Bowen Bao

Routing, band, modulation-format, and spectrum assignment (RBMSA) under quality-of-transmission (QoT) constraints is a challenging problem in C+L-band elastic optical networks (EONs) because discrete allocation decisions are tightly coupled with dynamic spectrum occupancy and physical-layer impairments (PLIs). Many existing deep reinforcement learning (DRL)-based RBMSA methods handle QoT constraints through post hoc verification and rely on handcrafted, single-scale spectrum features, limiting their ability to learn QoT-feasible and fragmentation-aware allocation policies. To address these issues, we propose a deep Q-network (DQN)-based QoT-aware RBMSA framework with multi-scale spectrum representation for C+L-band EONs. The method incorporates generalized signal-to-noise ratio (GSNR)-related physical-layer information and Haar wavelet-based multi-scale spectrum features into the state space and integrates modulation-format selection into the action space, enabling QoT-aware joint path, band, and modulation-format decision-making. In addition, a dynamic reward-centering mechanism is incorporated to reduce reward-mean shift and improve DQN training stability under dynamic traffic. Simulation results show that, under high-load scenarios, the proposed method reduces the blocking probability by up to 86.27% and 43.99% compared with representative heuristic and DRL-based baselines, respectively, while alleviating spectrum fragmentation and improving the average GSNR of established lightpaths.

With the rapid evolution of emerging technologies, such as artificial intelligence, large-scale data centers, and 5G/6G communication systems, network traffic demands have increased significantly in both volume and asymmetry. Space-division multiplexing elastic optical networks (SDM-EONs), enabled by the abundant resources provided through multi-core fiber transmission, have been widely recognized as an effective solution for supporting high-capacity optical networks. By exploiting the inherent resource advantages of multi-core fiber architectures, SDM-EONs offer a scalable approach to capacity expansion. However, in SDM-EON environments, inter-core crosstalk (XT) and spectrum fragmentation introduce non-negligible signal impairments and degrade network performance. To address these issues, this paper proposes a bidirectional XT-aware impact (BXI) to evaluate the XT effects under bidirectional transmission. Based on this metric, a fragmentation measure is further incorporated to develop a bidirectional crosstalk- and fragmentation-aware metric (BCFM). Meanwhile, to ensure the quality of transmission, a bidirectional optical signal-to-noise ratio (OSNR) impairment evaluation model is introduced. Building upon these components, a novel, to our knowledge, bidirectional crosstalk- and fragmentation-aware (BCFA) resource allocation scheme is finally formulated. Based on bidirectional core assignment, the BCFA scheme jointly considers the impacts of XT and spectrum fragmentation to improve overall resource utilization, in which a novel, to our knowledge, bidirectional crosstalk and fragmentation-aware metric is defined. This metric quantitatively evaluates the influence of adjacent links on a given lightpath under bidirectional transmission conditions. The simulation results demonstrate that the BCFA scheme achieves substantial reductions in blocking probability with higher spectrum utilization.

With the development of services such as video streaming and cloud computing, the demand for network capacity and quality of transmission would be gradually increased. Space-division multiplexing elastic optical networks (SDM-EONs) provide a promising approach to support the greater capacities of networks due to their sufficient resources with multi-core transmission existence. Unfortunately, inter-core crosstalk results in significant degradation of spectrum resource utilization and quality of transmission in SDM-EONs. To avoid this dilemma, this paper focuses on the crosstalk mitigation benefit of counter-propagation between the adjacent cores and presents a crosstalk impact fragmentation-aware metric (CIFA) under bidirectional spectrum where the state of spectrum slots is defined as unoccupied (“0”) and occupied (“1” for positive and “−1” for opposite transmissions). In this metric, the crosstalk impact on adjacent cores reflects the utilization of counter-propagation, and the fragmentation is considered to estimate spectrum utilization. Furthermore, benefiting from the presented metric, this paper proposes a bidirectional-propagation-based crosstalk-avoided spectrum-efficient resource allocation (BCSRA) scheme in SDM-EONs, which aims to avoid the crosstalk and fully utilize the spectrum resources in networks. In this scheme, the algorithm assigns the lightpath with the minimum CIFA to the incoming request, which minimizes the impact on future requests. Simulation results show that the proposed BCSRA scheme can effectively reduce the blocking probability and enhance resource utilization while avoiding inter-core crosstalk in SDM-EONs.

Hollow-core anti-resonant fibers (HC-ARFs) serve as ideal waveguides for terahertz wave transmission. To address the low computational efficiency of the traditional finite element method (FEM) in the multi-parameter optimization of HC-ARFs, we propose what we believe to be a novel sequential decision network (SDNet) for predicting the performance of nested terahertz HC-ARFs within the frequency range of 0.5 to 1.5 THz. The model integrates the decision-making principles of the random forest algorithm with the advantages of deep learning neural networks. By utilizing the fiber structural parameters and operating frequency as inputs, SDNet efficiently predicts both confinement loss (CL) and dispersion, classifying each into six distinct levels. Experimental results demonstrate that SDNet achieves prediction accuracies of 94.3% for CL and 97.7% for dispersion, significantly outperforming traditional machine learning algorithms such as Random Forest, K-NN, and Decision Tree. Additionally, analysis of confusion matrices demonstrates SDNet's superior ability in distinguishing between easily confusable categories. This work overcomes the previous limitations of predicting CL only at the 1.55 μm wavelength by extending the analysis to a broader terahertz frequency range and incorporating dispersion classification predictions, thus providing an efficient and accurate approach for the multi-parameter collaborative optimization of HC-ARFs.

Space division multiplexing elastic optical networks (SDM-EONs) is a promising solution to enhance the transmission capacity of optical networks. However, physical layer impairments (PLIs) degrade the service's quality of transmission (QoT). Additionally, service-differentiated spectrum demand leads to spectrum fragmentation, which is further aggravated by strict PLI constraints. Therefore, we propose a routing, core and spectrum allocation algorithm based on dynamic quantitative impairments for low fragmentation (QISEN-LowSF) in SDM-EONs. This algorithm quantitatively analysis multiple impairments and designs restrictive condition to weaken the fragmentation. Specifically, we design a path selection mechanism that takes transmission distance and integration degree of path resources as the cost metrics. Then, we abstract multiple impairment effects in core, and design a core selection mechanism at the cost of impairment estimation and resource integration of each core. When spectrum is allocated to the service, we design a reduced fragmentation spectrum allocation strategy based on the combination of quantified impairment with low-fragmentation. We also optimize the service impairment estimation model in the QoT calculation phase. The results show that QISEN-LowSF performs better than the comparison algorithms under different traffic loads. From the above, the QISEN-LowSF algorithm has superior dynamic flexibility and can reduce blocking probability and the resource fragmentation. Further, it can effectively handle various traffic loads.

Given the spring-up of diverse new applications with different requirements in metro optical networks, network slicing provides a virtual end-to-end resource connection with customized service provision. To improve the quality-of-service (QoS) of slices with long-term operation in networks, it is beneficial to reconfigure the slice adaptively, referring to the future traffic state. Considering the busy-hour Internet traffic with daily human mobility, the tidal pattern of traffic flow occurs in metro optical networks, expressing both temporal and spatial features. To achieve high QoS of slices, this paper proposes a node-oriented slice reconfiguration (NoSR) scheme to reduce the penalty of slices, where a gradient-based priority strategy is designed to reduce the penalties of slices overall penalties in reconfiguration. Besides, given that a precise traffic prediction model is essential for efficient slice reconfiguration with future traffic state, this paper presents the model combining the graph convolutional network (GCN) and gated recurrent unit (GRU) to extract the traffic features in space and time dimensions. Simulation results show that the presented GCN-GRU traffic prediction model achieves a high forecasting accuracy, and the proposed NoSR scheme efficiently reduces the penalty of slices to guarantee a high QoS in metro optical networks.

By integrating communications in different domains, integrated radio and optical networks can serve a wider range of applications and services. Integrated radio and optical network scenarios will involve more weak-computation-ability network nodes, such as small-cell base stations. To pursue efficient integrated radio and optical networks, more efficient ways to conduct transmission under the demand of edge and cloud collaboration are required. The lack of forward-looking resource allocation may easily lead to a waste of network resources without an expected return. Therefore, an efficient resource allocation scheme needs to consider certain issues: 1) a comprehensive perspective of traffic prediction; 2) a release of pressure on the transmission pipeline during the prediction process; and 3) a reduction of loss of edge nodes due to the computation. In this paper, benefiting from machine learning, we propose a resource allocation with edge-cloud collaborative traffic prediction (TP-ECC) in integrated radio and optical networks, where an efficient resource allocation scheme (ERAS) is designed based on the prediction results with the gated recurrent unit model. We maximize the utilization of limited resources to improve the awareness of network status. We present three evaluation indicators and build a network architecture to evaluate our resource allocation scheme. Through edge-cloud collaboration, our proposal can improve traffic prediction accuracy by 9.5% compared with single-point traffic prediction, and resource utilization is also improved by edge-cloud collaborative traffic prediction.

With the rapid development of communication and the rise of new network services, the resource provisioning of the optical access network becomes more significant than before, especially under the multiple-domain networking situation with the requirement of cross-domain service support. For the sake of high resource utilization to establish more connections of services, this paper proposes a cross-domain resource allocation (CDRA) scheme in a software-defined optical access network to meet the huge bandwidth supporting the requirement of new network services. To achieve this purpose, the global evaluation strategy with the consideration of the traffic situation in each node in its domain is presented in the CDRA scheme, and its interaction process makes decisions for comprehensive optimal resource allocation by integrating radio resources and optical aggregation resources of the entire access network. Furthermore, to manage the resources among multiple domains and support the interaction process of the CDRA scheme, a cross-domain unified control architecture is firstly upgraded by using software-defined networking technology, which includes the further design of CDRA function modules with the global evaluation strategy. The simulation results verify the feasibility of the upgraded architecture and further show that the proposed CDRA scheme can effectively decrease the blocking probability with a 29.35% improvement, balance the network load, and enhance the utilization of network resources of the network.

The Industrial Internet of Things (IIoT) is considered to be one of the most promising revolutionary technologies to increase productivity. With the refined development of manufacturing, the entire manufacturing process is split up into several areas of IoT production. Devices from different domains cooperate to perform the same task, which cause security problems in interacted communication among them. Existing authentication methods cause heavy key management overhead or rely on a trusted third party. It is imperative to protect privacy and ensure the credibility of the device during device interaction. This article proposes a federated hierarchical trust interaction scheme (FHTI) for the cross-domain industrial IoT. It builds a low-privacy network platform through blockchain and protects the data privacy of the IIoT. A hierarchical trust mechanism based on federated detection is designed to realize the unified trust evaluation of cross-domain devices. A trusted cross-domain method based on device trust value is designed to ensure the security and trustworthiness of cross-domain devices. The simulation results show that the FHTI scheme can improve the speed of identity authentication and the detection accuracy of malicious devices.