Non-terrestrial networks (NTNs) are becoming an attractive approach in 6G era to provide ubiquitous connectivity to everywhere. NTN is expected to bring opportunities for cost-effective coverage extension from the sky using satellites and aircraft as flying base stations. However, NTN also brings challenges in providing stable communication due to changes in network topology and communication environment over time, which needs continuous path selection. This paper proposes a path selection method that selects paths based on a predicted path computation rule for each time using a graph neural network (GNN) model that takes graphs with heterogeneous nodes as input. An NTN is transformed into graphs with heterogeneous nodes, where both network nodes and links become heterogeneous nodes in the transformed graph, to enable taking both features of network nodes and links as explicit input to GNN models. Evaluation results show that the proposed method outperforms existing routing methods in terms of packet delivery ratio of multiple traffic flows with different pairs of source and destination. The proposed method also outperforms flow-dependent models even for unseen traffic flows in training, which demonstrates that representing NTN by graphs with heterogeneous nodes and using GNN models enable flow-independent path selection.

Future carrier networks will need to aggregate traffic from widely distributed sensor nodes while meeting stringent service requirements in industrial and logistics applications. As these networks scale, ensuring deterministic latency and cost efficiency becomes increasingly critical. Technologies with optical transmission and aggregation capabilities, such as Passive Optical Networks (PONs) with switching functions, offer cost-effective solutions. However, the queuing delays caused by multi-stage traffic aggregation for large-scale data collection must be accurately estimated to meet performance requirements. Traditional machine learning approaches for the queuing delay estimation often rely heavily on large labeled datasets, which are expensive and impractical to collect in large-scale environments. To address these limitations, this study proposes a Queue-Informed Neural Network (QINN) that incorporates governing equations derived from queuing theory into the neural network training process. By embedding queuing principles into the learning model, the proposed QINN improves the accuracy of queuing delay estimations without requiring extensive datasets. Application of the proposed QINN model to simulation data under different aggregation scenarios, such as PONs combined with an Ethernet switch, demonstrates improved the accuracy of queue delay estimation. These results highlight the potential of integrating governing equations based on queuing theory to improve queuing delay estimation in aggregation networks.

Future carrier networks for 6G are expected to verify performance across heterogeneous networks integrating multiple technologies. This integration requires effective verification of network performance under unpredictable conditions. In response, a node modeling method has been proposed for digitally evaluating the performance of actual network nodes. However, creating accurate node models is challenging due to the high cost and difficulty of collecting extensive real-world data under various environmental conditions. Therefore, the objective of this study is to improve the performance of actual network nodes using limited real-world datasets. We investigate the potential of natural language processing technologies to improve the accuracy of router performance estimation. In this paper, we propose a Feature Name Decoration (FND) method using Large Language Models (LLMs) to predict actual router metrics. The FND can help clarify the relationships between specific features, such as router settings and traffic conditions, and their impact on router metrics. The results show that the proposed FND improves the estimation accuracy of actual router performance metrics, including throughput, packet loss rate, and packet delay.

Future carrier networks for 6G are expected to verify the performance across heterogeneous networks integrating multiple technologies. This integration requires effective verification of network performance under unpredictable conditions. In response, a node modeling method has been proposed for digitally evaluating the performance of actual network nodes. However, creating the node model is challenging due to the high cost and difficulty of collecting extensive real-world data under various environmental conditions to train the node model. Alternatively, fine-tuning the model with data from different data-rich domains could increase the operational cost due to the selection of utilized data and the decisions on which parameters to retain. Therefore, the objective of this study is to improve the performance of actual network nodes using limited real-world data without complex fine-tuning. We explore how natural language processing enhances router performance estimation. Here, we propose an attentive Retrieval-Augmented Generation (RAG) method with Large Language Models (LLMs) for estimating actual router performance by augmenting real-world data with the most relevant simulation data. The results show that the proposed attentive RAG with the LLM improves the estimation accuracy of actual router performance metrics: throughput, packet loss rate, and packet delay.

Future carrier networks for 6G are expected to guarantee the performance across heterogeneous networks that integrate multiple technologies. This integration requires the effective verification of network performance under unpredictable conditions. To address this, the network digital replica (NDR) has been proposed to evaluate the performance of actual network equipment digitally. However, a major challenge of the NDR is the difficulty and expense of acquiring extensive real-world datasets for various traffic patterns, which is necessary for training actual network node models. This paper proposes a meta learner-based transfer learning to infer actual router metrics based on the model using network simulation data. The proposed method aims to build a model of actual router metrics based on limited real-world datasets by supplementing it with different ranges and types of simulation data. To address the differences in these datasets, the proposed method utilizes Neural Processes as a meta-learner combined with Partial Least Squares analysis to capture and bridge the representation for packet multiplexing tasks between simulation and real-world datasets for transfer learning. The results show that the proposed method improves the inference accuracy of actual router metrics; throughput, packet loss rate, and packet delay.

Non-terrestrial networks (NTN) are becoming an attractive approach in beyond 5G/6G era to provide ubiquitous connectivity to everywhere, including uncovered or underserved areas. NTN enables efficient coverage of large areas from the sky by using satellites and aircraft as flying network nodes such as base stations and routers. In contrast, the node mobility of NTN introduces dynamic changes in the network topology, requiring continuous updates of the control plane, such as routing, to ensure stable communications in terms of packet delivery and latency. In addition, dynamic changes in the communication environment, such as weather, cause link quality and availability to fluctuate. As a result, existing path selection approaches may result in the selection of poor-quality paths even though they involve frequent control message flooding for topology discovery and path finding. This paper proposes a centralized path selection method in NTN using multiple path selection rules adaptively based on link information to increase packet delivery rate with fewer control messages. The path selection rule at each time is predicted by a machine learning (ML) model based on the link information. The rule prediction model is trained by training scenarios of an NTN with different parameters. Simulation results show that the proposed method outperforms the existing methods in terms of packet delivery rate and maximum latency with less than 1 % of control messages. The results also show that each of the multiple path selection rules in the proposed method contributes to increasing the packet delivery rate.

Future network infrastructures will need to provide network services safely and rapidly under complex conditions that include accommodating many devices and multiple access lines such as 5G / 6G supported by multiple carriers. Further-more, future carrier networks will support network disaggre-gation technologies to leverage best-of-breed technology from different suppliers in accordance with the service requirements. Therefore, the efficiency of the verification needs to be improved for the combinations of a large amount of various network equipment and components constituting the network infrastructure to ensure network quality and reliability for unknown network conditions. The issue focused on this study is how to improve the prediction accuracy for the metrics of black-boxed network nodes when only the network node settings and traffic conditions are known as the external conditions. To address this, here, we propose machine learning based node modeling to improve the accuracy of predicted network node metrics by recursively adding other predicted metrics to the training datasets step by step in accordance with the feature importance. Experimental results show that the coefficient of determination (R2) of router metrics the throughput, the packet loss, and the packet delays, could be improved by using the training datasets including router settings and other predicted router metrics.

Future network infrastructures will need to provide network services safely and rapidly under complex conditions that include accommodating many devices and multiple access lines such as 5G / 6G supported by multiple carriers. For this reason, the efficiency of the pre-verification needs to be improved for a large number of various devices to ensure safety and reliability. Furthermore, future carrier networks will support network disaggregation technologies to leverage best-of-breed technology from different suppliers in accordance with service requirements. Therefore, it is necessary to verify combinations of a large number of devices and the components constituting the network infrastructure to achieve optimal settings. In this paper, we propose the concept of network digital replica and a method of network node modeling to predict the performance of network nodes using neural-network-based machine learning. A network digital replica, which is a copy of a physical network, can be created in a digital domain not only to classify the specifications of network nodes but also to verify the performance for network devices digitally. We evaluate the effectiveness of the proposed method, which predicts the throughput and processing delays of actual routers on the basis of the sets of learning data including router settings and traffic conditions.

With the development of cameras and sensors, and the spread of cloud computing, life logs can be acquired and stored in general households for various services using the logs. However, it is difficult to analyze moving images acquired by a home sensor in real time using machine learning because the data size and the computational complexity are large. New computing paradigm called edge computing or fog computing, which enables distributed computing over edge and cloud, has the possibility to address this issue. The feature vectors are extracted from moving images by preprocessing on the sensor side and the only small feature vectors are sent to the cloud and used for learning. But, it is not clear how accurately we can recognize actions using only the feature vectors in the learning and inferring. We investigate the accuracies of action recognition with various machine learning methods using feature vector information obtained from moving images. We use the pose estimation library OpenPose for detection of the feature vectors and recognize actions using logistic regression, random forest, support vector machine, and neural network (NN) models, general NN and LSTM, as machine learning methods. The experimental results show that it is possible to recognize an action with 80% accuracy or higher when using random forest and neural network models. We also discuss a method to further improve the accuracy based on the experimental results.