Future Networks Research Articles

Scheme for a WDM-PON with colorless optical network units based on a flat optical frequency comb.

A new, to the best of our knowledge, scheme for a wavelength division multiplexing passive optical network (WDM-PON) based on a flat optical frequency comb (OFC) is proposed. Using an OFC as the optical source of the system can realize the colorlessness of optical network units (ONUs), and the direct detection of the downlink data further simplifies the ONU structure. The coherent demodulation of the uplink data improves the system performance due to the coherence of the comb lines. In this research, the proposed system is studied for its performance and power budgeting. The results show the flexibility, effectiveness, and practicability of the proposed scheme, which can be applied to future high-capacity optical access networks.

Modelling Public Intentions to Use Innovative EV Chargers Employing Hybrid Energy Storage Systems: A UK Case Study Based upon the Technology Acceptance Model

The current study investigates public intentions to use an innovative, off-grid renewably powered EV charging technology called FEVER (Future Electric Vehicle Energy networks supporting Renewables). We report the findings of a questionnaire-based survey (QBS) conducted at a zoo in the south of England, exploring the prospect of demonstrating FEVER. The QBS was designed around a context-specific technology acceptance model (TAM) and administered both face-to-face (n = 63) and online (n = 158) from April to May 2023. The results indicate that most participants were willing to pay to use FEVER, particularly where revenue would benefit the zoo. The participants agreed they intended to use the chargers, and that they would be useful and easy to use. The participants agreed that there would be normative pressure to use the chargers, but that their use would be enjoyable. Of greatest concern was that the chargers would be blocked by others. The participants were ambivalent about concerns over charging duration and charge sufficiency. Structural equation modelling confirmed that the context-specific TAM explained 58% of people’s use intentions. The core relationships of the TAM were confirmed, with ‘perceived usefulness’ additionally predicted by subjective norms and ‘perceived ease of use’ additionally predicted by anticipated enjoyment. Of the other variables, only concern that the chargers would be blocked was retained as a marginal predictor of ‘perceived ease of use’. The implications of these findings for the co-design and demonstration of FEVER are discussed.

Network Type Recognition Using Machine Learning Techniques

The telecom industry is going through a massive digital transformation with the adoption of ML, AI, feedback-based automation and advanced analytics to handle the next generation of applications and services. AI concepts are not new; the algorithms used by Machine Learning and Deep Learning are being currently implemented in various industries and technology verticals. With growing data and an immense volume of information over 5G, the ability to predict data proactively, swiftly and with accuracy, is critically important. Data-driven decision-making will be vital in future communication networks due to the traffic explosion and Artificial Intelligence (AI) will accelerate the 5G network performance. Mobile operators are looking for a programmable solution that will allow them to accommodate multiple independent tenants on the same physical infrastructure and 5G networks allow for end-to-end network resource allocation using the concept of Network Slicing (NS). Network Slicing will play a vital role in enabling a multitude of 5G applications, use cases, and services. Network slicing functions will provide end-to-end isolation between slices with an ability to customize each slice based on the service demands (bandwidth, coverage, security, latency, reliability, etc). Index Terms: Supervised Learning, Feature Engineering, Python, Network Slicing, Telecom, Classification Problems, EDA – Exploratory Data Analysis.

Assessing the Operation of a Multimodal Hub: A Traffic Impact Microsimulation Analysis

Creating new multimodal infrastructure in an existing transport network of an urban city is a challenging process. The responsible transport authorities have to pay special attention to the details regarding the accessibility and effectiveness of the new development, to avoid travelers’ confusion and network congestion. The subject of this paper is the assessment and optimization of the traffic network in the surroundings of the new multimodal depot of Thessaloniki’s future metro system with the use of the microsimulation software PTV VISSIM (version 2022). Five different scenarios were developed in collaboration with the city’s transport authority and evaluated into two stages, beginning with the whole traffic network, and then continuing with the analyzed intersections separately. The evaluation is based on Key Performance Indicators (KPIs), which were extracted by the software. According to the results of the base case scenario, the network functions satisfactorily, with slight delays. Regarding the future network, the operation of the new hub will strongly increase the traffic demand, while the proposed traffic network adjustments by the local authorities seem to cause significant delay problems. This paper aims to highlight the importance of using modeling tools during the design phase of a new infrastructure to create efficient, accessible, and sustainable infrastructures that enhance the public transport system.

Power System Integration of Electric Vehicles: A Review on Impacts and Contributions to the Smart Grid

In recent years, electric vehicles (EVs) have become increasingly popular, bringing about fundamental shifts in transportation to reduce greenhouse effects and accelerate progress toward decarbonization. The role of EVs has also experienced a paradigm shift for future energy networks as an active player in the form of vehicle-to-grid, grid-to-vehicle, and vehicle-to-vehicle technologies. EVs spend a significant part of the day parked and have a remarkable potential to contribute to energy sustainability as backup power units. In this way, EVs can be connected to the grid as stationary power units, providing a range of services to the power grid to increase its reliability and resilience. The available systems show that EVs can be used as alternative energy sources for various network systems like smart grids, microgrids, and virtual power plants besides transportation. While the grid–EV connection offers various contributions, it also has some limitations and effects. In this context, the current study highlights the power system impacts and key contributions of EVs connected to smart grids. Regarding the power system impacts in case of EV integration into smart grids, the challenges and difficulties are categorized under the power system stability, voltage/current distortions, load profile, and power losses. Voltage/current distortions like sags, unbalances, harmonics, and supraharmonics are also detailed in the study. Subsequently, the key contributions to the smart grid in terms of energy management, grid-quality support, grid balancing, and socio-economic impacts are explained. In the energy management part, issues such as power flow, load balancing, and renewable energy integration are elaborated. Then, the fault ride-through capability, reactive power compensation, harmonic mitigation, and grid loss reduction are presented to provide information on power quality enhancement. Lastly, the socio-economic impacts in terms of employment, net billing fees, integration with renewable energy sources, and environmental effects are elucidated in the present study.

Ferumoxytol-Enhanced Cardiac Cine MRI Reconstruction Using a Variable-Splitting Spatiotemporal Network.

Balanced steady-state free precession (bSSFP) imaging is commonly used in cardiac cine MRI but prone to image artifacts. Ferumoxytol-enhanced (FE) gradient echo (GRE) has been proposed as an alternative. Utilizing the abundance of bSSFP images to develop a computationally efficient network that is applicable to FE GRE cine would benefit future network development. To develop a variable-splitting spatiotemporal network (VSNet) for image reconstruction, trained on bSSFP cine images and applicable to FE GRE cine images. Retrospective and prospective. 41 patients (26 female, 53 ± 19 y/o) for network training, 31 patients (19 female, 49 ± 17 y/o) and 5 healthy subjects (5 female, 30 ± 7 y/o) for testing. 1.5T and 3T, bSSFP and GRE. VSNet was compared to VSNet with total variation loss, compressed sensing and low rank methods for 14× accelerated data. The GRAPPA×2/×3 images served as the reference. Peak signal-to-noise-ratio (PSNR), structural similarity index (SSIM), left ventricular (LV) and right ventricular (RV) end-diastolic volume (EDV), end-systolic volume (ESV), and ejection fraction (EF) were measured. Qualitative image ranking and scoring were independently performed by three readers. Latent scores were calculated based on scores of each method relative to the reference. Linear mixed-effects regression, Tukey method, Fleiss' Kappa, Bland-Altman analysis, and Bayesian categorical cumulative probit model. A P-value <0.05 was considered statistically significant. VSNet achieved significantly higher PSNR (32.7 ± 0.2), SSIM (0.880 ± 0.004), rank (2.14 ± 0.06), and latent scores (-1.72 ± 0.22) compared to other methods (rank >2.90, latent score < -2.63). Fleiss' Kappa was 0.52 for scoring and 0.61 for ranking. VSNet showed no significantly different LV and RV ESV (P = 0.938) and EF (P = 0.143) measurements, but statistically significant different (2.62 mL) EDV measurements compared to the reference. VSNet produced the highest image quality and the most accurate functional measurements for FE GRE cine images among the tested 14× accelerated reconstruction methods. 3 TECHNICAL EFFICACY: Stage 1.

Models of Chemical Communication for Micro/Nanoparticles.

ConspectusEngineering chemical communication between micro/nanosystems (via the exchange of chemical messengers) is receiving increasing attention from the scientific community. Although a number of micro- and nanodevices (e.g., drug carriers, sensors, and artificial cells) have been developed in the last decades, engineering communication at the micro/nanoscale is a recent emergent topic. In fact, most of the studies in this research area have been published within the last 10 years. Inspired by nature─where information is exchanged by means of molecules─the development of chemical communication strategies holds wide implications as it may provide breakthroughs in many areas including nanotechnology, artificial cell research, biomedicine, biotechnology, and ICT. Published examples rely on nanotechnology and synthetic biology for the creation of micro- and nanodevices that can communicate. Communication enables the construction of new complex systems capable of performing advanced coordinated tasks that go beyond those carried out by individual entities. In addition, the possibility to communicate between synthetic and living systems can further advance our understanding of biochemical processes and provide completely new tailored therapeutic and diagnostic strategies, ways to tune cellular behavior, and new biotechnological tools. In this Account, we summarize advances by our laboratories (and others) in the engineering of chemical communication of micro- and nanoparticles. This Account is structured to provide researchers from different fields with general strategies and common ground for the rational design of future communication networks at the micro/nanoscale. First, we cover the basis of and describe enabling technologies to engineer particles with communication capabilities. Next, we rationalize general models of chemical communication. These models vary from simple linear communication (transmission of information between two points) to more complex pathways such as interactive communication and multicomponent communication (involving several entities). Using illustrative experimental designs, we demonstrate the realization of these models which involve communication not only between engineered micro/nanoparticles but also between particles and living systems. Finally, we discuss the current state of the topic and the future challenges to be addressed.

Single-Photon Counting Receivers for Optical Wireless Communications in Future 6G Networks

Single-Photon Counting Receivers for Optical Wireless Communications in Future 6G Networks

AI and Blockchain Enabled Future Wireless Networks: A Survey And Outlook

Due to the explosion of mobile users and the ever-increasing heterogeneity and scale of wireless networks, traditional communication protocols and optimizing methods can not satisfy future wireless network (FWN) requirements. As promising technologies, artificial intelligence (AI) and blockchain are deemed as the solution for the FWN. AI, famous for its big data processing ability, will enable the FWN to self-update itself to better adapt to the dynamic network condition. Blockchain, as a distributed ledger, can guarantee data integrity, security and privacy. In this survey, we overview the concept of AI and blockchain and present their state-of-the-art applications in wireless networks. The potential of AI and blockchain is still huge and waiting to be fully explored in wireless networks. Therefore, we introduce how AI and blockchain can assist each other in FWNs. Furthermore, we explore the current constraints of applying both technologies in the FWNs. In the final part, we discuss the future direction of the deployment of AI and blockchain in FWNs.

Giant microwave–optical Kerr nonlinearity via Rydberg excitons in cuprous oxide

Microwave–optical conversion is key to future networks of quantum devices, such as those based on superconducting technology. Conversion at the single quantum level requires strong nonlinearity, high bandwidth, and compatibility with a millikelvin environment. A large nonlinearity is observed in Rydberg atoms, but combining atomic gases with dilution refrigerators is technically challenging. Here, we demonstrate a strong microwave–optical nonlinearity in a cryogenic, solid-state system by exploiting Rydberg states of excitons in Cu2O. We measure a microwave–optical cross-Kerr coefficient of B0 = 0.022 ± 0.008 m V−2 at 4 K, which is several orders of magnitude larger than other solid-state systems. The results are in quantitative agreement with a nonlinear susceptibility model based on the giant microwave dipole moment between nearby excitonic states. Our results highlight the potential of Rydberg excitons for nonlinear optics and form the basis for a microwave–optical frequency converter based on Cu2O.

Color-Based Time Synchronization for Future Networks: Advantages, System Architecture, and Potential Use Cases

Color-Based Time Synchronization for Future Networks: Advantages, System Architecture, and Potential Use Cases

Dynamic Co-Embedding Model for Temporal Attributed Networks.

In this article, we study the problem of embedding temporal attributed networks, with the goal of which is to learn dynamic low-dimensional representations over time for temporal attributed networks. Existing temporal network embedding methods only learn the representations for nodes, which are unable to capture the dynamic affinities between nodes and attributes. Moreover, existing co-embedding methods that learn the static embeddings of both nodes and attributes cannot be naturally utilized to obtain their dynamic embeddings for temporal attributed networks. To address these issues, we propose the dynamic co-embedding model for temporal attributed networks (DCTANs) based on the dynamic stochastic state-space framework. Our model captures the dynamics of a temporal attributed network by modeling the abstract belief states representing the condition of the nodes and attributes of current time step, and predicting the transitions between temporal abstract states of two successive time steps. Our model is able to learn embeddings for both nodes and attributes based on their belief states at each time step of the temporal attributed network, while the state transition tendency for predicting the future network can be tracked and the affinities between nodes and attributes can be preserved. Experimental results on real-world networks demonstrate that our model achieves substantial performance gains in several static and dynamic graph mining applications compared with the state-of-the-art static and dynamic models.

Using Convolutional Neural Networks for Blocking Prediction in Elastic Optical Networks

This paper presents a study on connection-blocking prediction in Elastic Optical Networks (EONs) using Convolutional Neural Networks (CNNs). In EONs, connections are established and torn down dynamically to fulfill the instantaneous requirements of the users. The dynamic allocation of the connections may cause spectrum fragmentation and lead to network performance degradation as connection blocking increases. Predicting potential blocking situations can be helpful during EON operations. For example, this prediction could be used in real networks to trigger proper spectrum defragmentation mechanisms at suitable moments, thereby enhancing network performance. Extensive simulations over the well-known NSFNET (National Science Foundation Network) backbone network topology were run by generating realistic traffic patterns. The obtained results are later used to train the developed machine learning models, which allow the prediction of connection-blocking events. Resource use was continuously monitored and recorded during the process. Two different Convolutional Neural Network models, a 1D CNN (One-Dimensional Convolutional Neural Network) and 2D CNN (Two-Dimensional Convolutional Neural Network), are proposed as the predicting methods, and their behavior is compared to other conventional models based on an SVM (Support Vector Machine) and KNN (K Nearest Neighbors). The results obtained show that the proposed 2D CNN predicts blocking with the best accuracy (92.17%), followed by the SVM, the proposed 1D CNN, and KNN. Results suggest that 2D CNN can be helpful in blocking prediction and might contribute to increasing the efficiency of future EON networks.

High‐Speed GaN‐Based 405 nm Violet Superluminescent Diode with Tilted Facet for Visible Light Communications

Visible light communication (VLC) based on superluminescent diode (SLD) is a promising technology with great potential for future 6G optical wireless network. In this work, a 405 nm violet SLD device is fabricated with an 8° tilted facet at one end of the waveguide based on InGaN/GaN quantum wells. The device has an optical power of 14.67 mW under continuous‐wave operation. The full width at half maximum (FWHM) of the SLD at 400 mA is 12.52 nm, which is significantly larger than that in a violet laser diode (LD). Using the SLD as transmitter in VLC system, a high data transmission rate of 3.299 Gbps has been achieved using the discrete multiple tone (DMT) modulation. The corresponding bit error ratio (BER) is measured to be 1.67 × 10−3, which satisfies the forward error correction (FEC) threshold of 3.8 × 10−3. The results suggest that such device can be used as a promising light source for VLC and light fidelity (LiFi) applications.

Deterministic K-Identification for Future Communication Networks: The Binary Symmetric Channel Results

Numerous applications of the Internet of Things (IoT) feature an event recognition behavior where the established Shannon capacity is not authorized to be the central performance measure. Instead, the identification capacity for such systems is considered to be an alternative metric, and has been developed in the literature. In this paper, we develop deterministic K-identification (DKI) for the binary symmetric channel (BSC) with and without a Hamming weight constraint imposed on the codewords. This channel may be of use for IoT in the context of smart system technologies, where sophisticated communication models can be reduced to a BSC for the aim of studying basic information theoretical properties. We derive inner and outer bounds on the DKI capacity of the BSC when the size of the goal message set K may grow in the codeword length n. As a major observation, we find that, for deterministic encoding, assuming that K grows exponentially in n, i.e., K=2nκ, where κ is the identification goal rate, then the number of messages that can be accurately identified grows exponentially in n, i.e., 2nR, where R is the DKI coding rate. Furthermore, the established inner and outer bound regions reflects impact of the input constraint (Hamming weight) and the channel statistics, i.e., the cross-over probability.

High‐Performance GdNiO3 Epitaxial Film Memristor for Neuralactivity Analysis

AbstractPerovskite‐type rare earth nickelates based memristor have recently attracted extensive attention in the field of novel storage computing due to their special electronic structure and exotic physical properties. However, there is still a shortage of memristors with ultra‐high stability performance, which will provide a solid foundation for future neural network computing with high accuracy recognition rates. Here, a GdNiO3‐based interfacial memristor is presented, which possesses ultra‐high stable performance, such as electroforming‐free, low device‐to‐device variation, reliable cyclic switching, high on/off ratio (≈104) and stable pulse modulation of conduction. Combined with the comprehensive microstructure results, this behavior is ascribed to the interface Schottky barrier variation caused by the 1D oxygen vacancy channel conduction according to the transmission electron microscopy results. In particular, based on the device's stable pulse modulation plasticity performance, the study also succeeds in achieving highly accurate neural firing pattern recognition up to ≈99.75% accuracy and monitoring of pattern transitions by implementing a reservoir computing system based on the device. This research advances the progress of nickelates in novel storage computing and paves the way for future efficient memristor‐based reservoir computing systems to handle more complex temporal tasks.

Gravitating towards Internet of Things: Prospective Applications, Challenges, and Solutions of Using IoT

In the contemporary world, the Internet of Things has been utilized enormously in individual and business sectors. The purpose of the paper is to determine the prospective areas for utilizing IoT. Moreover, it also identifies the challenges and solutions of using IoT in different sectors. The study mostly employs qualitative methodologies, utilizing secondary data and articles to conduct this review article. The researcher employed the PRISMA 2020 platform to discern and select pertinent studies and reports from indexed publications such as Scopus, Web of Science, PubMed, DOAJ, and others. The study used primarily secondary methods of data collection because the idea of Internet of Things technology is still relatively new and not yet used by all sectors of Bangladesh's economy. The review paper might not include pertinent research that was published in other languages or in publications that were not indexed, which could potentially introduce a bias stemming from language and ordering. From the point of implication portion, IoT would be the driving force behind the development of future wireless sensor networks, which will feature enhanced connection, intelligence, and application diversity in different sectors.

Improving the performance of low-frequency magnetic energy harvesters using an internal magnetic-coupled mechanism

In this study, we present a novel low-frequency magnetic field energy harvester (EH) employing beryllium bronze/Pb(Zr,Ti)O3 ceramic composited dual-beam structures with tip magnets attached to the inner and outer beams. This design incorporates the internal magnetic-coupled (IMC) effect, resulting in significantly enhanced coupling ability and a wide bandwidth. The validity of the IMC mechanism is confirmed through theoretical formulas and numerical simulations. By leveraging the IMC condition, the EH achieves an expanded bandwidth, which increases from 22 to 43 Hz. Moreover, the total output voltages at the inherent resonance and internal resonance are boosted by 15.4% and 32%, respectively. The performance of the IMC-EH can be further improved by increasing the number of the endmost magnets. Experimental investigations reveal that the IMC-EH generates a maximum RMS output power density of 56.25 μW Oe−2 cm−3, surpassing existing magnetically coupled piezoelectric energy harvesters. Remarkably, even under an ambient magnetic field as low as 1 Oe, the proposed IMC-EH still yields a total output power of 185 μW, sufficient to continuously power 26 LEDs in real time. This demonstrates its potential as a promising solution for low-power consumption small electronics. Furthermore, the implications of this work extend beyond its immediate benefits, as it inspires the design of future self-powered wireless sensor networks in the context of the Internet of Things.

Intelligent reflecting surfaces assisted integrated information and energy networks in D2D communications

AbstractIntelligent Reflecting Surface (IRS) is set to revolutionize future wireless networks, particularly in Device to Device (D2D) communication. Its unique intelligence lies in dynamically reshaping signal propagation and enhancing network performance. The proposed model ensures receivers in a D2D network are accessible only through strategically placed IRS elements, optimizing signal propagation and coverage. Simulations demonstrate the solution's effectiveness, enhancing communication for Information Users (IUs) and energy harvesting for Energy Users (EUs) while meeting SINR constraints. Our efficient algorithms tackle the non‐convex problem, enabling exclusive transmission of information signals from the AP to serve both IUs and EUs with improved performance.

A review of beamforming microstrip patch antenna array for future 5G/6G networks

With the increase in demand for high data rates and high bandwidth because of multiple users all over the globe, the technology has moved toward the next-generation of wireless communication. This rapid advancement of wireless communication technologies has led to the emergence of 5G networks, which promise significantly higher data rates, lower latency, and enhanced connectivity. Researchers believe that five essential techniques can enable 5G. Beamforming is one of those essentials, as it plays a vital role in achieving reliable and high-capacity communication. This review article portrays a comprehensive analysis of the 5G beamformer Microstrip Patch Antenna array techniques for communication systems. The paper comprises of a deep overview of the fundamental concepts and principles of beamforming, including analog, hybrid, and digital beamforming techniques. It explores the advantages and disadvantages of each approach and discusses their suitability for 5G applications. An in-depth examination of various beamforming techniques employed in 5G, encompassing traditional beamforming, massive Multiple-Input-Multiple-Output beamforming, hybrid beamforming, and adaptive beamforming. The discussion encompasses the strengths, weaknesses, and performance trade-offs of each technique, along with their applicability in diverse deployment scenarios and applications. The review of multiple couplers that are used for the feeding of the antenna is discussed with included hybrid coupler, Wilkinson power divider, branch line coupler, and butler matrix in beamformer smart antenna for 5G/6G communications. Numerous beamforming techniques are compared based on their merits, demerits, and applications. Moreover, the dielectric substrate utilized to design the beamformer was also reviewed. The findings presented in this paper serve as a valuable resource for the researcher, scholars, and engineers working in the field of 5G wireless communications and antenna designing, facilitating the development and deployment of efficient and robust beamforming solutions for future 5G networks.