Services In Cellular Networks Research Articles

Optimization of Quality of Service in 5G Cellular Network by Focusing on Interference Management

Optimization of Quality of Service in 5G Cellular Network by Focusing on Interference Management

Jual Beli HP Tidak Ada Layanan Jaringan di Angkola Muaratais Tapanuli Selatan Perspektif Wahbah Az-Zuhaili

Buying and selling is a legal act that has existed since the existence of human civilization. In connection with the sale and purchase of mobile phones, there are often undue events where the seller is not responsible for the implementation of khiyar aib, this is experienced by consumers in buying and selling mobile phones with obstacles to no cellular network services due to illegal IMEI, duplication of IMEI and the use of temporary IMEI. If the IMEI of the cellphone is not officially registered, one day it may be subject to restrictions on the use of cellular network services. Meanwhile, when asked for accountability, the seller did not respond to consumer complaints, because the seller claimed to have tried the goods before handing over to the buyer. The research method used in this research is qualitative field research, the authors will reveal the meaning of the behavior and reality of the surrounding community. Primary data is a source of data that is directly obtained from the first source, namely people who buy unofficial cellphones at the counter. Secondary data is data obtained indirectly from data sources, secondary data in this study is data derived from literature (library research), namely research by examining and reading books or books, articles, and websites related to khiyar aib.

Impact Assessment of Elevation Angles on Signal Propagation at VHF and UHF Frequencies for Improved Rural Telephony

Rural telephony is challenging in the remote part of Nigeria due to inadequate telecommunication infrastructure, exorbitant cost of communication systems and poor road network for extension of fiber network. These factors constitute poor or no cellular network services in many villages. Alternatively, using Television White Space (TVWS) technology to facilitate telephony services in the rural areas through Ultra High Frequency (UHF) and Very High Frequency (VHF) spectrum is cost effective. Thus, this research investigates impact of elevation angle on signal propagation at UHF/VHF frequencies. The experimental test scenarios took measurements of received signal quality performance at different elevation angles and transmit power levels to obtain more stable results for substantive inference. The experimental test scenario considered a communication link, operating at UHF frequency of 436 MHz. During the experiment, azimuth and propagation loss for the communication link were kept constant while the receiving antenna elevation angles were varied to assess the impact of elevation angles. The assessment examined results obtained during the experiment. Comparing the received signal quality performance at zero (00) elevation angle, it has been observed that the received signal quality improves when the transmit power allocation increases. Results further show that for a given transmit power level of 34dBm, at zero (00) elevation angle test configuration, received signal quality performance of 1.80 dB, 6.90 dB at 300 elevation angle and 10.9 dB at 600 elevation angle were obtained, compared to improved quality performance of 11.8 dB at 00 elevation angle, 19.90 dB at 300 elevation angle and 24.92 dB at 600 elevation angle when the transmit power level was increased to 46.98 dBm. It is deduced from the experimental results that elevation angle of receiving antenna has significant influence on the received signal quality performance. This insight is very useful in the design and network planning of rural telecommunication services using TVWS frequencies for improved rural broadband penetration.

User plane acceleration service for next-generation cellular networks

User plane acceleration service for next-generation cellular networks

Implementation of mmWave long‐range backhaul for UAV‐BS

AbstractUncrewed aerial vehicles (UAVs) have become a vital element in nonterrestrial networks, especially with respect to 5G communication systems and beyond. The use of UAVs in support of 4G/5G base station (uncrewed aerial vehicle base station [UAV‐BS]) has proven to be a practical solution for extending cellular network services to areas where conventional infrastructures are unavailable. In this study, we introduce a UAV‐BS system that utilizes a high‐capacity wireless backhaul operating in millimeter‐wave frequency bands. This system can achieve a maximum throughput of 1.3 Gbps while delivering data at a rate of 300 Mbps, even at distances of 10 km. We also present the details of our testbed implementation alongside the performance results obtained from field tests.

NSEN: Improving Session Efficiency in Distributed MEC Networks

The rapid development of 5G technology and edge computing prompts emerging edge applications with heterogeneous requirements for network services with respect to latency, reliability and throughput. Although content distribution networks (CDN) and edge computing proposals such as multiaccess edge computing (MEC) improve quality of service by deploying services close to end users at the edge, the session efficiency of service still is constrained by host name resolution which also leads to quality degradation of service in the case of user mobility. In this article, we proposed a novel network architecture, Named Service Edge Network (NSEN), on the basis of DNS protocol. We enabled NSEN functionality in the MEC reference architecture and presented high-efficiency session models to satisfy the requirements of various edge applications and enhance the mobility support in distributed MEC environment. The simulation and experiment results demonstrate the NSENenabled MEC architecture can improve session efficiency of MEC services in 5G cellular networks in terms of latency and throughput.

Intelligent monitoring of vibration and structural-borne noise induced by rail transit

Abstract The monitoring of urban rail transit vibration and structural-borne noise can well solve the problem of small amount of data and large discreteness in traditional evaluation tests. In this study, a monitoring system is utilized to collect and preprocess vibration and noise signals. By employing cellular network and cloud-based services, real-time acquisition and analysis of vibration and noise signals are achieved. In this article, it is proposed to normalize the vibration data obtained after edge computing. After treatment, the gray correlation analysis method was used to determine the correlation between each vibration data component and vibration data classification. Combining frequency domain analysis of vibration data, the data components with high correlation are used as inputs to an improved K-nearest neighbours (KNN) model. Additionally, the correlation of each data component is introduced into the distance calculation formula. The improved KNN model shows improvements in recall rate, precision rate, F-measure, and accuracy compared to the original KNN model, with increases of 0.76 per cent, 2.76 per cent, 1.81 per cent, and 1.61 per cent respectively. Through practical measurements, it is found that different vehicles cause significant variations in vibration, with differences of up to 11 dB in tunnel wall vibration. The differences in tunnel wall vibration caused by the same vehicle at different passenger loads do not exceed 5 dB. Combining practical application cases, the rail transit environmental noise monitoring system established in this study demonstrates its applicability in monitoring vibration and noise-sensitive areas, as well as analysing the effectiveness of vibration reduction and noise control measures.

INVESTIGATING THE PATH LOSS OF CELLULAR MOBILE NETWORK IN SUBURBAN AREAS OF BENIN METROPOLIS OF NIGERIA AT 910MHZ

This paper focuses on investigating path loss of mobile cellular network in suburban areas of Benin metropolis at a propagation frequency of 910MHz. The network Cell Info Lite application installed in GIONEE M5 CDMA was used to measure the received signal strength of the transmitted signal. The relative parameters were obtained from 100meters from the Glo base station from August, 2021 to October, 2021 within an average temperature of 270C. The results showed that Hata models are extensively used for the analysis of path loss evident in mobile cellular networks, GSM and CDMA system as depicted by the terrain of sub-urban areas of Benin City Metropolis. It was observed that the more the mobile stations move away from the base station, the higher the signal loss. This paper therefore guides cellular network service provider on accurate method of designing, deploying, and managing network to enhance good network coverage.

Spatial-Temporal Cellular Traffic Prediction for 5G and Beyond: A Graph Neural Networks-Based Approach

During the past decade, Industry 4.0 has greatly promoted the improvement of industrial productivity by introducing advanced communication and network technologies in the manufacturing process. With the continuous emergence of new communication technologies and networking facilities, especially the rapid evolution of cellular networks for 5 G and beyond, the requirements for smarter, more reliable, and more efficient cellular network services have been raised from the Industry 5.0 blueprint. To meet these increasingly challenging requirements, proactive and effective allocation of cellular network resources becomes essential. As an integral part of the cellular network resource management system, cellular traffic prediction faces severe challenges with stringent requirements for accuracy and reliability. One of the most critical problems is how to improve the prediction performance by jointly exploring the spatial and temporal information within the cellular traffic data. A promising solution to this problem is provided by Graph Neural Networks (GNNs), which can jointly leverage the cellular traffic in the temporal domain and the physical or logical topology of cellular networks in the spatial domain to make accurate predictions. In this paper, we present the spatial-temporal analysis of a real-world cellular network traffic dataset and review the state-of-the-art researches in this field. Based on this, we further propose a time-series similarity-based graph attention network, TSGAN, for the spatial-temporal cellular traffic prediction. The simulation results show that our proposed TSGAN outperforms three classic prediction models based on GNNs or GRU on a real-world cellular network dataset in short-term, mid-term, and long-term prediction scenarios.

Predictive Quality of Service in Cellular Networks: Challenges, Framework, and Application in Vehicular Communications

Predictive quality of service will allow next-generation cellular networks to improve the management of the existing services and support more demanding use cases. In this article, we discuss the main challenges on the way to practical realizations of predictive quality of service, from fundamental considerations related to prediction horizons and handling uncertainty to practical aspects on data collection and implications of the architectural choice. We also present a general methodology for predictive quality of service that addresses some of the main challenges and may be used to realize predictive quality of service at scale. As a case study, we apply the framework to a vehicular communications setting using a data-set acquired from a deployed private LTE network.

An Attack to One-Tap Authentication Services in Cellular Networks

An Attack to One-Tap Authentication Services in Cellular Networks

Machine Learning-Based Integrated Wireless Sensing and Positioning for Cellular Network

Integrated sensing and communication (ISAC) is one of the most promising technical directions in B5G/6G era. Nevertheless, it is challenging for network operators to provide proper human-centric services (HCSs), appropriately with the explosive growth of mobile terminals in constantly changing scenarios. Scenario sensing is the foundation to provide users with intelligent context-aware services, while the combination of machine learning (ML) and automatic data collection enables network operators to become more flexible in decision-making and planning. In this article, an ML-based integrated indoor/outdoor (IO) sensing and positioning framework is proposed to improve the potential of ML in the context of cellular network service management. First, large amounts of measurement reports (MRs) are collected over Layer 3 at user equipment (UE) and evolved NodeBs (eNBs) in urban areas, including both indoor and outdoor samples to simulate the minimization of drive test (MDT). Then, a random-forest-based IO classifier is implemented to sense the mobile scenarios and filter the positioning fingerprints. Subsequently, the weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -nearest neighbor (WKNN)-based Enhanced Cell ID (ECID) merging with the MR method is used for cellular positioning. The performance evaluation has shown that the positioning error of the MRs after denoising is effectively reduced compared with the conventional fingerprint-based positioning. Specifically, under the 67% standard defined by Federal Communications Commission (FCC), the resulting positioning error is about 4% lower than the case without preprocessing. In addition, comparative experiments are conducted to discuss the impact of different <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> values at different data sizes of fingerprints. Moreover, the further data analysis shows that MRs collected in the mild indoor (MI) scenarios have a positive effect on the overall positioning error, which indicates that refined contextualizations contribute to the cellular positioning for HCS upgrade.

CeRA-eSP: Code-Expanded Random Access to Enhance Success Probability of Massive MTC.

With the growing interest in the Internet of Things (IoT), research on massive machine-type communication (mMTC) services is being actively promoted. Because mMTC services are required to serve a large number of devices simultaneously, a lack of resources during initial access can be a significant problem when providing mMTC services in cellular networks. Various studies on efficient preamble transmission have been conducted to solve the random access problem of mMTC services. However, supporting a large number of devices simultaneously with limited resources is a challenging problem. In this study, we investigate code-expanded random access (CeRA), which extends the limited preamble resources to the code domain to decrease the high collision rate. To solve the existing CeRA phantom codeword and physical uplink shared channel (PUSCH) resource shortage problems, we propose an optimal preamble codeword set selection algorithm based on mathematical analysis. The simulation results indicate that the proposed code-expanded random access scheme to enhance success probability (CeRA-eSP) achieves a higher random access success rate with a lower access delay compared to the existing random access schemes.

A value-added IoT service for cellular networks using federated learning

The number of Internet-of-Things (IoT) devices is expected to reach 64 billion by 2025. These IoT devices will mostly use cellular networks for transferring a huge amount of IoT data to the cloud for machine learning (ML) based forecasting. Keeping in view a large number of application scenarios for highly resource constraint IoT devices connected with the cellular networks, we propose a value-added IoT service (VAIS) for the cellular network operators based on the federated learning (FL) paradigm. Through simulation experiments, we show, for real air quality data and specific ML models, the proposed VAIS reduces the backhaul data by 70× and requires less energy than its equivalent cloud-based conventional approach, however, with a slight increase in communication time. From the insights we gained, we believe that a properly designed VAIS would efficiently utilize network resources, not only reduce management for IoT users but also the operating costs for cellular operators, and encourage IoT applications on limited backhaul cellular networks.

Fair and Energy-Efficient Coverage Optimization for UAV Placement Problem in the Cellular Network

Unmanned Aerial Vehicle (UAV) Base Station (BS) placement optimization is an essential operational task to improve the Quality of Service (QoS) in UAV-aided wireless cellular networks. The existing approaches are almost zeroth order methods, and the few first order methods mainly ignore the allocation fairness, computational efficiency, and backhaul constraints. In this paper, we formulate the UAV placement problem as a constrained optimization problem, with the objective of maximizing the fair coverage versus energy consumption while satisfying the backhaul constraints at different time nodes. To guarantee fair QoS allocation, we introduce a novel fairness index to ensure fair communication opportunity and the novel region coverage ratio to avoid excess QoS on covered spots. An accurate and efficient proximal stochastic gradient descent based alternating algorithm that iteratively executes two optimization steps is proposed to optimize the UAV locations, which enables the fast single point-based first order methods to solve the complex problems with constraints. Experiment results manifest that the proposed algorithm performs well both in synthetic data scenario and in real city scenario. Furthermore, the proposed first order algorithm is more efficient than the existing zeroth order algorithm, typically referring to the meta-heuristic method.

Cognitive radio based spectrum sharing models for multicasting in 5G cellular networks: A survey

The dramatic growth of smart devices catering content-centric data traffic has fueled the need for group communication services in cellular networks, among which multicasting is of particular interest. The unique potential of multicasting to fulfill the vision of a hyper-connected society in 5G networks is underlined by its ability to manage resources efficiently, while supporting concurrent transmission of common data to a large number of users. The bandwidth intensive communication requirements of multimedia multicast applications demand optimal usage of the spectral resources. Moreover, massive capacity, connectivity and ultra low latency requirements of the content centric applications have triggered the urge to get the content nearer to the users and employ short range communication, thus leading to densification of cellular networks. However, the limited availability of radio resources coupled with elevated levels of interference pose a major hindrance towards enabling spectrum reuse in dense networks. Cognitive radio (CR) is a viable technology to enhance the spectral efficiency, by allowing low priority unlicensed users to share the spectrum of high priority licensed users. The ability of CR to adapt its transmission parameters according to the application demands makes it a promising candidate for efficient spectrum management and interference mitigation in 5G cellular networks. This paper presents a survey on the state-of-the art techniques proposed for catering multicast services in CR networks. The study provides a detailed analysis on the utility of various spectrum sharing models of CR networks in fulfilling the end-to-end communication goals of multicast services. We particularly emphasize on the nature of multicast traffic, the key enabling techniques and methodologies employed for enhancing the efficacy of such services and approaches for mitigating interference experienced by the licensed users. By carefully analyzing the existing works, we provide possible design guidelines for multicast services in future 5G networks. Finally, we identify few open issues and discuss the future research directions.

Performance Evaluation of Key Performance Indicators for UMTS Networks in Calabar, Nigeria

For appropriate regulation and quality assurance, continuous monitoring of cellular network services is necessary. A drive test measurement was carried out in Calabar, to investigate four UMTS networks, taking cognizance of Call Setup Success Rate (CSSR), Drop Call Rate (DCR), Handover Success Rate (HOSR), Call Setup Time (CST), Received Signal Code Power (RSCP) and Signal-to-Interference Ratio (Ec/Io). The collected data was analyzed and the obtained results was compared with the performance threshold levels of the Nigerian Communication Commission (NCC). The four networks had excellent CST, with MTN leading, followed by Airtel, then 9mobile and Globacom. For CSSR, Airtel had excellent performance, as there was no blocked call. This was followed by MTN and Globacom. 9mobile failed in CSSR as it was below the NCC recommended benchmark. There was no drop call in Airtel, MTN and 9mobile, but Globacom’s performance was below the recommended threshold for dropped calls. For HOSR, the four networks showed excellent performance as they were able to handover calls between cells, as recommended by NCC. Considering the RSCP, the four networks displayed excellent services as subscribers experienced good voice and data services. However, the four networks had poor coverage quality (Ec/Io), the worst, being displayed by MTN. This shows that the equipment capability of the networks is low. The network operators are advised to monitor their services often, so as to optimize their networks for a better Quality of Service (QoS).

5G-EECC: Energy-Efficient Collaboration-Based Content Sharing Strategy in Device-to-Device Communication

In the past few years, mobile data traffic has seen exponential growth due to the emergence of smart applications. Although throughput enhancement techniques such as macro- and femtocells reduce cell size, they are relatively expensive to implement. Mobile device-to-device (D2D) communication has emerged as a solution to support the growing popularity of multimedia content for local service in next-generation 5G cellular networks. Content sharing is the prominent feature, which helps D2D communication in reducing offload traffic on the network, improving the energy efficiency of the device, and reducing backhaul connectivity costs. In traditional mapping approaches such as one to one or one to many, a massive amount of traffic is distributed among the devices resulting in high-energy consumption. In this paper, we propose a novel energy-efficient content sharing scheme called Energy-Efficient Collaboration-based Content (EECC) sharing strategy in D2D communication that shares content equally across devices based on their capacities and battery life under mobility. The proposed work includes cluster formation, cluster head selection, and helper node selection. In addition, we relied on a cooperative caching policy to ensure that content is distributed efficiently. The simulation results indicate a 12.05% reduction in energy compared to the state-of-the-art technique with a 2-gigabyte video file. To evaluate scalability, we increased the file size from 3 to 4 gigabytes, yet the performance in terms of energy consumption remained the same.

A Software-Defined Networking Solution for Interconnecting Network Functions in Service-Based Architectures

Mobile core networks handle critical control functions for delivering services in modern cellular networks. Traditional point-to-point architectures, where network functions are directly connected through standardized interfaces, are being substituted by service-based architectures (SBAs), where core functionalities are finer-grained microservices decoupled from the underlying infrastructure. In this way, network functions and services can be distributed, with scaling and fail-over mechanisms, and can be dynamically deployed, updated, or removed to support slicing. A myriad of network functions can be deployed or removed according to traffic flows, thereby increasing the complexity of connection management. In this context, 3GPP Release 16 defines the service communication proxy (SCP) as a unified communication interface for a set of network functions. In this paper, we propose a novel software-defined networking (SDN)-based solution with the same role for a service mesh architecture where network functions can be deployed anywhere in the infrastructure. We demonstrated its efficiency in comparison with alternative architectures.

Forecasting Quality of Service for Next-Generation Data-Driven WiFi6 Campus Networks

Forecasting the users’ movements and behaviors is extremely valuable for early warning systems to provide high-quality service in wireless and cellular networks. However, forecasting the service of the specific network devices with the additional knowledge of user behaviors is underexplored. This study proposes a WiFi6-specific QoS forecasting engine, which uses a spatio-temporal graph approach to predict QoS parameters, e.g., throughput, in terms of user position in WiFi6 networks. Since WiFi6 networks are planning to meet various traffic types with dense users, it is crucial to analyze it in both spatial and temporal manner by preserving graph-structured data. In this study, we modeled the problem with a novel deep learning approach, Graph Convolution Networks (GCNs), by adapting the Omni-Scale 1D CNN for temporal analysis. Then, we analyze the forecasting performance with two datasets in terms of variety error metrics over loss rate, link speed, throughput, and round trip time (RTT). Also, we give the baselines, ARIMA, FARIMA, SVR, and RNN to compare the proposed solution in terms of accuracy. Finally, we present the simulation results to compare the proposed QoS forecasting approach with user mobility forecasting. All experiments show that proposed WiFi6-specific QoS forecasting gives superior results for multi-horizon QoS prediction with respect to user positions considering heterogeneous traffic types.