Network Quality Research Articles

Machine Learning for Wireless Network Throughput Prediction

This paper analyzes a dataset containing radio frequency (RF) measurements and Key Performance Indicators (KPIs) captured at 1876.6MHz with a bandwidth of 10MHz from an operational 4G LTE network in Nigeria. The dataset includes metrics such as RSRP (Reference Signal Received Power), which measures the power level of reference signals; RSRQ (Reference Signal Received Quality), an indicator of signal quality that provides insight into the number of users sharing the same resources; RSSI (Received Signal Strength Indicator), which gauges the total received power in a bandwidth; SINR (Signal to Interference plus Noise Ratio), a measure of signal quality considering both interference and noise; and other KPIs, all derived from three evolved node base stations (eNodeBs). After meticulous data cleaning, a subset of measurements from one serving eNB, spanning a 20-minute duration, was selected for deeper analysis. The PDCP DL Throughput, as a vital KPI metric, plays a paramount role in evaluating network quality and resource allocation strategies. Leveraging the high granularity of the data, the primary aim was to predict throughput. For this purpose, I compared the predictive capabilities of two machine learning models: Linear Regression and Random Forest. Metrics such as Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used to examine the models as they offer a comprehensive insight into the models’ accuracies. The comparative analysis highlighted the superior performance of the Random Forest model in predicting the PDCP DL Throughput. The insights derived from this research can potentially guide network engineers and data scientists in optimizing network performance, ensuring a seamless user experience. Furthermore, as the telecommunication industry advances towards the integration of 5G and beyond, the methodologies explored in this paper will be invaluable in addressing the increasingly complex challenges of future wireless networks.

Probabilistic and Harmonic Assessment of Power Quality Parameters from Distributed Energy Resources in a Distribution Network

Distributed energy resources (DERs) are leading actors in energy transition worldwide. Despite the environmental benefits, DERs are intermittent (consumption/generation power changes according to the input variables), and the use of power electronic devices affects the power quality in distribution networks. Typically, models and simulations (deterministic or probabilistic) are proposed to estimate such impacts. However, previous studies have analyzed the main effects caused by one or two DERs during a day. These studies had a reduced level of detail in the applied methodology in modeling and impact evaluation, or they did not consider that the harmonic distortion level depends on input variables. The aim of this article was to develop a probabilistic harmonic simulation approach to characterize the hourly effect of photovoltaic systems (PVS), battery energy storage systems, and electric vehicle charging stations in a 34-node IEEE test feeder. The individual and collective effect estimation of the DERs consists of the hourly quantification of the effective voltage value, voltage unbalance, power factor, and harmonic distortion, with data obtained using Python and PowerFactory co-simulation for 23 operation scenarios. These impacts were assessed through indicators proposed in the literature and hypothesis tests (inferential statistics). Each DER generally operates at a specific time, making it easy to identify the individual effects of network parameters. The PVS increased the effective voltage value and total harmonic distortion. However, electric vehicles and batteries increase the voltage unbalance and power factor due to the two-phase connection in some network nodes.

Current state and problems of reliability of power supply systems of electrical complexes of industrial enterprises

In accordance with the Energy Strategy of Russia until 2035 and the Decree of the Government of the Russian Federation "Improving the reliability and quality of power supply to consumers of electric energy through the introduction of new technologies and optimization of the activities of territorial grid organizations", the issues of improving the reliability of power supply of electrical complexes of industrial enterprises in a market economy are of particular importance.This article presents the results of a survey of managers and specialists of the services of the chief power engineer of industrial enterprises, representatives of design institutes, equipment manufacturers, university researchers on the existing problems of reliability of power supply systems of industrial enterprises. The expediency of substantiating the identification of the current situation by the main reliability indicators and their relationship in the existing economic conditions for calculating the reliability of power supply systems is substantiated. A comparative analysis of the responses of respondents of energy services is presented, their interpretation is given.The main problems in the following blocks are identified: the quality of the enterprise's energy supply and its reliability; the reliability of energy distribution within the enterprise and control of the influence of installed electrical equipment on the quality of the electrical network; knowledge regarding reliability and interest in carrying out such calculations. Recommendations are given on the identified problems, including the analysis of individual indicators of reliability of power supply to industrial enterprises.

Рекреационный потенциал речных бассейнов для развития сельского туризма в Актюбинской области

The aim of the research was to analyze the climate comfort and assess the recreational potential of river basins for the development of rural tourism in Aktobe Region, Kazakhstan. The ArcGIS 10.5 software was applied to conduct tourist and recreational studies in the target watersheds. Methodologically, the research included two stages. The first one comprised an overview of the available data on local hydrology, terrain (slope degrees), and climatic factors. The efforts allowed creating four thematic maps based on ArcGIS 10.5 SRTM (Shuttle Radar Topographical Mission) imagery of the basins in question. The second stage revealed the tourist and recreational efficiency of river massifs, specifically allowed evaluating the recreational use of rivers as per the proposed methodology. It was found that surface water bodies are not used effectively enough in rural tourism development in the region, i.e. that this type of touristic services has not yet progressed due to numerous issues hindering their spreading, including the underdeveloped social and engineering rural infrastructure, poor quality of road network and transport communications. All these lead to low attractiveness, diminish the effectiveness of rural ecology preservation measures, as well as complicate the organization and creation of proper tourist accommodations. To analyze the possibilities of rural tourism development in specific areas, a special tourist and recreational assessment was carried out within the framework of the research. According to climate index characteristics, the dynamics of the region’s climatic favorability was determined with the main conclusion that it is the poor infrastructure development in Aktobe Region settlements that prevents a full scale expansion of rural tourism in the target river basins

Persaingan Pemasaran Mengenai Kuliatas Produk Dan Jaringan Bisnis Pada Karsa Mahardika Nusantara

This article aims to determine marketing competition regarding product quality and business networks at Karsa Mahardika Nusantara. This research focuses on the marketing and quality of products and business networks that have been carried out by Karsa Mahardika Nusantara. Observation and interviews are data collection techniques in this research. Based on the results of this research analysis, it can be concluded that Karsa Mahardika Nusantara has done marketing well and is able to compete with similar competitors, even though they have to expand their marketing area, Karsa Mahardika Nusantara is confident in the quality and materials they have so that they continue to grow and develop well until now.

Acceptance Analysis of Cyclops Application in Telkomsel Pamasuka Area Using Innovation Diffusion Theory (IDT) and Technology Acceptance Model (TAM)

Purpose: In this thesis, we will examine how much employee acceptance in the Pa Masuka Area, especially “Network” and “Sales', which totals around 522 people to the CYCLOPS application using the Innovation Diffusion Theory (IDT) and Technology Acceptance Model (TAM) methods, then an online survey is conducted.
 Research methodology: Data analysis was performed using a Structural Equation Modeling (SEM) model.
 Results: Digitization of processes at Telkomsel continues to be carried out to increase the effectiveness of employee work, including in the Pausuka Area, especially in the Area Network Operations Sub-Directorate of Pausuka which is responsible for 18,000 sites serving 30 million customers based on April 2023 data.CYCLOPS is a supporting application for digitizing processes that makes it easier for employees, especially the "Network" and "Sales" sections, to observe network conditions (Live Monitoring), find out the potential for network development, make improvements that are right on target according to set priorities to carry out network quality tests.

Should we really use graph neural networks for transcriptomic prediction?

The recent development of deep learning methods have undoubtedly led to great improvement in various machine learning tasks, especially in prediction tasks. This type of methods have also been adapted to answer various problems in bioinformatics, including automatic genome annotation, artificial genome generation or phenotype prediction. In particular, a specific type of deep learning method, called graph neural network (GNN) has repeatedly been reported as a good candidate to predict phenotypes from gene expression because its ability to embed information on gene regulation or co-expression through the use of a gene network. However, up to date, no complete and reproducible benchmark has ever been performed to analyze the trade-off between cost and benefit of this approach compared to more standard (and simpler) machine learning methods. In this article, we provide such a benchmark, based on clear and comparable policies to evaluate the different methods on several datasets. Our conclusion is that GNN rarely provides a real improvement in prediction performance, especially when compared to the computation effort required by the methods. Our findings on a limited but controlled simulated dataset shows that this could be explained by the limited quality or predictive power of the input biological gene network itself.

Distributed Data-Driven Learning-Based Optimal Dynamic Resource Allocation for Multi-RIS-Assisted Multi-User Ad-Hoc Network

This study investigates the problem of decentralized dynamic resource allocation optimization for ad-hoc network communication with the support of reconfigurable intelligent surfaces (RIS), leveraging a reinforcement learning framework. In the present context of cellular networks, device-to-device (D2D) communication stands out as a promising technique to enhance the spectrum efficiency. Simultaneously, RIS have gained considerable attention due to their ability to enhance the quality of dynamic wireless networks by maximizing the spectrum efficiency without increasing the power consumption. However, prevalent centralized D2D transmission schemes require global information, leading to a significant signaling overhead. Conversely, existing distributed schemes, while avoiding the need for global information, often demand frequent information exchange among D2D users, falling short of achieving global optimization. This paper introduces a framework comprising an outer loop and inner loop. In the outer loop, decentralized dynamic resource allocation optimization has been developed for self-organizing network communication aided by RIS. This is accomplished through the application of a multi-player multi-armed bandit approach, completing strategies for RIS and resource block selection. Notably, these strategies operate without requiring signal interaction during execution. Meanwhile, in the inner loop, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm has been adopted for cooperative learning with neural networks (NNs) to obtain optimal transmit power control and RIS phase shift control for multiple users, with a specified RIS and resource block selection policy from the outer loop. Through the utilization of optimization theory, distributed optimal resource allocation can be attained as the outer and inner reinforcement learning algorithms converge over time. Finally, a series of numerical simulations are presented to validate and illustrate the effectiveness of the proposed scheme.

Taktik Komunikasi Efektif dalam Kampanye Lingkungan : Peran Humas dalam Kesadaran dan Aksi Bersama

In contemporary environmental discourse, the role of Integrated Marketing Communication (IMC) and collaborative efforts between Public Relations (PR) practitioners and environmental experts hold significant relevance. This study explores the pivotal role of communication strategies in environmental campaigns and the collaborative mechanisms between PR and environmental specialists within various organizational contexts. Estaswara (2000) underscores the strategic necessity of integrated communication in marketing, emphasizing its transformation from a mere promotional tool to a strategic business process. The findings highlight the insufficiency of environmental awareness alone in inciting tangible action. Discussions center around designing campaign messages to effectively drive concrete and sustainable behavioral changes. Collaboration between PR and environmental experts is articulated through several methods. Firstly, monitoring emissions involves joint efforts to develop and manage emission monitoring systems like Continuous Industrial Emission Monitoring Information System (CISPEK) and Continuous and Networked Effluent Water Quality Monitoring System (SPARING). Secondly, Performance Rating Program for Environmental Performance (PROPER) is used by PR to establish cooperation and enhance public trust in environmental management. Thirdly, effective communication and community engagement leverage social media and communal activities to disseminate information and amalgamate feedback from the public. The jurnal asserts the vital role of PR in fostering public participation, guiding communities on critical issues, and encouraging their contribution to sustainable development. By providing pertinent explanations, PR elevates societal awareness regarding crucial issues like environmental sustainability, climate change, and public health, stimulating active involvement in sustainable development endeavors..

Social network type contributes to purpose in life among older people, mediated by social support

A sense of Purpose in Life is an important aspect of ageing well which is related to older adult’s social relationships. Social network types and the different sources of support they provide are theorized here as a pathway to maintaining a sense of purpose as we age. The study utilized a population sample from the 2016 and 2020 NZ Health, Work, and Retirement (NZHWR) longitudinal survey waves; N = 2869 (mean age of 65.82 years (SD = 6.40). A structural equation model investigated the relationship between Social Network Type and Purpose in Life and the mediating role of Social Support. The final model had a good fit to the data and explained 33.7% of the variance in Purpose in Life. Private, Self-Contained and Family Dependent network types (2016) were negatively related to Purpose in Life 4 years later (2020). Support from sense of Attachment, Reassurance of Worth, Reliable Alliance and Social Integration played a mediating role between Social Network type and Purpose in Life. These findings highlight the importance of social networks in maintaining a sense of purpose among older adults and highlight pathways for the types of networks, and kinds of social support they offer, which contribute to a sense of purpose in life. They underscore the importance of social support for the well-being of older adults and highlight the need to consider the quality and type of social networks and support they provide, when designing interventions to enhance well-being.

Patent Research of DC Electric Drive Power Supply Systems Based on Active Converters With Power Correction

Despite the fact that the AC electric drive has recently been increasingly “displacing” the DC drive, it is quite relevant, because completely satisfies the requirements of technological processes. Consideration of the problem of electromagnetic compatibility of DC converter installations with the supply electrical network occupies a large place. The article provides research and analysis of the influence of valve loads on the quality of the power supply network and ways to improve the quality of power supply by replacing existing converters with active DC-DC converters with power factor correction, as well as a patent study.

Driving Peru’s Road Infrastructure: An Analysis of Public–Private Partnerships, Challenges, and Critical Success Factors

Peru has experienced significant growth but faces challenges with its infrastructure. Despite economic and population growth, competitiveness has not risen at the same pace. The importance of proper roads for economic development and the lack of planning have led the country to confront an infrastructure deficit. This deficit affects connectivity and the quality of the road networks, thereby influencing competitiveness. To bridge this gap, the PPP modality had been used, but challenges were encountered, ranging from the execution of works to maintenance issues. This research study pursues three main objectives: to describe the current landscape of road infrastructure in Peru; to present the Peruvian PPP system and the experience gained through implemented concession contracts; and to identify the critical success factors of Peruvian concessions and propose an evaluation methodology for future contracts. To achieve this, an empirical study of 16 operational road PPPs and a literature review of the most relevant international experiences on success variables for such contracts were conducted. The primary contribution of this study lies in presenting the Peruvian experience and identifying key success factors for this type of contracts in Peru.

DashReStreamer: Framework for Creation of Impaired Video Clips under Realistic Network Conditions

The continuous rise of multimedia entertainment has led to an increased demand for delivering outstanding user experience of multimedia content. However, modelling user-perceived Quality of Experience (QoE) is a challenging task, resulting in efforts for better understanding and measurement of user-perceived QoE. To evaluate user QoE, subjective quality assessment, where people watch and grade videos, and objective quality assessment in which videos are graded using one or many objective metrics are conducted. While there is a plethora of video databases available for subjective and objective video quality assessment, these videos are artificially infused with various temporal and spatial impairments. Videos being assessed are artificially distorted with startup delay, bitrate changes, and stalls due to rebuffering events. To conduct a more credible quality assessment, a reproduction of original user experiences while watching different types of streams on different types and quality of networks is needed. To aid current efforts in bridging the gap between the mapping of objective video QoE metrics to user experience, we developed DashReStreamer, an open-source framework for re-creating adaptively streamed video in real networks. The framework takes inputs in the form of video logs captured by the client in a non-regulated setting, along with an.mpd file or a YouTube URL. The ultimate result is a video sequence that encompasses all the data extracted from the video log. DashReStreamer also calculates popular video quality metrics like PSNR, SSIM, MS-SSIM and VMAF. Finally, DashReStreamer allows creating impaired video sequences from the popular streaming platform, YouTube. As a demonstration of framework usage we created a database of 332 realistic video clips, based on video logs collected from real mobile and wireless networks. Every video clip is supplemented with bandwidth trace and video logs used in its creation and also with objective metrics calculation reports. In addition to dataset, we performed subjective evaluation of video content, assessing its effect on overall user QoE. We believe that this dataset and framework will allow the research community to better understand the impacts of video QoE dynamics.

5G's Integration with Edge Computing

This study addresses the transformative integration of 5G networks with Edge Computing and Mobile Edge Computing (MEC) and explores the collaborative standards established by industry associations such as ETSI and 3GPP. The article explores the multiple possibilities of this integration, encompassing consumer and operator services, and meeting the demands of new technologies such as augmented reality, virtual reality and the Internet of Things. The strategic coexistence of distributed MEC is explored, while the security and privacy challenges of MEC are explored, emphasizing layered security and blockchain technologies. The study highlights the role of 5G and MEC in reshaping the communications landscape, providing affordable and efficient computing at the network edge, and improving network performance and quality of experience (QoE). As the 5G and MEC ecosystem evolves, the paper predicts a transformative impact on connectivity, speed, reliability and responsiveness across industries, and emphasizes the continued importance of research and development in shaping the future of communications and computing.

A Robust approach for mitigating load voltage imbalances using glowworm swarm optimizer for power quality enrichment

This paper addresses the paramount issue of maintaining optimal power quality in power system networks to ensure reliable operations. The paper introduces an innovative solution that effectively mitigates power quality challenges stemming from disturbances like voltage sag/swell events and harmonics. The approach combines a Dynamic Voltage Restorer (DVR) unit with a Solar PhotoVoltaic (PV) system, incorporating advanced control strategies. Leveraging the Glowworm Swarm Optimizer (GSO) Tuned Proportional-Integral-Derivative (PID) Controller and the Versatile Control on Synchronous Reference Frame (VCSRF) theory, our system achieves exceptional results. Extensive MATLAB/Simulink simulations demonstrate the superiority of the proposed DVR system over traditional PID, GA, PSO, and SSA controller-based DVR systems. Notably, our approach achieves significant reductions in Total Harmonic Distortion (THD), with reductions of 5.27 % (PID), 3.14 % (GA), 1.29 % (PSO), 0.841 % (SSA), and 0.03 % (GSO-PID), underscoring its substantial effectiveness. Practical implementation further validates the improved THD performance. The paper's findings highlight the efficiency and potential of the integrated DVR unit with a solar PV system in effectively mitigating power quality issues. These results offer valuable insights for researchers and practitioners seeking advanced solutions to power quality challenges. Future research endeavors aim to optimize and expand this approach for diverse power quality improvement applications.

Flow-based Service Time optimization in software-defined networks using Deep Reinforcement Learning

A Ternary Content-Addressable Memory (TCAM) constitutes a memory variant employed within Software Defined Networking (SDN) node flow tables. These TCAMs deliver swift processing, enabling rapid parallel lookups. Nonetheless, due to their high energy consumption and cost, TCAMs have limited dimensions. This size constraint influences rule capacity, and suboptimal rule management can degrade network quality of service. Although different techniques for flow table management have been proposed during recent years, such as eviction, idle and hard timeout mechanisms, this paper proposes a Deep-Reinforcement Learning (DRL) solution, namely DRL-Idle, that is able to maximize the service time of the flows in an SDN network without considering any assumption about their status throughout time. By means of a continuous learning, DRL-Idle is able to also minimize the number of rule installations that are required to serve the target flows. Based on the idea of dynamically modifying the idle timeout value of the flows according to their needs, DRL-Idle outperforms existing approaches aiming at solving the Rule Placement Problem, averaging a 30% increase in performance.

Microcell Planning Analysis Using Cell Splitting Method In 4G LTE Network

As the growth of technology and telecommunication network, demands of mobile technology is also raised. The quality of the network will decrease by the insufficient number of network and cell to support the growth of user, and the scope of e-Node B is not optimal. The method of cell splitting can be used to solve the problem, this method splitting macro cell network become smaller size of cell with scoping area of each without interruption. The planning of new site as microcell implemented in area of Flamboyan Baru using XL operator. As the count gained, 1 site microcell and 2 cell with the antenna of 18,5 meters and power of 38 dBm, cell splitting method could increase network quality and improve the average number of parameter RSRP, SINR, and throughput, also proving that the implementation of cell splitting in microcell could escalate the quality of LTE network, the improvement of network quality in SINR parameter with average number of 24,23 dB with category of very good, RSRP parameter with average number of -94,28 dBm with category good, and the average number of throughput reach 63.094,91 kbps with category very good.

How to evaluate a multi-country implementation-focused network: Reflections from the Quality of Care Network (QCN) evaluation.

Learning about how to evaluate implementation-focused networks is important as they become more commonly used. This research evaluated the emergence, legitimacy and effectiveness of a multi-country Quality of Care Network (QCN) aiming to improve maternal, newborn and child health (MNCH) outcomes. We examined the QCN global level, national and local level interfaces in four case study countries. This paper presents the evaluation team's reflections on this 3.5 year multi-country, multi-disciplinary project. Specifically, we examine our approach, methodological innovations, lessons learned and recommendations for conducting similar research. We used a reflective methodological approach to draw lessons on our practice while evaluating the QCN. A 'reflections' tool was developed to guide the process, which happened within a period of 2-4 weeks across the different countries. All country research teams held focused 'reflection' meetings to discuss questions in the tool before sharing responses with this paper's lead author. Similarly, the different lead authors of all eight QCN papers convened their writing teams to reflect on the process and share key highlights. These data were thematically analysed and are presented across key themes around the implementation experience including what went well, facilitators and critical methodological adaptations, what can be done better and recommendations for undertaking similar work. Success drivers included the team's global nature, spread across seven countries with members affiliated to nine institutions. It was multi-level in expertise and seniority and highly multidisciplinary including experts in medicine, policy and health systems, implementation research, behavioural sciences and MNCH. Country Advisory Boards provided technical oversight and support. Despite complexities, the team effectively implemented the QCN evaluation. Strong leadership, partnership, communication and coordination were key; as were balancing standardization with in-country adaptation, co-production, flattening hierarchies among study team members and the iterative nature of data collection. Methodological adaptations included leveraging technology which became essential during COVID-19, clear division of roles and responsibilities, and embedding capacity building as both an evaluation process and outcome, and optimizing technology use for team cohesion and quality outputs.

RDVFF- Reliable Data Dissemination in Vehicular Ad Hoc Networks Based on Validation of Far to Farthest Zone

<p>The rise of connected and autonomous vehicles has resulted in the development of vehicular ad hoc networks (VANETs) as a means of improving road safety, traffic efficiency, and passenger comfort. However, VANETs present challenges to achieving reliability and service quality in networks suffered highly by broadcast storms due to their mobility, scalability, and high node density. By addressing such challenges, this paper seeks to contribute to solving the problem of efficient information dissemination by diluting the broadcast storm and coverage issues by proposing a novel relay selection scheme based on the validation of the far-to-farthest zone technique. A flag control suppression mechanism is presented to overcome repeated information problems. RDVFF includes a separate algorithm for the optimization of participants for relay inclusion to address the latency issue during communication based on a multi-directional multiple-selection scheme. Compared with state-of-art protocols in terms of throughput, delivery ratio, collision, and latency experimental results show improvement of an average of 21.27%, 18.34%, 36.76%, and 38.04% respectively.</p> <p> </p>

Enhancing Wireless Sensor Network Lifetime through Energy-Efficient Data Clustering and Compressed Forwarding in video Processing

Wireless Sensor Networks (WSN) play a crucial role in diverse data gathering applications, but face a significant challenge in the form of limited energy reserves within sensor nodes. Enhancing the network's Quality of Service, particularly its lifetime, is paramount. Prolonging the network's operational span hinges on mitigating energy consumption, with communication accounting for a substantial portion of nodal power usage. By reducing data transmission, not only can energy consumption be curtailed, but also bandwidth requirements and network congestion can be minimized. In the context of Wireless Sensor Networks, the Distributed Similarity-based Clustering and Compressed Forwarding (DSCCF) approach strives to construct data-similar iso-clusters with minimal communication overhead. This technique involves extracting trend and magnitude components from lengthy data series using an LMS filter, resulting in what is termed data projection. Data similarity between nodes is assessed by measuring the Euclidean distance between these data projections, thereby facilitating efficient and low-overhead iso-cluster formation. To further economize intra-cluster communication, an adaptive-nLMS-based dual prediction framework is employed. During each data collection round, the cluster head holds instantaneous data for each cluster member, using either prediction or direct data communication. Furthermore, inter-cluster data is reduced via a multi-level lossless compressive forwarding technique. Impressively, this proposed approach has achieved an 80% reduction in data while maintaining optimal data accuracy for the collected information. The transmission of inter-cluster data exclusively occurs through a network backbone comprised solely of cluster heads. Initially, the cluster heads establish this network backbone. Each cluster head dispatches a link request query towards the sink through the backbone, receiving a link reply message containing path length and the weakest link of the path. The cluster head repeats this process for each available path, subsequently selecting the most optimal path based on the acquired information and its reliability in terms of link quality