High-speed Networks Research Articles

Impact of high-speed railway network improvement on consumption synergy in Beijing-Tianjin-Hebei region

This paper investigates the impact of high-speed rail (HSR) network improvement on the consumption synergy of the Beijing-Tianjin-Hebei region by using the panel data of 13 prefectural-level cities from 2006 to 2021. The results show that: (1) The improvement of the HSR network in the Beijing-Tianjin-Hebei region has a significant positive impact on raising overall regional consumption levels. (2) The HSR network improvement has a heterogeneous impact on consumption in different cities within the Beijing-Tianjin-Hebei region. (3) The promotional effect on peripheral cities is greater than on central cities, thereby promoting the coordinated consumption development of the Beijing-Tianjin-Hebei region. The findings of this study have important policy implications, confirming that the construction of the HSR network can narrow the consumption gap within the region and promote consumption synergy across the region.

Research on the Calculation Method and Influence Law of High-speed Railway Line Sound Source Propagation Attenuation Caused by Building Sheltering

Abstract In recent years, China’s rail transit system, particularly the high-speed railway network, has experienced rapid development. However, the issue of noise pollution along these railways has become increasingly severe. The accurate prediction of sound levels at specific points near buildings is challenging due to complex factors such as differences in line height, distance, and angle relative to the building. To address this challenge and improve predictions of sound propagation attenuation caused by building shelter from high-speed railway line sources, this study derives an integral expression for diffraction attenuation based on Curze and Meakawa’s point source acoustic diffraction theory. Numerical calculations are conducted to investigate the sound propagation attenuation patterns under different position relationships between buildings and railway lines. Field tests are also carried out along a high-speed railway in China. The findings are as follows: (1) The calculated results show a difference of less than 0.5 dB compared to measured values, indicating that they accurately reflect sound attenuation caused by building shelter. (2) The angle between the building and the railway as well as the distance between the sound point and the edge of the building significantly influence building shelter attenuation; with angle being most prominent among them within a frequency band of 500~2000 Hz resulting in an attenuation range of 0.6~15 dB variation should be considered when predicting noise levels at sensitive points or designing vibration and noise reduction measures accordingly. (3) Different distances between buildings and railway lines result in additional effects on attenuations.

The New Dimension of Surgery: Telesurgery and Surgical Nursing

In the recent COVID-19 pandemic, it has been observed that surgical patients have turned more towards telemedicine applications to meet their health care needs due to the fact that they cannot apply to hospitals unless they are mandatory and even the majority of non-urgent medical activities, including elective surgeries, are paused. Telesurgery, one of the telehealth concepts in the provision of health services, has emerged as a promising application known as remote surgery, which provides remote surgical care to patients. Today, telesurgical interventions performed away from patients using surgical robots, virtual reality applications, advanced computer and network technologies have become a rising trend in the field of surgery. It is obvious that telesurgery applications, which aim to provide high quality remote surgery with worldwide expertise, have some obstacles due to the fact that it is a very new technology. However, despite the obstacles, it is witnessed that telesurgical interventions are being successfully applied day by day thanks to different application methods. It is predicted that telesurgery, which is expected to reach and serve more patients in the future, will be more widely applied and developed with its high-speed advanced network connection, lower cost rates and reaching all over the world, even to space and the depths of the sea. When we look at the nursing practices in telesurgery, which is a new application area in the field of surgery, it is stated that positive results have been achieved, but the number of studies is still insufficient. Depending on the telesurgery technology, which is expected to develop rapidly in the coming years, it is thought that there will be a rapid increase in the number of studies on telesurgery in the field of surgical nursing. Therefore, in this review, it was aimed to clarify telesurgical interventions, which have been started to be applied by surgeons as a new trend in recent years and which is a very new and highly developed application area for nurses, and to examine the surgical nursing interventions applied in this field.

Impact of high-speed railway network on county-level accessibility and economic linkage in Jiangxi Province, China: A spatio-temporal data analysis

Abstract High-speed railway (HSR) networks have profoundly influenced interregional accessibility and economic linkages. This study examined 100 counties (cities) and districts in Jiangxi Province, using spatial and temporal data to measure weighted average travel time, daily accessibility, the accessibility coefficient, and the total and intensity of economic linkages. This study analyzed the impacts of operating a single HSR line versus an entire network. The findings revealed that (1) the HSR network has created a balanced spatial–temporal convergence effect, narrowing the gap between most locations and showing a clear “corridor effect”; (2) the network has strengthened economic connections among counties and has significantly enhanced overall economic output by creating two high-level economic linkage belts, formed around the Shanghai–Kunming and Beijing–Hong Kong HSRs; and (3) the network amplifies the “Siphon effect” and “Matthew effect,” further disadvantaging non-connected regions. Therefore, the province should promote a well-designed, HSR network, enhancing economic exchanges among counties and fostering a high-level economic belt characterized by diversity, complementary advantages, and coordinated development.

High-speed railway express delivery volume forecast based on data-driven ensemble forecast approaches: The China case

Current researches on logistics delivery volume forecast mainly focus on traditional transportation modes such as road, railway, and aviation, with little research on predicting high-speed railway express delivery (HSRED) volume. In this paper, a data-driven ensemble forecast approach is proposed to forecast the HSRED. Firstly, we use four kinds of single methods to predicate the HSRED, include Auto-Regressive Moving Average Model (ARMA), Linear Regression (LR), Random Forest (RF) and Singular Spectrum Analysis (SSA). Secondly, based on the predication results of single methods, we apply two ensemble methods including Arithmetic Mean (AM) and Allocation Integration Operator (AIO) to determine 22 combinations of ensemble forecast approaches. The Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Correlation Coefficient (CC) are used to evaluate the performance of each single and ensemble approach. Next, a real-world case study is conducted by using the HSRED in China as the background, the delivery demand data from Jan. 2014 to Mar. 2023 is used as the input. The results show that: The prediction results based on SSA, ARMA, and RF are close to the actual data. LR has the worst predication performance. The overall performance indicators of the ensemble models are better than those of the single prediction models. The ensemble models by using AIO are generally better than those by using AM. SSA-ARMA ensemble model constructed by AIO shows the best predictive performance. The trend of HSRED changes from 2025 to 2050 is gentle, and there will be no explosive growth, and it will gradually decrease and eventually stabilize. Finally, we distribute the predicted results to each province based on the gravity model and conduct visual analysis. The results show that: Provinces with high demand for HSRED include Shandong, Zhejiang, Jiangsu, Shanghai, and Beijing, mostly concentrated in the eastern and southeastern regions, we can consider strengthening the development and increasing investment of HSRED in these cities. Provinces with less demand, efforts can be made to strengthen publicity, accelerate the formation process of the high-speed railway network, and increase support for the HSRED industry.

A fair multi-attribute data transaction mechanism supporting cross-chain

Reliable storage and high-speed data networks enable individuals to access high-quality Internet of Things (IoT) data for scientific research through global transactions. Blockchain technology provides transparency for institutions to securely store and manage IoT data, while cross-chain transaction mechanisms facilitate the flow of IoT data. However, fairness issues may arise when it comes to cross-chain transactions of IoT data. This paper proposes a mechanism for multi-attribute data transactions to support cross-chain. The solution utilizes Vickrey–Clarke–Groves (VCG) auction, Paillier, Intel SGX, and other technologies to design a secure and equitable data seller selection scheme. The scheme ensures that the selection process for data sellers is both informed and private. Additionally, we generate a key pair for each attribute in the dataset to produce the corresponding attribute data signature. The dataset’s legitimacy is verified through batch verification to ensure that the data seller’s purchased attributes align with their requirements. The exchange of crypto assets and private keys between data sellers and buyers is designed to achieve fair payment. Our research suggests that the scheme meets the necessary security standards, and simulation results confirm its feasibility and effectiveness.

When green transportation backfires: High-speed rail's impact on transport-sector carbon emissions from 315 Chinese cities

High-speed rail is often viewed as a green transportation mode, substituting for carbon-intensive vehicular and aviation trips. However, its potential to induce additional travel demand and transport-sector carbon emissions has been largely overlooked. Here, we empirically examine the impacts of high-speed rail accessibility, the ease with which one city can reach other cities via the high-speed rail network, on transport-sector carbon emissions across 315 Chinese cities between 2010 and 2020, using a two-stage least squares model. Contrary to the anticipated emission-reduction effect, our analysis suggests a net positive impact of high-speed rail on transport-sector carbon emissions. Specifically, a 1% increase in high-speed rail accessibility leads to a 0.18% increase in transport-sector carbon emissions in the long run, as carbon emissions generated from induced travel demand have outweighed the carbon savings from substitution for carbon-intensive inter-city trips. This high-speed rail-induced increase in carbon emissions is observed in the road transport subsector, while the aviation subsector exhibits a compensatory reduction. Moreover, we find that integrating the high-speed rail network into local subway systems can curb the emission-intensifying effect, highlighting the importance of joint public transit planning. This study reveals an unexpected environmental impact of high-speed rail, conveying valuable insights into transport-sector decarbonization.

Land price dynamics in response to high-speed rail network characteristics: An empirical analysis

High-speed railway (HSR) has become the backbone of China's rapid and comprehensive urban transportation network system. However, the impact of the network characteristics of HSR on urban land prices has not been studied in sufficient detail. To fill this gap, this study focuses on both the Yangtze River Delta urban agglomeration and the Beijing-Tianjin-Hebei urban agglomeration and explores the linkage relationship between the HSR network and urban land prices. For this analysis, social network analysis and fixed effects model are used. The results show that the overall HSR network density of these two urban agglomerations still has room for improvement. Affected by regional proximity and the direction of HSR lines, HSR presents a spatial pattern of a multi-center–multi-tier network. The “siphon effect” is the main effect of the HSR network characteristics on land prices. However, the weakening degree of spatial spillover effect on siphon effect of the HSR network in the Yangtze River Delta urban agglomeration is stronger than that in the Beijing-Tianjin-Hebei urban agglomeration. Local governments should fully utilize the spatial effects of HSR and enhance the level of coordinated development among urban agglomerations.

Impact of dispersion-shifted fiber on optical communications link through orthogonal channels

Dispersion-Shifted Fiber (DSF) is essential for reducing chromatic dispersion in high-speed optical communication systems. This study investigates the influence of Four Wave Mixing (FWM) on the quality of signals in orthogonal channels. We examine the advantages of DSF technology and analyze the impact of modulation formats such as On-Off Keying with Return-to-Zero (OOK-RZ) and Duo Binary Modulation class-1 (DBM-1) on transmission performance at different distances. This research assesses the efficacy of orthogonal channels in mitigating four-wave mixing (FWM) effects and improving the overall performance of eight-channel systems at distances of 100 km and 200 km through computer simulations. The results of our study show notable enhancements, namely in optimizing the Q-factor (a metric for signal quality) and reducing bit error rates when employing orthogonal channels compared to previous work. By integrating orthogonal channels with OOK-RZ modulation, we achieved higher performance and reduced nonlinear impairments in a simulated eight-channel system with 50 GHz spacing and 80 Gb/s data rates. This effect was particularly pronounced at high input power levels. At an input power of 20 dBm and a distance of 200 km, this particular combination yielded a maximum Q-factor of 27.25 and a minimum FWM power of −54 dBm. In comparison, under the same conditions, the use of OOK-RZ alone resulted in an FWM power of −24 dBm and a Q-factor of only 1.63. This research provides vital insights into enhancing the efficiency and dependability of optical communication systems, hence facilitating breakthroughs in high-speed data transfer and network scalability.

Lightweight Certificate-Less Anonymous Authentication Key Negotiation Scheme in the 5G Internet of Vehicles

In the current 5G vehicle network system, there are security issues such as wireless intrusion, privacy leakage, and remote control. To address these challenges, an improved lightweight anonymous authentication key negotiation scheme based on certificate-less aggregate signatures is proposed and its security and efficiency are analyzed. The result shows that the scheme can offer security attributes including anonymity, traceability, and revocability, as well as effective identity authentication, and it can resist forgery attacks, man-in-the-middle attacks, tampering attacks, and smart card loss attacks. Moreover, compared with similar schemes, it possesses superior security and more efficient computational efficiency and less communication overhead, thereby being more appropriate for high-speed, large-capacity, low-latency, and resource-constrained 5G vehicle network application scenarios.

Constructing Si@CN@MXene from silicon waste as high-performance lithium-ion battery anodes

Silicon represents one of the most attractive materials for anodes in lithium-ion batteries (LIBs) due to highest theoretical specific capacity. Thus, there is a most urgent need to prepare Si-based nano materials in a very efficient way, and develop some reasonable approaches for their modification in order to resolve the short-falls of Si anode, which include both low conductivity and huge volume changes during intercalation of lithium ions. In this work, the kerf loss silicon (KL Si) from the photovoltaic industry has been used as an inexpensive Si source for the synthesis of the Si@CN@MXene (SCM) composite to be applied as an anode material. Meanwhile, the chitosan works as the charge bridge in sequence with the Si and MXene nanosheets under electrostatic force during composite preparation. The MXene nanosheet and N-doped carbon layer that build up during chitosan calcination can be attributed to forming a hierarchical high-speed conductive network over the surface of silicon, which contributed to alleviate the volume expansion of silicon. The SCM electrode discharge capacity was obtained at a stable value of 900.8 mAh g−1 at 0.5 A g−1, 861.9 mA g−1 at 1 A g−1 and 657.6 mA g−1 even at 2 A g−1 after 100 cycles. The SCM electrode also exhibited good rate performance from 0.2 to 5 A g−1. Therefore, this study recommends that the method is very promising for producing the silicon anode material for LIBs from KL Si.

Microservices-Based Resource Provisioning for Multi-User Cloud VR in Edge Networks

Cloud virtual reality (VR) is attracting attention in terms of its lightweight head-mounted display (HMD), providing telepresence and mobility. However, it is still in the research stages due to motion-to-photon (MTP) latency, the need for high-speed network infrastructure, and large-scale traffic processing problems. These problems are expected to be partially solved through edge computing, but the limited computing resource capacity of the infrastructure presents new challenges. In particular, in order to efficiently provide multi-user content such as remote meetings on edge devices, resource provisioning is needed that considers the application’s traffic patterns and computing resource requirements at the same time. In this study, we present a microservice architecture (MSA)-based application to provide multi-user cloud VR in edge computing and propose a scheme for planning an efficient service deployment considering the characteristics of each service. The proposed scheme not only guarantees the MTP latency threshold for all users but also aims to reduce networking and computing resource waste. The proposed scheme was evaluated by simulating various scenarios, and the results were compared to several studies. It was confirmed that the proposed scheme represents better performance metrics than the comparison schemes in most cases from the perspectives of networking, computing, and MTP latency.

Supporting Differentiated Streaming Services in Heterogeneous Vehicle-to-Everything Networks.

Advancements in assisted driving technologies are expected to enable future passengers to use a wide range of multimedia applications in electric vehicles (EVs). To address the bandwidth demands for high-resolution and immersive videos during peak traffic, this study introduces a bandwidth-management algorithm to support differentiated streaming services in heterogeneous vehicle-to-everything (V2X) networks. By leveraging cellular 6G base stations, along with Cell-Free (CF) Massive Multi-Input Multi-Output (mMIMO) Wi-Fi 7 access points, the algorithm aims to provide a high-coverage, high-speed, and low-interference V2X network environment. Additionally, Li-Fi technology is employed to supply extra bandwidth to vehicles with limited connectivity via V2V communication. Importantly, the study addresses the urgency and prioritization of different applications to ensure the smooth execution of emergency applications and introduces a pre-downloading mechanism specifically for non-real-time applications. Through simulations, the algorithm's effectiveness in meeting EV users' bandwidth needs for various multimedia streaming applications is demonstrated. During peak-bandwidth-demand periods, users experienced an average increase in bandwidth of 47%. Furthermore, bandwidth utilization across the V2X landscape is significantly improved.

Innovative Progress in Phased-Array Antenna Systems for Ultra-Low Latency and High-Speed Communication Networks for Resilient Infrastructure

Innovative Progress in Phased-Array Antenna Systems for Ultra-Low Latency and High-Speed Communication Networks for Resilient Infrastructure

Impact of digital infrastructure construction on financial development: Evidence from the “broadband China” Strategy

The “Broadband China” has enabled China to achieve leapfrog development in the construction of its high-speed broadband networks, thereby accelerating the development of digital infrastructure and unleashing the potential for financial sector growth. This has created a strong impetus for economy to shift towards high-quality development. An in-depth understanding of the empirical correlation between the development of digital infrastructure and financial progress is absent, leaving an unexplored domain of research concerning the impact of the “Broad China” strategy on financial agglomeration (Finagg), financial scale (Finscal), and digital finance (Digfina). In order to evaluate the dynamic impacts of this policy on financial development, this research builds a multi-period difference in differences (DID) model using panel data from 269 prefecture-level cities between 2011 and 2020. The study concludes that the “Broadband China” policy significantly fosters the growth of Finagg, Finscal, and Digfina, in China, and found that the “Broadband China” policy has a significant impact on the regional heterogeneity of financial agglomeration and digital finance. There are significant variations in the effects of this policy on different regions, while the regional heterogeneity differences in the impact on financial scale are relatively small. Additionally, the three mediating variables of technological innovation, fund support, and talent agglomeration play a mediating role in the mechanism of digital infrastructure on financial development.

On DGA Detection and Classification Using P4 Programmable Switches

Domain Generation Algorithms (DGAs) are highly effective strategies employed by malware to establish connections with Command and Control (C2) servers. Mitigating DGAs in high-speed networks can be challenging, as it often requires resource-intensive tasks such as extracting high-dimensional features from domain names or collecting extensive network heuristics. In this paper, we propose an innovative framework leveraging the flexibility, per-packet granularity, and Terabits per second (Tbps) processing capabilities of P4 programmable data plane switches for the rapid and accurate detection and classification of DGA families. Specifically, we use P4 switches to extract a combination of unique network heuristics and domain name features through shallow and Deep Packet Inspection (DPI) with minimal impact on throughput. We employ a two-fold approach, comprising a line-rate compact Machine Learning (ML) classifier in the data plane for DGA detection and a more comprehensive classifier in the control plane for DGA detection and classification. To validate our approach, we collected malware samples totaling hundreds of Gigabytes (GBs), representing over 50 DGA families, and utilized campus traffic from normal benign users. Our results demonstrate that our proposed approach can swiftly and accurately detect DGAs with an accuracy of 97% and 99% in the data plane and the control plane, respectively. Furthermore, we present promising findings and preliminary results for detecting DGAs in encrypted Domain Name System (DNS) traffic. Our framework enables the immediate halting of malicious communications, empowering network operators to implement effective mitigation, incident management, and provisioning strategies.

ASAP: IEEE 802.11ax-based seamless access point handover for moving vehicles

The increasing number of connected and automated vehicles has led to a sharp increase in the demand for network access of moving vehicles. Although 5G networks support terminals with high mobility, the traffic load is too heavy to bear if all the vehicles have a large amount of data for transmission. Therefore, IEEE 802.11-based wireless network is a complementary offload solution to provide high-speed network access for vehicles with low cost, easy deployment and high scalability. However, frequent network handover of moving vehicles between multiple roadside access points (APs) results in network performance degradation, which is one of the challenges in vehicular communications. In this paper, we propose a framework (referred to as ASAP) based on the up-to-date IEEE 802.11ax standard to provide moving vehicles with seamless handover between multiple APs. By leveraging the high efficiency (HE) sounding protocol of IEEE 802.11ax, each AP is capable to monitor the current location of moving vehicles in real time. In addition, a mechanism is also proposed for AP uplink/downlink transmissions through collaboration between the APs and the backbone network to achieve seamless handover for moving vehicles. Since ASAP is based on IEEE 802.11ax, the compatible security scheme such as IEEE 802.11i can be applied to ASAP for security enhancement. The proposed solution does not require any modification on the user terminals, making it possible to be implemented in practice. Extensive simulations show that ASAP significantly reduces the network handover delay to microsecond level, and improves network throughput up to 59% compared with the state-of-the-art methods.

Study on the Effectiveness of English Teaching in Universities Based on 5G Mobile Internet

Higher education is becoming increasingly competitive and all educational institutions are concentrating on improving quality and changing traditional higher education teaching methods. New-type classroom instruction has embraced a unique advancement opportunity with the arrival of the fifth generation (5G) era. It is critical to develop a teaching assistance system that makes use of high-speed network methodology and new-type display methodology. For the innovation and reform of higher education, this article combines soft computing techniques, artificial intelligence (AI), and 5G networks. This paper outlines the exact processes and measures for incorporating “5G” technology into higher education. Finally, conduct a comparative experiment to see how good the system is at learning AI knowledge when compared to standard learning methods. The outcomes of the experiments are examined to show that employing this approach to gain AI knowledge is successful and improves students' enthusiasm in learning as well as their hands-on abilities.

High-speed rail new towns and their impacts on urban sustainable development: a spatial analysis based on satellite remote sensing data

With the rapid expansion of China’s high-speed rail network, numerous high-speed rail new towns have been established. While these new towns have brought about significant economic opportunities, they also impose pressure on local resources and environment. Accurately assessing the impacts of high-speed rail new towns on urban sustainable development is therefore a crucial issue to address. Our study employs satellite remote sensing data and spatial econometric methods to evaluate the impacts of 223 high-speed rail new towns in China from 2011 to 2021. The results indicate a gradual narrowing of development disparities among high-speed rail new towns in different cities. The construction of high-speed rail new towns has facilitated the sustainable development of cities. Notably, central high-speed rail new towns demonstrate a more considerably driving effect on the urban sustainable development compared to peripheral ones. The findings of the study provide valuable insights for policymakers and urban planners in China and other countries embarking on high-speed rail development projects. Our research highlights the importance of considering the potential impacts of high-speed rail new towns on the urban sustainable development and the need for careful planning and management to ensure that these newly-established towns contribute to a more sustainable urban future.

Optimized CNN and adaptive RBFNN for channel estimation and hybrid precoding approaches for multi user millimeter wave massive MIMO

ABSTRACT The millimetre-wave (mmWave) communication satisfies the demand for high data rates due to the characteristic of wide bandwidth. Using massive multiple-input multiple-output (MIMO) technology, a significant propagation loss of mmWave communication is effectively compensated. However, it is challenging to provide a specialised radio frequency chain for each antenna due to the constrained physical area with closely spaced antennas and prohibitive power consumption in mmWave massive MIMO systems. This paper presents novel approaches for effective channel estimation and hybrid precoding in mmWave communication systems. To address the challenges of channel estimation, a convolutional neural network (CNN) is utilised, and network parameters are optimised using enhanced whale optimization algorithm (EWOA). The proposed CNN-based channel estimation method aims to accurately estimate the channel in mmWave systems with enhanced efficiency and reduced complexity. By training CNN using EWOA optimisation algorithm, the network parameters are fine-tuned to improve accuracy and generalisation capability of channel estimation process. Furthermore, hybrid precoding is achieved using adaptive radial-basis function neural networks (adaptive RBFNNs) which enables efficient precoding while minimising complexity. Moreover, the adaptive RBFNN approach determines the optimal precoding weights based on channel state information, resulting in a improved performance and a reduced computational overhead. The performance analysis is validated using the MATLAB/Simulink software and offers to provide effectual and reliable mmWave communication systems, facilitating the realisation of high-speed and high-capacity wireless networks.