Search Results (559)

This study addresses the optimisation of latent heat thermal energy storage (LTES) composites for construction applications by utilising mechanistic modelling. The work focuses on enhancing the performance of phase change materials (PCMs) incorporated into expanded perlite (EP) for building energy efficiency by delivering sorption capacity models analysing factors such as particle size, surface area, and pore volume, particularly highlighting the performance of EP as a PCM carrier due to its high porosity (around 90%) and large surface area (up to 20 m²/g), which allowed for improved energy storage density and heat transfer. Key challenges in the integration of PCMs into construction materials, such as limited thermal conductivity and leakage during phase transitions, are explored. The model evaluates key parameters affecting sorption, such as temperature, pressure, and surface characteristics of the materials. The results indicate that while higher temperatures enhance sorption in larger pores, they reduce efficiency in smaller ones, leading to a slight overall decrease in total sorption capacity at elevated temperatures. The sorption capacity of water is a value slightly above 2 kg/kg EP, while the PCM RT27 exhibits a sorption capacity of 0.59 kg/kg EP. These results represent the optimised sorption performance in terms of temperature between 40 °C and 50 °C. Furthermore, applying vacuum impregnation is investigated in relation to the pore radii of the EP particles. Larger pore radii show a noticeable improvement in overall sorption capacity from 0.59 kg/kg EP to 0.68 kg/kg EP as pressure increases, especially beyond 4 × 10⁵ Pa. The contribution of inter-particle sorption remains stable, while the intra-particle sorption in large pores drives the overall capacity upward. The findings convey significant findings in optimising the design of LTES-enhanced composites for improved energy storage, thermal regulation, and structural integrity in building applications. Full article

The use of a static modulation coding scheme (MCS), such as 7, and resource keep probability ($P_{rk}$) value, such as 0.8, was proven to be insufficient to achieve the best packet reception ratio (PRR) performance. Various adaptation techniques have been used in the following years. This work introduces a novel optimization algorithm approach called the fuzzy inference reinforcement learning (FIRL) sequence for adaptive parameter configuration in cellular vehicle-to-everything (C-V2X) mode-4 communication networks. This innovative method combines a Sugeno-type fuzzy inference system (FIS) control system with a Q-learning reinforcement learning algorithm to optimize the PRR as the key metric for overall network performance. The FIRL sequence generates adaptive configuration parameters for $P_{rk}$ and MCS index values each time the Long-Term Evolution (LTE) packet is generated. Simulation results demonstrate the effectiveness of this optimization algorithm approach, achieving up to a 169.83% improvement in performance compared to static baseline parameters. Full article

The evolution from 5G to 6G networks is driven by the need to meet the stringent requirements, i.e., ultra-reliable, low-latency, and high-throughput communication. The new services are called Further-Enhanced Mobile Broadband (feMBB), Extremely Reliable and Low-Latency Communications (ERLLCs), Ultra-Massive Machine-Type Communications (umMTCs), Massive Ultra-Reliable Low-Latency Communications (mURLLCs), and Mobile Broadband Reliable Low-Latency Communications (MBRLLCs). Network slicing emerges as a critical enabler in 6G, providing virtualized, end-to-end network segments tailored to diverse application needs. Despite its significance, existing datasets for slice selection are limited to 5G or LTE-A contexts, lacking relevance to the enhanced requirements. In this work, we present a novel synthetic dataset tailored to 6G network slicing. By analyzing the emerging service requirements, we generated traffic parameters, including latency, packet loss, jitter, and transfer rates. Machine Learning (ML) models like Random Forest (RF), Decision Tree (DT), XGBoost, Support Vector Machine (SVM), and Feedforward Neural Network (FNN) were trained on this dataset, achieving over 99% accuracy in both slice classification and handover prediction. Our results highlight the potential of this dataset as a valuable tool for developing AI-assisted 6G network slicing mechanisms. While still in its early stages, the dataset lays a foundation for future research. As the 6G standardization advances, we aim to refine the dataset and models, ultimately enabling real-time, intelligent slicing solutions for next-generation networks. Full article

This work investigates the impact of rainfall on cellular communication links, leveraging smartphone-collected measurements. While existing studies primarily focus on line-of-sight (LoS) microwave propagation environments, this work explores the impact of rainfall on typical signal metrics over cellular links when the LoS path is not guaranteed. We examine both small-scale and large-scale variations in signal measurements across dry and rainy days, considering diverse locations and time windows. Through statistical and spectral analysis of a large dataset, we uncover novel insights into how rainfall influences cellular communication links. Specifically, we observe a consistent daily fluctuation pattern in key cellular metrics, such as the reference signal received quality. Additionally, spectral features of key mobile metrics show noticeable changes during rainfall events. These findings, consistent across three distinct locations, highlight the significant impact of rainfall on everyday cellular links. They also suggest that the widely available by-product signals from mobile phones could be leveraged for innovative rainfall-sensing applications. Full article

To address the severe spectrum shortage in mobile networks, the 3rd Generation Partnership Project (3GPP) standardized Long Term Evolution (LTE)-License Assisted Access (LAA) technology. The LTE-LAA system ensures efficient coexistence with other existing unlicensed systems by incorporating listen-before-talk functionality and conducting random backoff operations similar to those in the IEEE 802.11 distributed coordination function. In this paper, we propose an analytical model to calculate the throughput of each system in a scenario where a single LTE-LAA system shares an unlicensed channel with multiple wireless local area network (WLAN) systems. The LTE-LAA system is utilized for supplementary downlink transmission from the LTE-LAA eNodeB (eNB) to LTE-LAA devices. Our proposed analytical model uses a Markov chain to represent the random backoff operations of the LTE-LAA eNB and WLAN nodes under non-saturated traffic conditions and to calculate the impact of the clear channel assessment (CCA) performed by the LTE-LAA eNB. Through numerical results, we demonstrate how the throughput of both the LTE-LAA and WLAN systems is determined by the contention window size and CCA threshold of the LTE-LAA eNB. Full article

To support the coexistence of long-term evolution (LTE)-license-assisted access (LAA) and wireless local area network (WLAN) in unlicensed bands, the load-based listen-before-talk (LB-LBT) scheme has been developed, incorporating channel sensing and backoff functions similar to those used in WLAN. In the LB-LBT scheme, the contention window size and clear channel assessment (CCA) threshold of the LTE-LAA eNodeB (eNB) significantly influences its transmission probability and the interference caused by concurrent WLAN transmissions outside the CCA range. However, most existing LB-LBT schemes use fixed contention window sizes and CCA thresholds, irrespective of the channel congestion status. To address this limitation, in this paper, we propose a channel measurement-based LBT (CM-LBT) scheme to enhance overall system throughput while ensuring fairness between LTE-LAA and WLAN systems. Our proposed CM-LBT scheme adaptively adjusts the contention window size and CCA threshold of LTE-LAA eNB in the LB-LBT scheme, according to the current channel access activities of LTE-LAA and WLAN systems. Through simulations, we evaluate the performance of our proposed CM-LBT scheme against existing LBT schemes by assessing the throughput of LTE-LAA and WLAN systems, as well as the fairness between them, using a reward function. Full article

This paper presents a circularly polarized (CP) microstrip antenna with dual circular polarization that is fed using a 3 dB 90° hybrid coupler to ensure dual-CP radiation. The proximity-coupled structure is used as a feeding technique to obtain a larger operational bandwidth that covers the standard LTE 43 of the 5G mid-band applications (3.6–3.8 GHz). The antenna was fabricated on an FR4 dielectric substrate with overall dimensions of 66.1 × 35 × 1.6 ${\mathrm{m}\mathrm{m}}^3$. A circular patch was formed as a radiating element, with four identical slits that were etched on the patch to guarantee the presence of CP radiation at the required mid-band of the 5G frequency range. The measured impedance bandwidth (BW) of this antenna was 10.6% (3.46–3.85 GHz), while the 3 dB axial ratio bandwidth (ARBW) was 8.2% along the range (3.50–3.80 GHz). The measured average gain was about 1.3 dBi along the antenna impedance bandwidth. Full article

The United Nations has pushed for improved mobile connectivity, ensuring that 97% of the world’s population lives within reach of a mobile cellular signal. This is within the framework of objective nine regarding industry, innovation, and infrastructure for sustainable development. The next challenge is for users to know the quality of this service. The Long-Term Evolution (LTE) network’s quality of service (QoS) is evaluated with key performance indicators (KPI) that only specialists can interpret. This work aims to assess the QoS and effectiveness of the fourth-generation (4G) LTE network using a weighted fuzzy inference system. Analytic Hierarchy Process (AHP) is integrated to rank the fuzzy rules. The KPIs that are considered for the evaluation are download speed, upload speed, latency, jitter, packet loss rate, reference received signal power (RSRP), and reference received signal quality (RSRQ). The evaluated data were collected collaboratively with end-user equipment (UEs). Different usage scenarios are contemplated to define the importance according to the positive impact of the QoS of the LTE mobile network. The advantage of the weighted fuzzy inference system concerning the fuzzy inference system is that each KPI is assigned a different weight, which implies having rules with hierarchies. In this way, the weighted fuzzy inference system provides two indices of quality and effectiveness. It can be a valuable tool for end users and regulatory bodies to identify the quality of the LTE mobile network. Full article

Protonated hydrogen cyanide, HCNH⁺, is one of the most important molecules of interest in the astrophysical and astrochemical fields. This molecule not only plays the role of a reaction intermediary in various types of interstellar reactions but was also identified in Titan’s upper atmosphere. The cross sections for the dissociative recombination (DR) and vibrational (de-)excitation (VE and VDE) of HCNH⁺ and its ${\mathrm{CNH}}_2^{+}$ isomer are computed using a theoretical approach based on a combination of the normal mode approximation for the vibrational states of the target ions and the UK R-matrix code to evaluate electron-ion scattering matrices for fixed geometries of ions. The theoretical convoluted DR cross section for HCNH⁺ agrees well with the experimental data and a previous study. It was also found that the DR of the ${\mathrm{CNH}}_2^{+}$ isomer is important, which suggests that this ion might be present in DR experiments of HCNH⁺. Moreover, the ab initio calculations performed on the H₂CN⁺ isomer predict that this ion is a transition state. This result was confirmed by the study of the reaction path of the HCNH⁺ isomerization that was carried out by evaluating the intrinsic reaction coordinate (IRC). Finally, thermally averaged rate coefficients derived from the cross sections are provided for temperatures in the 10–10,000 K range. A comprehensive set of calculations is performed to assess the uncertainty of the obtained data. These results should help in modeling non-LTE spectra of HCNH⁺, taking into account the role of its most stable isomer, in various astrophysical environments. Full article

When it comes to the development of 4G and 5G technologies, long-range IoT or machine-to-machine (M2M) communication can be achieved with the help of cellular infrastructure. In non-standalone (NSA) 5G infrastructure, cellular-IoT (C-IoT) devices are attached and authenticated by a 4G core network even if it is connected to a 5G base station. In an NSA-based 5G network, the presence of dual connectivity sometimes raises interoperability and authentication issues due to technological differences between LTE and 5G. An attacker explores these technological differences, introduces the threats, and performs various types of attacks like session hijacking at the interfaces and Man-in-the-Middle (MITM) attacks. With the introduction of these attacks, the attackers exploit the network resources and pinch out various critical information sources. To resolve this issue, the NSA-based C-IoT network must incorporate robust and seamless authentication and authorization mechanisms. This article presents the ML-AKA protocol that is used to enhance interoperability and trust between 4G and 5G networks by using a uniform key-sharing (UKS) mechanism. The proposed ML-AKA protocol is analyzed with the help of the AVISPA tool and validated with the use of Proverif. Further, the proposed protocol is compared with other existing protocols like EPS-AKA and UAKA-D2D, and the outcome shows that the proposed protocol significantly reduces the chances of MITM, DDOS and Spoofing attacks during the interoperability in the NSA-C-IoT network. Full article

This paper describes an analysis of power supply rejection and noise improvement techniques for an envelope-tracking power amplifier. Although the envelope-tracking technique improves efficiency, its power supply rejection ratio is much lower than that of average power tracking or a fixed-supply power amplifier. In FDD systems with the envelope-tracking technique, the low power supply rejection ratio generates much output noise in the RX band and degrades the receiver’s sensitivity. An SM is designed by using a 130 nm CMOS process, and the chip die area is 2 × 2 mm² with a 25-pin wafer-level chip-scale package. The designed SM achieved peak efficiencies of 78–83% for LTE signals with a 5.8 dB PAPR and various channel bandwidths. For the low-output-noise-supply modulator, noise reduction techniques using resonant-frequency tuning and a notch filter are employed, and the measured results show maximum 1.8/5/5.3/3.8/3 dB noise reduction in LTE bands B17/B5/B2/B3/B7, respectively. Full article

This study introduces a low-cost alternative method for mapping the electric field strength from 4G LTE base stations and identifies areas where this mapping is more accurate. A drive test campaign was conducted in the urban environment of Thessaloniki, Greece, using data obtained from three identical smartphones, each connected to a different mobile operator and an exposimeter. The smartphones used a mobile application to record Reference Signal Received Power (RSRP) values, while the exposimeter measured the electric field strength in selected frequency bands. In the first part, the variability of the received power over different periods within certain areas was studied, and the reasons for this variability were identified. In the second part, a linear factor was calculated to convert RSRP values into electric field strength using data from both the application and the exposimeter. The converted RSRP values were subsequently compared with the exposimeter data for validation. The results indicate that in areas where the variability of the received power is lower, the linear relationship between smartphone and exposimeter data is statistically stronger resulting in calculated electric field strength values are closer to the measured. Full article

In recent years, the increasing number of internet-connected devices has exceeded the capacity of fourth-generation (4G) cellular networks, leading to the development of fifth-generation (5G) technology, designed to offer higher speeds, greater bandwidth, and lower latency. In this context, this study evaluated Universal Filtered Multi-Carrier (UFMC) and Generalized Frequency Division Multiplexing (GFDM) techniques, implementing them in a radio-over-fiber (RoF) system and a Next-Generation Radio Access Network (NG-RAN) fronthaul link, and compared the results using communication quality metrics such as bit error rate (BER). Additionally, through signal generation and processing in Matlab, the performance of UFMC and LTE signals was analyzed, confirming that simultaneous transmission over an RoF channel allows for efficient signal separation in the frequency domain, with the UFMC giving power to LTE. Full article

Our long journey on the road of telecommunications is continuously evolving. We have experienced several technological changes, modernizations, optimizations, and various mergers in the past decades. Virtualization and ‘cloudification’ of legacy telecommunication equipment has made communication networks not only more flexible, but also opened new doors. Brand new types of services have become available thanks to the ongoing fusion of the two domains of telecommunications and IT (Information Technology). This overview paper first discusses the evolution of services with an enhanced focus on mobile networks. Then, the possibilities offered by IT are shown. Finally, some examples are given of how Communication Service Providers and end users can benefit from these recent changes. Full article

As the focus of space exploration has recently shifted from national efforts to private enterprises, interest in the space industry has increased. With the rising number of satellite launches, the risk of collisions between satellites and between satellites and space debris has grown, which can lead not only to property damage but also casualties caused by the debris. To address this issue, various machine learning and deep learning-based methods have been researched to improve the accuracy of satellite orbit prediction and mitigate these risks. However, most studies have applied basic machine learning models to orbit prediction without considering the model size and execution time, even though satellite operations require lightweight models that offer both a strong prediction performance and rapid execution. In this study, we propose a time series forecasting framework, the Lightweight Transformer Encoder (LTE), for satellite orbit prediction. The LTE is a prediction model that modifies the encoder structure of the Transformer model to enhance the accuracy of satellite orbit prediction and reduce the computational resources used. To evaluate its performance, we conducted experiments using about 4.8 million data points collected every minute from January 2016 to December 2018 by the KOMPSAT-3, KOMPSAT-3A, and KOMPSAT-5 satellites, which are part of the Korea Multi-Purpose Satellite (KOMPSAT) series operated by the Korea Aerospace Research Institute (KARI). We compare the performance of our model against various baseline models in terms of prediction error, execution time, and the number of parameters used. Our LTE model demonstrates significant improvements: it reduces the orbit prediction error by 50.61% in the KOMPSAT-3 dataset, 42.40% in the KOMPSAT-3A dataset, and 30.00% in the KOMPSAT-5 dataset compared to the next-best-performing model. Additionally, in the KOMPSAT-3 dataset, it decreases the execution time by 36.86% (from 1731 to 1093 s) and lowers the number of parameters by 2.33% compared to the next-best-performing model. Full article