Access Network Research Articles

Mapping National‐Scale Road Surface Types Using Multisource Open Data and Deep Learning Model

ABSTRACTIdentifying road surface type (e.g., paved and unpaved) is crucial for pavement maintenance, transportation management, and road network accessibility research. Existing approaches relying on vehicle‐mounted devices or remote sensing data are have limitation for large‐scale road networks. This study proposes a novel approach to identify national‐scale road surface type using multiple open geospatial datasets and machine learning models. Specifically, 16 input variables were designed based on these datasets (including OpenStreetMap, GDP, population, building height, and land cover). Nigeria and Cameroon were selected as study areas. A substantial dataset, auto‐extracting road surface tags from OpenSreetMap, was used to train a model. The trained model predicted road surface types across the two study areas. Result indicated: (1) Most of the input variables positively impact the output variable, with “road class” being the most influential; (2) The proposed approach with deep learning model‐TabNet performs the best, with an overall accuracy above 85%; and (3) More than 83% of roads in the two African countries are unpaved, with paved roads concentrated in backbone roads and southern provinces. This approach has been validated and offers valuable insights for local authorities aiming to enhance road infrastructure.

Exploring Attitudes of Physicians towards the implementation of Mobile Health technology in Saudi Arabia

This study aims to explore Attitudes of Physicians towards the implementation of Mobile Health technology in SaudiArabia. In order to fulfil such aim, qualitative methodology was adopted and textual data were collected from 12physicians who work in King Fahad General Hospital Jeddah in Saudi Arabia. The gathered data were analysed throughusing thematic analysis. Three themes were identified (i.e. Benefits of m-health adoption, Challenges of m-healthadoption, and attitudes of medical staff following the application of m-health on their performance). The benefitsof m-health include making remote patient monitoring possible and easy, improves provider communication andcoordination, and Gives patients faster access to providers and care. The challenges of m-health include issues relatedto its usability, system integration, data security and privacy, network access, and reliability. The attitudes ofmedical staff following the application of m-health on their performance are divided into positive and negativeattitudes. Positive attitudes that connect between m-health and Increasing performance instead of documentation,and negative attitudes that connect between decreasing performance and m-health due to time consuming and itneeds training courses. The findings of this study cannot be generalised to the whole population due to limitations interms of sample size.

The delay-energy trade-off for advanced sleep modes in radio access networks

The delay-energy trade-off for advanced sleep modes in radio access networks

Demonstration of a Hybrid B5G System Integrating VLC and RF-Based Technologies with Access Networks

Demonstration of a Hybrid B5G System Integrating VLC and RF-Based Technologies with Access Networks

ZT-SDN: An ML-Powered Zero-Trust Architecture for Software-Defined Networks

Zero Trust (ZT) is a security paradigm aiming to curtail an attacker’s lateral movements within a network by implementing least-privilege and per-request access control policies. However, its widespread adoption is hindered by the difficulty of generating proper rules due to the lack of detailed knowledge of communication requirements and the characteristic behaviors of communicating entities under benign conditions. Consequently, manual rule generation becomes cumbersome and error-prone. To address these problems, we propose ZT-SDN , an automated framework for learning and enforcing network access control in Software-Defined Networks. ZT-SDN collects data from the underlying network and models the network “transactions’’ performed by communicating entities as graphs. The nodes represent entities, while the directed edges represent transactions identified by different protocol stacks observed. It uses novel unsupervised learning approaches to extract transaction patterns directly from the network data, such as the allowed protocol stacks and port numbers and data transmission behavior. Finally, ZT-SDN uses an innovative approach to generate correct access control rules and infer strong associations between them, allowing proactive rule deployment in forwarding devices. We show the framework’s efficacy in detecting abnormal network accesses and abuses of permitted flows in changing network conditions with real network datasets. Additionally, we showcase ZT-SDN’s scalability and the network’s performance when applied in an SDN environment.

Tailoring Spectral Response of Grating-Assisted Co-Directional Couplers with Weighting Techniques and Rational Transfer Functions: Theory and Experiment

This work addresses the tailoring spectral response of grating-assisted co-directional couplers (GADCs) in the context of wavelength filtering for fiber-to-the-home (FTTH) applications. Design methods for spectral response engineering by means of coupling profile apodization-type weighting techniques and also more advanced rational transfer functions fitting a predefined spectral window template are presented. Modeling results based on coupled mode theory are then applied for the design and experimental fabrication of InGaAsP/InP GADCs targeting 1.3+/1.3− µm diplexer application in FTTH access networks. The experimental results are found to be in good agreement with the modeling predictions. The design tools presented are quite general and can be easily adapted to other technology platforms, such as silicon photonics for the use of GADCs as add-drop wavelength division multiplexers. The field of parity–time symmetry is another avenue where these types of gain–loss-assisted GADCs as active components are of interest for switching applications, and the design methods presented here may find utility.

A Review of Transportation 5.0: Advancing Sustainable Mobility Through Intelligent Technology and Renewable Energy

Transportation 5.0 is an advanced and sophisticated system combining technologies with a focus on human-centered design and inclusivity. Its various components integrate intelligent infrastructure, autonomous vehicles, shared mobility services, green energy solutions, and data-driven systems to create an efficient and sustainable transportation network to tackle modern urban challenges. However, this evolution of transportation is also intended to improve accessibility by creating environmentally benign substitutes for traditional fuel-based mobility solutions, even when addressing traffic management and control issues. Consequently, to promote synergy for sustainability, the diversified nature of the Transportation 5.0 components ought to be efficiently and effectively managed. Thus, this study aims to reveal the involvement of Transportation 5.0 core component prediction in the sustainable transportation system through a systematic literature review. This study also contemplates the causal model under system dynamics modeling in order to address sustainable solutions and the movement toward sustainability in the context of Transportation 5.0. From this review, in addition to the developed causal model, it is identified that every core component management method in the sustainable Transportation 5.0 system reduces environmental impact while increasing passenger convenience and the overall efficiency and accessibility of the transport network, with greater improvements for developing nations. As the variety of transportation options, including electric vehicles, is successfully integrated, this evolution will eventually enable shared mobility, green infrastructure, and multimodal transit options.

Coherent Optics for Passive Optical Networks: Flexible Access, Rapid Burst Detection, and Simplified Structure

With the development of the Internet of Things, cloud networking, and 4K/8K high-definition video, global internet traffic has seen a dramatic increase. This surge in traffic has placed higher demands on the performance of optical networks, featuring higher data rates, lower latency, and lower cost. The passive optical network (PON) is a representative scenario of optical access networks. Issues such as burst-mode detection in upstream PON scenarios, flexible rate allocation in downstream scenarios, and the simplification of hardware complexity at the optical network unit (ONU) side have attracted considerable attention. Compared to intensity modulation/direct detection (IM/DD), a recently proposed coherent PON incorporates a local oscillator laser at the receiver, enabling superior receiver sensitivity, spectrally efficient modulation, linear optical field recovery, and flexible channel selection. These features significantly enhance the flexibility and data rates of PON systems. This paper provides a comprehensive review of the development of coherent PONs, particularly in aspects of preamble design for burst-mode detection in upstream scenarios, the design of flexible rate PONs in downstream scenarios, and solutions for reducing hardware complexity at the ONU side.

Cloud computing: Innovative strategy towards effective pedagogy in Christian religious studies

Cloud computing is an emerging phenomenon within the realm of Information and Communication Technology (ICT) that holds significant potential for enhancing various aspects of skill acquisition, data analysis, critical thinking, knowledge updating, problem-solving, research data generation, and the facilitation of engaging and stimulating teaching and learning experiences, among other benefits. Cloud computing is widely recognized as a standard paradigm that offers efficient network access to a shared pool of computing resources. These resources can be provisioned and released with minimal effort and reciprocal agreements between service providers. Education, on the contrary, is a systematic procedure of imparting knowledge that necessitates consistent updating and application of internationally recognized best practices in its execution. Therefore, this paper articulates the significance of implementing and utilizing cloud computing as an innovative approach to future-oriented and efficient pedagogy in the field of Christian Religious Studies. The outcome of this interaction can best be illustrated from the experience of many seminaries and theological colleges that have adopted cloud-based Learning Management Systems (LMS) such as Moodle or Canvas. These platforms allow educators to share course materials, facilitate discussions, and assess student performance. The research design employed in this study is survey methodology. The study’s population included 540 participants, specifically 313 undergraduate students, 192 education lecturers, and 35 administrative staff members from a tertiary institution. The sample size of 230 respondents was determined using the YARO YAMENT formula and selected through a stratified random sampling technique. A questionnaire consisting of 30 items was utilized to collect data. The questionnaire was developed, validated, and its reliability was assessed using the CRONBACH alpha formula, yielding a coefficient of 0.84. The collected data underwent analysis utilizing measures of central tendency (mean) and dispersion (standard deviation) in order to address the research inquiries. Additionally, the null hypothesis was assessed through the application of ANOVA. The study revealed that achieving a global equilibrium in terms of information and materials accessible to cloud computing users is crucial. Specifically, online materials should be designed to incorporate both local and global content, ensuring their relevance and appeal to individuals regardless of their language or cultural background. Fifteen viable strategies for enhancing futuristic and effective pedagogy in the field of Christianity.

Experimental investigation of a seamlessly converged fiber-wireless access network employing free-running laser- and envelope detection-based mmWave generation and detection.

Employing free-running laser/envelope detection-based millimeter wave (mmWave) signal generation/detection at remote radio heads (RRHs)/user equipment (UE) offers a cost-effective solution for seamlessly integrating existing intensity modulation-direct detection (IM-DD)-dominated optical access networks and wireless networks. Such fiber-wireless convergence enables a continuous flow of signals with varying characteristics between the baseband unit (BBU) and UE across fiber and wireless network segments without the need for optical-electrical-optical (O-E-O) conversions and digital signal processing (DSP) at intermediate nodes. In this paper, we extensively investigate the performance of such a fiber-wireless converged access network employing free-running laser/envelope detection-based mmWave generation/detection in an IM-DD-based 1.67 Gbit/s transmission system with 25 km standard single-mode fiber (SSMF) and 5 m @38 GHz mmWave wireless links. Experimental results demonstrate that both mmWave frequency tunability and adaptive mmWave network coverage are achievable by just dynamically and adaptively configuring the output wavelength and power of the RRH-embedded free-running laser. Additionally, envelope detection allows RRHs to use low-cost MHz-linewidth-level free-running lasers while maintaining excellent performance stability.

RSSI Enabled Routing in Fiber Wireless Access Network

RSSI Enabled Routing in Fiber Wireless Access Network

Optical Access Networks for Smart Sustainable Cities: From Network Architecture to Fiber Deployment

Optical Access Networks for Smart Sustainable Cities: From Network Architecture to Fiber Deployment

The Spatial Accessibility of High-Capacity Public Transport Networks—The Premise of Sustainable Development

At the present time, the increase in the level of motorization has led to levels of congestion that are difficult for the inhabitants of large cities to bear, with negative implications on quality of life. Solving the problem of congestion can no longer be achieved just by expanding and developing the networks; other solutions must be found that fit the requirements of the sustainable development of society. One of the solutions that is becoming more and more promoted today is that of increasing the degree of use of high-capacity public transport networks. For this, the network must serve a surface as large as possible of the territory, and the operating conditions must deal with transport requests with a varied structure and with different intensities in short time intervals. The paper aims to review several models of the topological accessibility of transport networks and to analyze how the Bucharest metro network serves the territory, after the last extension carried out in 2020. It is noted that only 56% of the city’s surface is currently covered by the metro network, while the city is in a continuous territorial expansion. Through the case study presented in the paper, the accessibility of some points of interest for Bucharest residents who use the metro network is also determined. At the end, there are formulated conclusions regarding the development and territorial coverage of the metro network and proposals for further developments, so that the services offered can benefit as many of Bucharest’s residents as possible.

Real‐Time Communication System Using Canonical Cortical Graph Neural Network With High‐Level Target Navigation Pigeon‐Inspired Optimization

ABSTRACTRadio‐frequency (RF) fingerprinting is an emerging technology for advanced device authentication. This work addresses the issue by improving both the algorithm and hardware to target real‐time AI processing in communication systems. Here, efficient real‐time communication system using canonical cortical graph neural network with high‐level target navigation pigeon‐inspired optimization (RCS‐CCGNN‐HTNPIO) is proposed. Initially, radio frequencies from multiple devices such as modem, TV, and mobile phone, these devices transmit RF signals, which are captured and processed via RF fingerprinting. Then, RF fingerprinting system is designed to distinguish between known signals and unknown signals. Afterwards, the canonical cortical graph neural network (CCGNN) for analyzing and identifying the specific RF fingerprint of each signal. The CCGNN evaluates the signals and categorizes them as known signal or unknown signal based on the RF fingerprint characteristics. Hence, the high‐level target navigation pigeon‐inspired optimization (HTNPIO) is used to optimize the CCGNN. The performance metrics provide a complete assessment of the system's ability to manage the demands of real‐time processing in communication networks. The RCS‐CCGNN‐HTNPIO approach attains19.21%, 26.12%, and 30.15% higher accuracy compared with existing techniques likes deep learning based multidimensional radio‐frequency fingerprinting enhancement approach for IoT device identification (DL‐RFF‐DI), embedding‐assisted attentional deep learning for real‐world RF fingerprinting of Bluetooth (ADL‐RW‐RFF), and multichannel attentive feature fusion for radio‐frequency fingerprinting (MCA‐RFF). The simulation results prove that the RCS‐CCGNN‐HTNPIO can provide a robust and adaptive solution for achieving high accuracy in next‐generation radio access networks.

Flow of non-Newtonian fluids in self-similar networks across different scales

Several natural and manufactured flow systems exhibit scale-invariance or self-similarity, despite appearing to be disordered. This study focuses on the laminar flow of non-Newtonian fluids through self-similar systems of tubes with porous walls (intrinsic permeability much less than 10-4 m2). Two arrangements of tubes are discussed: a dendritic flow structure and a bundle of tubes. The size of these arrangements is described as a function of structural parameters for both straight and tortuous tubes, and the flow resistance is obtained as a function of structural parameters and fluid properties. Among other findings, it is shown that the prefractal dimension of dendritic networks for maximum flow access is dependent on the size constraint chosen to design the network. Results also show that as the prefractal dimensions of diameters and lengths rise, so does the overall size of the dendritic network and tube bundle system. In contrast, it is observed that the flow resistance diminishes as the prefractal dimension for diameters increases, whereas for tortuous tubes the flow resistance increases as the prefractal dimension for lengths increases. The approaches presented in this paper have numerous potential applications, including fluxes in biological systems, microfluidic media, and hydrology.

A Semantic Approach to Successive Interference Cancellation for Multiple Access Networks

A Semantic Approach to Successive Interference Cancellation for Multiple Access Networks

Modeling and Analysis of Retrial Mechanisms in Large-Scale Massive and Sporadic Access Networks

Modeling and Analysis of Retrial Mechanisms in Large-Scale Massive and Sporadic Access Networks

Hydra Radio Access Network (H-RAN): Multi-Functional Communications and Sensing Networks, Initial Access Implementation, Task-2 Approach

Hydra Radio Access Network (H-RAN): Multi-Functional Communications and Sensing Networks, Initial Access Implementation, Task-2 Approach

Cognitive Foundation Collaboration Platform Technology for Enhancing the Intelligence of Radio Access Network Operation and Management

Cognitive Foundation Collaboration Platform Technology for Enhancing the Intelligence of Radio Access Network Operation and Management

Unified bandwidth scheduling in centralized fixed access network (C-FAN)

Unified bandwidth scheduling in centralized fixed access network (C-FAN)