effects. To address this issue, we propose the C+L-band component-wise Gaussian noise (C+L-CWGN) PLI model that accounts for arbitrarily spectral-shaped demands. The C+L-CWGN model is computationally efficient and suitable for most network management approaches. Our results indicate that the C+L-CWGN model can reduce PLI overestimation by more than 27.1% in typical scenarios.

Optical fiber communication networks are the backbone of information transmission, carrying over 90% of the world’s data. However, optical fibers are susceptible to fiber-bending eavesdropping attacks, which can result in massive data leaks. The detection of fiber-bending eavesdropping attacks has become one of the effective strategies to safeguard data security. In this paper, we propose a detection scheme based on a convolutional neural network (CNN) for detecting fiber-bending eavesdropping attacks in dual-polarization coherent optical communication systems. To validate the feasibility and effectiveness of the proposed scheme, we first establish the relationship between the fiber-bending radius and the bending power loss coefficient. Based on this relationship, a 100 km, 25 GBaud dual-polarization 16 quadrature amplitude modulation (16QAM) coherent optical communication system is constructed. This system collects both normal polarization data and bending eavesdropping polarization data. Subsequently, the performance of the CNN algorithm in detecting fiber-bending eavesdropping attacks is evaluated. The results show that under bending radii of 10.8 mm, 12.1 mm, 15 mm, and mixed bending radii, the eavesdropping detection accuracy rates of the CNN reach 99.8%, 99.67%, 99.22%, and 96.15%, respectively.

The photonic firewall is a device that detects and localizes risky information at the optical layer to enhance the security of optical networks. The most significant and challenging component of the photonic firewall is the all-optical pattern matching system, which determines its overall performance. In response to the explosion of data volume, it is crucial to develop reconfigurable all-optical pattern matching systems capable of handling signals in arbitrary high-order modulation formats. All-optical pattern matching systems based on optical real-time Fourier transform(RTFT) and conjugate multiplication are proposed for phase-shift keying (PSK) optical signals. Numerical simulations demonstrate that the system is capable of matching 24-bit target sequences within 528-bit source sequences, which are signals in either QPSK or 8PSK modulation formats, at a transmission rate of 260 GBaud.

Optical time domain reflectometer (OTDR) is a key device for diagnosing the health of optical fiber links. The backscattered signals it relies on are inevitably affected by noise during transmission, especially when the signal is weak and severely disturbed, making it difficult to identify events in the test curve. Consequently, filtering out noise from these signals to facilitate subsequent fault localization and event recognition has emerged as a critical problem to solve. To address this issue, a novel OTDR signal denoising method has been proposed, combining an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) with non-local means (NLM) filtering. This approach classifies the intrinsic mode functions (IMFs) components derived from ICEEMDAN based on their sample entropy values, reconstructs the divided signal components, and further filters out high-frequency noise components using the NLM algorithm, to complete the effective denoising of the OTDR signal. Comparative analyses through simulations and practical examples show that this method out-performs traditional denoising techniques like soft and hard wavelet thresholding, improved wavelet thresholding, NLM, and ICEEMDAN-improved wavelet thresholding in terms of denoising effectiveness. The proposed method has significant improvements/reductions in signal-to-noise ratio (SNR)/root mean square error (RMSE) and provides a new approach for noise reduction in OTDR signal.

This paper aims to tackle the issue of risk-avoid lightpath provision in multi-domain space division multiplexing (SDM) elastic optical data center networks (SDM-EODCNs). The service requests are categorized as intra-domain or inter-domain, and inter-domain requests are further classified as trusted or untrusted. A system model is presented, including a network model, a service request model, an inter-core crosstalk (IC-XT) model, and a risk model between service requests. To reduce the blocking probability of service requests while avoiding security risk, an integer linear programming (ILP) model is formulated to minimize the maximum index of frequency slots occupied by all types of service requests. Furthermore, a risk-avoid lightpath provision algorithm (RALPA) is proposed, consisting of different algorithms like data center selection, core prioritization, frequency block determination, and core and frequency slot allocation. RALPA ensures that trusted service requests remain secure from potential attacks by untrusted ones. Additionally, core priorities are dynamically generated for different service requests, reducing the search space and improving the search's time efficiency. Finally, the ILP model is solved in small-scale topology, and large-scale simulation experiments are conducted on various network topologies and core structures. The results show that our RALPA algorithm outperforms the benchmark algorithms in terms of blocking probability and utilization of network resources.

Multi-access Edge Cloud (MEC) networks are powerful for providing emerging computation-intensive and latency-sensitive applications with low latency leveraging ubiquitous edge devices. These networks enable complex applications to be split into multiple components/subtasks and deployed among multiple edge servers with limited computation and communication resources. However, multiple subtasks within an application are dependent on each other. They cannot be executed in parallel, resulting in non-trivial resource waste when allocating resources to every subtask throughout the lifetime of the application. This paper investigates the multi-component task offloading problem in MEC networks that addresses the dependencies among components and three-dimensional (3D) resource allocation, i.e., computation, communication, and time slots. The problem is NP-hard and challenging to solve due to the complex task dependencies, including triangular dependencies among multiple subtasks and the routing of edges between dependent subtasks. To address the challenge, we first propose a non-destructive task reconfiguration algorithm that transforms a task call graph into multiple sequential layers, breaking out the triangular dependency. Then, we develop a de P endency-awa R e task offlo A ding algorithm w I th ta S k r E configuration ( PRAISE ) algorithm to maximize the total offloading benefit. PRAISE decouples the original problem into task offloading and 3D convex resource optimization. Simulation results show that PRAISE outperforms baselines with higher system benefits and lower resource costs.

In the large-scale network environment, there are still problems that the factors considered are not comprehensive enough and the results are not accurate enough when searching for critical vulnerabilities. To improve the accuracy and comprehensiveness of critical vulnerable node discovery and optimize the storage space of the attack graph, this paper proposes a method of critical vulnerable node discovery based on Bayesian attack subgraphs and double thresholds. Firstly, the network is divided into multiple sub-networks by the community division algorithm. The vulnerability nodes are constructed according to the host vulnerabilities, and the Bayesian attack subgraph is constructed by quantifying the vulnerability nodes. Then, the attack paths inside the Bayesian attack subgraphs are searched and the attack probability of attack paths is calculated. Next, the attack path information in each Bayesian attack subgraph is integrated to calculate the complete path attack probability. Finally, the double thresholds are set based on the complete attack path information to realize the discovery of critical vulnerable nodes. Experiments show that the proposed method can search for the critical vulnerable nodes more accurately and optimize the storage space of the attack graph.