High-speed Networks Research Articles

Modeling the delay propagation dynamics in high-speed railway networks with a traffic-driven SIS epidemic model

The propagation of delays is a prevalent issue in high-speed railway (HSR) networks. However, the underlying properties of delay propagation have not been fully explored, hindering the effective management of this problem. In this paper, considering the impact of traffic flow, we model the delay propagation as a traffic-driven susceptible–infected–susceptible (SIS) epidemic spreading process. We introduce a traffic frequency matrix to estimate the traffic rate and further derive the propagation dynamic equations using a continuous-time Markov chain method. We then derive the epidemic threshold of the model, which is proportional to the recovery rate and inversely proportional to the average path length. Finally, through simulation, we study the impacts of various factors including the infection rate, recovery rate, and the degree and number of initial delay stations. Our work provides some insights for the prediction and control of delay propagation in HSR networks.

Advanced Modulation Formats for 400 Gbps Optical Networks and AI-Based Format Recognition

The integration of communication and sensing (ICAS) in optical networks is an inevitable trend in building intelligent, multi-scenario, application-converged communication systems. However, due to the impact of nonlinear effects, co-fiber transmission of sensing signals and communication signals can cause interference to the communication signals, leading to an increased bit error rate (BER). This paper proposes a noncoherent solution based on the alternate polarization chirped return-to-zero frequency shift keying (Apol-CRZ-FSK) modulation format to realize a 4 × 100 Gbps dense wavelength division multiplexing (DWDM) optical network. Simulation results show that compared to traditional modulation formats, such as chirped return-to-zero frequency shift keying (CRZ-FSK) and differential quadrature phase shift keying (DQPSK), this solution demonstrates superior resistance to nonlinear effects, enabling longer transmission distances and better transmission performance. Moreover, to meet the transmission requirements and signal sensing and recognition needs in future optical networks, this study employs the Inception-ResNet-v2 convolutional neural network model to identify three modulation formats. Compared with six deep learning methods including AlexNet, ResNet50, GoogleNet, SqueezeNet, Inception-v4, and Xception, it achieves the highest performance. This research provides a low-cost, low-complexity, and high-performance solution for signal transmission and signal recognition in high-speed optical networks designed for integrated communication and sensing.

Streaming Large-Scale Microscopy Data to a Supercomputing Facility.

Data management is a critical component of modern experimental workflows. As data generation rates increase, transferring data from acquisition servers to processing servers via conventional file-based methods is becoming increasingly impractical. The 4D Camera at the National Center for Electron Microscopy generates data at a nominal rate of 480 Gbit s-1 (87,000 frames s-1), producing a 700 GB dataset in 15 s. To address the challenges associated with storing and processing such quantities of data, we developed a streaming workflow that utilizes a high-speed network to connect the 4D Camera's data acquisition system to supercomputing nodes at the National Energy Research Scientific Computing Center, bypassing intermediate file storage entirely. In this work, we demonstrate the effectiveness of our streaming pipeline in a production setting through an hour-long experiment that generated over 10 TB of raw data, yielding high-quality datasets suitable for advanced analyses. Additionally, we compare the efficacy of this streaming workflow against the conventional file-transfer workflow by conducting a postmortem analysis on historical data from experiments performed by real users. Our findings show that the streaming workflow significantly improves data turnaround time, enables real-time decision-making, and minimizes the potential for human error by eliminating manual user interactions.

Channel reconstruction through improvised deep learning architecture for high-speed networks

Efficient acquisition of channel state information (CSI) is quite complicated process but immensely essential to exploit probable benefits of massive multiple input multiple output (MIMO) systems. Therefore, a deep learningbased model is utilized to estimate channel feedback in a massive MIMO system. The proposed improvised deep learning-based channel estimation (IDLCE) model enhances channel reconstruction efficiency by using multiple convolutional layers and residual blocks. The proposed IDLCE model utilizes encoder network to compress CSI matrices where decoder network is used to downlink reconstruct CSI matrices. Here, an additional quantization block is incorporated to improve feedback reconstruction accuracy by reducing channel errors. A COST 2,100 model is adopted to analyse performance efficiency for both indoor and outdoor scenarios. Further, deep learning-based model is used to train thousands of parameter and correlation coefficients much faster and to minimize computational complexity. The proposed IDLCE model evaluate performance in terms of normalized mean square error (NMSE), correlation efficiency and reconstruction accuracy and compared against varied state-of-art-channel estimation techniques. Excellent performance results are obtained with large improvement in channel reconstruction accuracy

Maximizing energy efficiency in HetNets through centralized and distributed sleep strategies under QoS constraint

This research addresses the critical need to optimize the Energy Efficiency (EE) for Ultra-Dense HetNets amid the ever-increasing demands for high-speed data networks. The rapid increase in high-speed devices highlights the urgent necessity for a transformative change in upcoming 5G cellular networks. According to Ericsson's 2022 report, mobile data traffic volume is expected to double by 2027, with mobile video traffic anticipated to rise by nearly 30% each year. In response to these challenges, researchers have identified essential technologies that facilitate 5G networks, including massive Multiple-input-Multiple-output (m-MIMO) systems and HetNets. Although both promise enhanced coverage and throughput, HetNets emerges as a cost-effective solution, surpassing m-MIMO in implementation cost and coverage. However, achieving maximum EE in HetNets necessitates careful consideration of various constraints, including delay, coverage probability, and Signal-to-Interference-plus-Noise Ratio (SINR) thresholds. This research marks a significant milestone in adopting the Distributed Dynamic Opportunistic Sleep Strategy (D-DOSS) approach. The D-DOSS method organizes small clusters throughout the network and evaluates key Quality of Service (QoS) parameters including Energy Utilization Efficiency (EUE), Coverage Probability, Data Throughput, and Success Probability using Monte Carlo simulations. This research analyzes Distributed Sleep (DS) and Centralized Schemes (CS) concerning given QoS parameters. While DS methodologies often exhibit performance trade-offs compared to CS, they provide significant advantages in terms of ease of implementation and management. CS, though representing the most commonly used method in ultra-dense HetNets involves high computational costs that complicate its management. By integrating the D-DOSS and addressing various constraints, this research not only advances HetNet technologies but also makes a significant contribution to optimizing EE while preserving network performance and QoS. The innovative D-DOSS approach offers a promising solution to the challenges of energy efficiency in wireless communication networks and paves the way for future advancements in HetNet deployments. The results and analysis show that D-DOSS effectively addresses the limitations of DS and outperforms existing CS techniques.

A Modified TCP BBR to Enable High Fairness in High-Speed Wireless Networks

Wireless networks, especially 5G and WiFi networks, have made great strides in increasing network bandwidth and coverage over the past decades. However, the mobility and channel conditions inherent to wireless networks have the potential to impair the performance of traditional Transmission Control Protocol (TCP) congestion control algorithms (CCAs). Google proposed a novel TCP CCA based on Bottleneck Bandwidth and Round-Trip propagation time (BBR), which is capable of achieving high transmission rates and low latency through the estimation of the available bottleneck capacity. Nevertheless, some studies have revealed that BBR exhibits deficiencies in fairness among flows with disparate Round-Trip Times (RTTs) and also displays inter-protocol unfairness. In high-speed wireless networks, ensuring fairness is of paramount importance to guarantee equitable bandwidth allocation among diverse traffic types and to enhance overall network utilization. To address this issue, this paper proposes a BBR–Pacing Gain (BBR–PG) algorithm. By deriving the pacing rate control model, the impact of pacing gain on BBR fairness is revealed. Adjusting the pacing gain according to the RTT can improve BBR’s performance. Simulations and real network experiments have shown that the BBR–PG algorithm retains the throughput advantages of the original BBR algorithm while significantly enhancing fairness. In our simulation experiments, RTT fairness and intra-protocol fairness were improved by 50% and 46%, respectively.

AI-Driven Neural Networks for Real-Time Passenger Flow Optimization in High-Speed Rail Networks

AI-Driven Neural Networks for Real-Time Passenger Flow Optimization in High-Speed Rail Networks

Unsupervised domain adaptation for drive-by condition monitoring of multiple railway tracks

Monitoring railway tracks through drive-by vibration data collected by in-service trains offers a cost-effective and adaptable solution for inspecting multiple railway lines. However, numerous existing drive-by monitoring methods rely on supervised learning models, necessitating extensive labelled data for each line. In this paper, a novel framework is proposed based on Unsupervised Domain Adaptation (UDA) concept which facilitates the transfer of a geometric defects diagnosis model learned from one line to a new line without the need for any labelled data from the new line. The proposed framework learns the dynamic-based features that are sensitive to damage and also invariant to different railway tracks. It comprises three components: data pre-processing, UDA implementation, and damage diagnosis. The framework uses the data from the source domain, including corresponding labels, as well as the unlabelled data from the target domain as input. The outputs of the framework consist of the predicted labels for the target domain. The performance of the proposed framework is evaluated using a comprehensive dataset of field measurements of a high-speed train passing 4 different lines within the French high-speed rail network. The proposed UDA framework is implemented using four common UDA algorithms including Information-Theoretical Learning (ITL), Geodesic Flow Kernel (GFK), Transfer Component Analysis (TCA), and Subspace Alignment (SA). The results show that the proposed framework has a 14% increase in the anomaly detection accuracy compared to traditional unsupervised learning methods in which UDA is not used. Furthermore, this study investigates the impact of incorporating a percentage of target data labels during training (semi-supervised domain adaptation), along with various sensor layouts and different tuning parameters, on the accuracy of the proposed approach. The results show that the proposed framework can significantly facilitate the monitoring of railway track conditions using the data collected by in-service trains which could be great interest of railway owners.

A Comprehensive Overview of Internet of NanoThings and its Applications

INTRODUCTION: Internet of NanoThings (IoNT) is regarded as the next generation of the Internet of Things due to its bright future and wide range of applications . According to the global "Internet of Nanothings (IoNT) Market" research, there has been good growth in recent years, and this trend is expected to continue until 2030. IoNT is basically the Internet of Things on a nanoscale. IoNT essentially describes how nanoscale devices are connected to one another within current networks. A high-speed network can be used to connect different nanodevices through the IoNT. This study provides a comprehensive overview of the architecture, benefits and applications of IoNT. OBJECTIVES: The objective is to provide insights into IoNT framework and it applications in fields like healthcare, food industry, agriculture , environment monitoring etc, METHODS: This study explored the IoNT architecture and its applications. The exploration involves reviewing the articles, journals and other web resources. RESULTS: Highlights the aspects of IoNT and emphasizing the importance of its applications in various fields. The need to combine this research and emphasize the IoNT related applications stems from the dearth of research in the field. CONCLUSION: The world is becoming increasingly developed as a result of ongoing technological advancements. Future developments, which are expected to peak in the next one to two decades, will be led by IoT and nanotechnology. IoNT offers potential and means to enhance numerous facets of individuals' lives. Its primary characteristics are monitoring and diagnostic services, which support and improve decision-making and outcomes across a range of application domains.

High-speed Ta2O5-based threshold switching memristor for LIF neurons

Due to their high similarity to biological ion channels, low power consumption, small footprint, and the fact that they do not require reset circuits, threshold switching memristors have been intensively studied for simulating neurons in neuromorphic chips. Switching speed is one of the key challenges which limit the application of threshold switching memristors in chips. In this study, Ta2O5 threshold switching memristors with high switching speeds were prepared by doping with silver. The results show that 14 wt. % Ag doped Ta2O5 threshold switching memristors exhibit excellent bi-directional threshold switching performance, featuring fast switching speeds (<20 ns, <18 ns), low leakage currents (<10 pA), and high switching ratio (>107). According to the field nucleation theory, the rapid switching speed can be attributed to the low nucleation energy (0.26 eV) of silver within the Ta2O5 matrix, which is achieved by incorporating 14 wt. % Ag during the doping process. Based on Pspice, a LIF (leaky integrate-and-fire) neuron based on the silver nanoparticles doped Ta2O5 threshold switching memristors is built, and its firing function has been simulated. The results show that the LIF neuron with a short switching time is able to excite pulse spiking with high frequencies. These results demonstrated that the silver nanoparticles doped Ta2O5-based threshold switching memristors hold significant potential for constructing high-speed artificial neural networks.

Ethernet's Impact on PCB Design and Solutions

Ethernet is a technology that connects computers to form a local network through which devices can communicate with other devices. For example, 800 GBE (800 Gigabit Ethernet) is a high-speed network technology in the Ethernet standard that supports 800 Gbit/s (800 gigabit per second) data transfer rate. However, over the years, Ethernet has developed into a worldwide data communication system, with speeds ranging from the original 10 Mbit/s to 800 Gbit/s and even 1.6 Tbit/s today. In this context, the complexity of PCB (printed circuit board) chip test design continues to increase.

Empowering super-stars or rising-stars? Dynamics of the high-speed rail network planning in China

ABSTRACT The development of high-speed rail (HSR) systems as a critical component of the global 'infrastructure turn' is deeply embedded in regional socioeconomic contexts. Drawing on advances in actor-oriented network modelling and longitudinal inter-city HSR data in China during 2008-2022, this study explores how the evolution of the HSR network is dynamically and differentially underpinned by attribute-based effects of socioeconomic scale and growth rate to balance the short-term efficiency and long-term equity of nationwide development. After controlling for structure-based effects (transitive triads and balance) as well as attribute-based effects related to geographic, natural and cultural contexts, the results provide evidence that while the scale and the growth rate of economy and population both positively impact HSR link formation, the impacts of scale-related effects significantly diminish over time whereas the impacts of growth-rate-related effects exhibit a significant strengthening trend. The results further illustrate that while the scale-related and growth-rate-related effects of economy function as substitutes in driving HSR link formation, the two corresponding effects of population complement each other, providing evidence for the heterogeneous roles of economic and social contexts in underpinning HSR network planning. The findings provide implications for more effective and equitable regional socioeconomic development through strategic HSR network planning.

Connect to green: High-speed rail network and firms’ environmental performance

High-speed rail (HSR) has completely revolutionized intercity travel, significantly impacting economic activities. Recent studies have increasingly focused on how HSR affects environmental pollution. Given China’s extensive HSR policy in recent years, we extend this discussion in the context of China, introducing several new dimensions. Firstly, by manually collecting the daily operating routes of each HSR line in China, we construct a dynamic, directed, and weighted HSR network and provide a detailed description of the topological evolution of Chinese HSR from 2007 to 2014. Secondly, by employing network theory algorithms, we develop a city-level HSR strength index. This approach diverges from the commonly used multi-period difference-in-differences (DID) specification, allowing us to better address heterogeneity and discontinuity issues in identifying HSR effects. Thirdly, by leveraging the specific structure of the HSR network, we analyze how cities are interconnected and, for the first time, discuss the impact of “HSR-catalyzed supervision pressure” on firms’ pollution performance. Our findings show that firms exposed to higher HSR service intensity are more likely to reduce their SO2 emissions, a result that remains robust across various checks and other pollutants. We propose an intercity interaction story, predicting that HSR facilitates spillovers among city groups, thereby increasing environmental supervision pressure from neighboring cities in the HSR network. In further empirical analysis, we identify three specific sources of supervision pressure: peer pressure, vertical pressure, and public pressure.

HI-Net: A novel histopathologic image segmentation model for metastatic breast cancer via lightweight dataset construction

Since 2020, breast cancer has remained the most prevalent cancer worldwide and the World Health Organisation projects significant increases by 2040, with new cases expected to exceed 3 million annually (a 40% increase) and deaths to surpass 1 million (a 50% increase), highlighting the urgent need for advancements in detection and treatment. Current detection of metastasis is highly dependent on labour-intensive and error-prone pathological examination of large-scale biotissue. Given the high-resolution (100,000 × 100,000 gigapixels) but limited quantity of open-source pathological slide datasets, existing deep learning models face preprocessing challenges. This paper introduces HI-Net, a high-speed panoramic feature-extraction pyramid network for rapid and accurate detection of metastatic breast cancer, balancing panoramic segmentation and local attention. Additionally, a lightweight pathological slide dataset optimised for 512 x 512-pixel resolution, derived from downsampled and reassembled competitive datasets, accelerates training and reduces computational costs. HI-Net demonstrates superior performance on existing medical imaging competition datasets and our lightweight dataset, evidencing its effectiveness across datasets and potential for contributing to the generalisation of intelligent diagnostics.

An improved whale optimization algorithm for UAV swarm trajectory planning

For the problem of trajectory planning in the small-scale unmanned aerial vehicle (UAV) swarms, classical intelligent algorithms and newly emerged bio-inspired algorithms often suffer from being trapped in local optima, leading to suboptimal solutions. Moreover, these algorithms fail to ensure optimal solutions and convergence speed in high-speed dynamic UAV networking scenarios. In response to these challenges, this paper proposes an Improved Whale Optimization Algorithm (IWOA) that considers the global perspective. To address the issue of initial solution diversity, an opposition-based learning method is introduced by constructing a reverse population, enhancing the diversity of the initial population. To overcome the problem of the algorithm getting trapped in local optima, random convergence factors, and end-point random neighborhood perturbations are incorporated to accelerate the global convergence speed of the IWOA and prevent it from getting stuck in local optima. Additionally, a grid digital elevation model is employed when constructing the experimental environment model, considering various physical constraints of the UAV swarm. This ensures that the simulation validation of the IWOA algorithm is closer to real-world scenarios. Simulation results indicate that, compared to commonly used algorithms, the IWOA can generate more optimal trajectories for drone swarm planning under constraints resembling real-world scenarios. It exhibits better performance in trajectory evaluation, convergence speed, and stability, thereby meeting the requirements of trajectory planning for small-scale UAV swarms. The proposed IWOA enhances UAV swarm coordination and efficiency, significantly impacting real-world applications in various fields.

Virtualization and energy management optimization of high speed computer network data centers based on optical switching and network technology

With the development of cloud computing and big data technology, the scale and complexity of data centers are increasing, and the thermal energy consumption management of computer processors can effectively improve the energy efficiency of data centers and reduce operating costs. Therefore, this study discusses the application of high-speed computer network architecture based on optical switching and network technology in data center virtualization. By constructing a high-speed computer network model based on optical switching technology, virtualization technology is used to dynamically schedule and manage resources, combined with real-time monitoring system, and the energy consumption data of the processor is collected. Using the data analysis method, the energy consumption performance under different load conditions is evaluated and the optimization strategy is proposed. The experimental results show that the network architecture using optical switching technology significantly reduces the latency of data center and improves the resource utilization. In the thermal management of the processor, dynamic resource scheduling successfully reduces the energy consumption and effectively maintains the stability and performance of the system. As a result, data center virtualization solutions based on optical switching and networking technologies can significantly optimize the power management of computer processors and improve overall energy efficiency.

A systematic review of design and performance enhancement techniques for the reduction of radar cross section in multiple input multiple output antennas

multiple-input multiple-output technology has emerged as a pivotal solution for addressing the capacity constraints of high-speed broadband wireless networks. By employing multiple antennas at both the transmitter and receiver, multiple-input multiple-output significantly enhances wireless communication efficiency. This paper presents a comprehensive review of multiple-input multiple-output antenna design strategies tailored for fifth-generation (5 G) networks and beyond. Specifically, it critically examines methodologies aimed at mitigating radar cross section in multiple-input multiple-output antennas, a crucial aspect of antenna engineering, especially in scenarios requiring reduced detectability. Various design and performance enhancement techniques proposed in the literature for RCS reduction in multiple-input multiple-output antennas are systematically analyzed, focusing on aspects such as antenna geometry and materials methods. Synthesizing these findings provides valuable insights into the current state of research, highlighting advancements, challenges, and future directions in developing efficient multiple-input multiple-output antennas with minimized radar cross section. This review contributes to antenna technology by aiding researchers and practitioners in informed decision-making regarding radar cross section reduction techniques for multiple-input multiple-output systems, and it identifies innovative approaches pertinent to multiple-input multiple-output applications. Finally, key considerations in multiple-input multiple-output antenna design are addressed, proposing potential directions for future research and development initiatives.

Analisis Penggunaan Metode XConnect Main and Backup Link Berbasis VPN MPLS Layer 2 pada Jaringan Metro-Ethernet

The rapid development of information and communication technology requires excellent scalability, which is crucial for network performance availability. MPLS is a technology that can be used for communication in high-speed backbone networks based on virtual private networks (VPN). MPLS provides solutions to various current computer network problems such as speed, scalability, quality of service (QoS), and network traffic management. This research aims to optimize the scalability of MPLS Layer 2 VPN-based network traffic by applying the xconnect main and backup method implemented in metro Ethernet networks. The network simulation implementation was created using the Emulated Virtual Environment – Next Generation (EVE-NG) application to obtain data on convergence time and QoS parameters according to TIPHON standards, as well as the use of MPLS and Route Reflector to enhance MPLS backbone performance. This research shows that both main and backup MPLS L2VPN paths have good index values in packet delivery according to TIPHON standards, with an average rating of 3.0. Thus, this research can help optimize path scalability to improve the quality of MPLS L2VPN network traffic.

End-to-end learning of joint noise shaping and probabilistic shaping for OAM mode division multiplexing transmission.

Intensity modulation direct detection (IM/DD) orbital angular momentum (OAM) mode division multiplexing (MDM) technology can greatly expand the capacity of a communication system, which is a promising solution for the next generation of high-speed passive optical networks (PONs). However, there are serious obstacles such as mode coupling, device nonlinear impairment, and quantization noise in an IM/DD OAM-MDM system with a low-resolution digital-to-analog converter (DAC). In this Letter, we propose a novel, to the best of our knowledge, end-to-end (E2E) learning scheme based on a double residual feature decoupling network (DRFDnet) emulator with joint probabilistic shaping (PS) and noise shaping (NS) for the OAM-MDM IM/DD transmission. Our DRFDnet emulator can accurately build a complex nonlinear model of an OAM-MDM system by separating the signal impairments into linear and nonlinear. Meanwhile, a DRFDnet-based E2E scheme for joint PS and NS is presented with the aim of compensating the signal impairment for the OAM-MDM IM/DD system. An experiment is carried out on a 200 Gbit/s PON system based on the OAM-MDM IM/DD transmission. The experimental results demonstrate that the proposed DRFDnet-based joint PS and NS scheme is a promising solution to effectively mitigate nonlinear distortions and outperforms the CGAN-based joint PS and NS scheme and traditional joint PS and NS scheme with receiver sensitivity improvements of 1.2 dBm and 2.5 dBm under hard-decision forward error correction (HD-FEC) thresholds, respectively. Our experimental results demonstrate that the proposed DRFDnet emulator-based E2E learning scheme is a viable candidate for future PON.

Does transportation infrastructure contribute to urban entrepreneurship? Evidence from the high-speed railway in China

High-speed railways have a profound impact on entrepreneurship. Adopted China's city panel data, we use a time-varying DID model to estimate the influence of the high-speed railways on urban entrepreneurship and study its mechanism. The analysis results reveal that high-speed railways have improved the entrepreneurial vitality of cities. The mechanism test suggests that high-speed railway promotes entrepreneurship activities by accelerating human capital flow, strengthening producer services agglomeration, and promoting market integration. A range of robustness tests including a parallel trend test, placebo test, and PSM-DID model are used to ensure the reliability of the empirical results. Heterogeneity analysis and spatial analysis are used to deepen the research. Heterogeneity analysis reveals that high-speed railway construction is likely to motivate entrepreneurship in eastern cities, big cities, and cities with high-level business environments. The spatial analysis results show that the opening of the high-speed railways promotes the entrepreneurial level of surrounding cities. The research results help to evaluate the economic benefits of high-speed railway network expansion, and provide decision-making references for stimulating urban entrepreneurship.