Cellular Network Providers Research Articles

Empirical analysis of 5G deployments: A comparative assessment of network performance with 4G

The advent of 5G networks has introduced new challenges for network operators, particularly in terms of coverage, capacity, and service quality. Consequently, it is crucial to monitor and assess network performance to detect irregularities and enhance functionality and deployment efficiency. This is especially important for Non-Standalone (NSA) 5G networks, which rely on existing 4G infrastructure and may not fully utilize the advanced capabilities of 5G technology. This study evaluates wireless system/network performance, focusing on key quality indicators and data throughput within typical town settings featuring various operational scenarios. To achieve this, portable smartphone devices were employed to simultaneously measure real-world data from all 5G and 4G networks, providing a comparative analysis across all (three) cellular network providers in the area.Results highlight differing 5G rollout strategies and performance across operators, revealing that broad coverage may struggle with congestion issues, while a tailored deployment approach could yield better results. Additionally, the analysis pinpoints performance challenges related to coverage, throughput, resource utilization, and interference, thus guiding enhancements in 5G network deployments and establishing a baseline for tracking the evolution of 5G performance.

Investigating the influence of absorptive capacity of recipients within cross-border transfer of knowledge: evidence from emerging markets

PurposeThis study aims to investigate whether the absorptive capacity, learning intent of the recipient, the attractiveness of the knowledge source and the relationship quality between the recipient and the source in high-technology sectors in emerging markets influences the nature of the intra-organizational knowledge being transferred.Design/methodology/approachA total of 180 completed survey responses from all cellular network providers operating in Pakistan were analyzed. This study uses multiple regression analysis to empirically tests the above relationships using data from the cellular network sector in Pakistan.FindingsThe findings highlight how the highly educated workforce and the motivation to learn has an impact on the effective cross-border sharing of knowledge, both technological and marketing knowledge.Originality/valueThis study is one of the few to test the factors that influence the effective and efficient transfer of knowledge from developed to emerging markets.

Machine Learning Based Anomaly Detection as an Emerging Trend in Telecommunications

Research Question: This paper investigates into how machine learning can be applied for the purpose of detecting anomalies in the data describing transport component within the cellular network. Motivation: In the field of telecommunications, terabytes of data are generated each hour. This makes the manual analysis almost impossible to perform. There are thousands of components whose behaviour needs to be monitored, since anomalous behaviour could indicate a possible failure that can lead to network degradation, huge maintenance costs, and finally – a bad user experience. Our goal is to try to catch anomalous behaviour automatically, and thus help domain experts when performing drill down analysis of the degraded base stations and their key performance indicators (KPIs). Idea: The main idea of this paper is to empirically evaluate the application of machine learning for the problem of anomaly detection, in the field of telecommunications, specifically to long term evolution (LTE) networks. Data: Data used in the analysis contains information about base transceiver stations (BTS) behaviour through the time. The data are gathered from a cellular network provider located in Serbia. The data are collected on an hourly basis, for a period of two weeks, resulting in almost 700 thousand rows. The behaviour is assessed by 96 transport KPIs coming from BTS, describing the package losses, delays, transmission success rates, etc. Tools: Two main algorithms, ensemble-based Isolation Forest and autoencoder neural network, are elaborated and applied in order to identify patterns of anomalous behaviour. Findings: The results show that machine learning can be successfully applied in the field of LTE networks for the problem of anomaly detection. Machine learning can significantly reduce the time needed for the domain experts to identify anomalies within the network. In addition to time efficiency, one of the algorithms tested is able to identify anomalous KPIs separately, which is crucial when performing root cause analysis, by using drill-down approach, in order to identify which component is degraded. Contribution: This paper enriches existing research related to anomaly detection in LTE networks and provides an innovative approach to automated root-cause analysis of network degradation.

Spectral Efficiency Improvement of Full-Duplex D2D Communication in Cellular Networks

Radio spectrum is becoming scarce due to increasing demand for high data rate, mobile communication and ever-expanding population with diverse need to always stay interconnected. The cellular network providers and researchers in the academia are continually finding innovative ways to efficiently manage the existing telecommunication infrastructure and plan effective for future expansions and technology. Device to Device (D2D) communication, Full-Duplex (FD) radio, Heterogeneous network are a few of such innovative technologies developed to face the challenges. Device to device communication is one promising technology that is studied for deployment in future network technologies, however it is not without its challenges. Various researches have been carried out and are still being carried out to better understand and improve device to device capabilities. The use of full-duplex radios is an area of study with capability for improving device to device communication due to recent development in full-duplex radio although its major drawback is limited self-interference cancellation abilities to be deployed in large transmit power system. This research presents the practicality of deploying existing FD radios in device to device communication and simulate the amount of self-interference cancellation required using MATLAB for effective use with device to device. Two interference management schemes were implemented to improve the performance of FD-D2D communication, first power control scheme was developed to mitigate interference between D2D and base station in uplink resource sharing, Interference Limited Area (ILA) method was adapted to deal with interference between D2D and cellular user Uplink and Downlink transmission. The performance between 75dB to 110dB Self-Interference (SI) cancellation was carried out. The result was compared to conventional cellular and Half-Duplex D2D communication to estimate the improvement offered on spectral efficiency. The work has improved on achieving almost 100% spectral efficiency thereby improving the Quality of Service (QoS) for cellular network

Systematic review of congestion handling techniques for 802.11 wireless networks

SummaryIEEE 802.11 is a matured standard for wireless local area networks (WLANs). The cellular network providers had made every possible effort to offload their traffic over WLANs whenever it was possible, owing to the benefits that this technology provides. IEEE 802.11 is gaining greater prominence with the arrival of 802.11ax standard, which is backed by the Wi‐Fi alliance by branding as Wi‐Fi 6. The issue of congestion has been studied well for various networks. Particularly, in wireless networks, it needs to be tackled in a peculiar manner owing to the uncertain nature of the wireless medium. This study explores and evaluates the improvements done at various protocol layers to handle the problem of congestion in 802.11‐based wireless networks. An attempt is made to understand the implication of different parameters and the significance of cross‐layer interaction. Further, an endeavor is made to assess the influence on congestion control mechanisms in the different architectures of 802.11 networks. Lastly, the challenges that might be confronted at different protocol layers, while dealing with the issue of congestion, are classified.

A Next-Generation Core Network Architecture for Mobile Networks

Over the years, the cellular mobile network has evolved from a wireless plain telephone system to a very complex system providing telephone service, Internet connectivity and many interworking capabilities with other networks. Its air interface performance has increased drastically over time, leading to high throughput and low latency. Changes to the core network, however, have been slow and incremental, with increased complexity worsened by the necessity of backwards-compatibility with older-generation systems such as the Global System for Mobile communication (GSM). In this paper, a new virtualized Peer-to-Peer (P2P) core network architecture is presented. The key idea of our approach is that each user is assigned a private virtualized copy of the whole core network. This enables a higher degree of security and novel services that are not possible in today’s architecture. We describe the new architecture, focusing on its main elements, IP addressing, message flows, mobility management, and scalability. Furthermore, we will show some significant advantages this new architecture introduces. Finally, we investigate the performance of our architecture by analyzing voice-call traffic available in a database of a large U.S. cellular network provider.

WiFi Temporal Coverage: Analysis of Socio-Economics Influences in Malaysia

According to Internet Survey 2017, it was found that 89.4 percent of Internet users in Malaysia used a smartphone as a medium to access the internet. The proliferation of mobile devices and bandwidth hungry application, has resulted in mobile network provider (MNO) struggles to keep up with the ever-increasing demand for higher network capacity and wider network coverage. WiFi offloading seems the most viable solution due to the abundance and readily available infrastructure. Diverted traffic to the WiFi network may possibly reduce cellular network traffic, hence cellular network provider may be able to accommodate traffic growth at a lower cost. By analysing and understanding the user’s offloading behaviour may provide guidance in price and cost restructuring strategies or in network planning. This paper investigates temporal coverage of mobile user’s WiFi utilization and socio-economics influences through an extensive measurement study. The measurement study is carried out for 18 days and collected real data traces of user WiFi utilization on Android smartphone. The observation analysed user’s WiFi temporal coverage where participants are categorized into two different socio economics group. The finding reveals low WiFi temporal coverage for both groups of smartphones users. Group 1 spends more time using WiFi compared to Group 2, while both groups shows higher temporal coverage during active hour. Findings of this research may potentially serve as insights to the relevant stakeholder or regulatory body, as well as a barometer to carry out enhancement measures or any potential joint venture between cellular network provider and ISP.

Complex Intuitionistic Fuzzy Graphs with Application in Cellular Network Provider Companies

In recent years, a mathematical approach of blending different aspects is on the way, which as a result gives a more generalized approach. Following the above mathematical approach, we combine two very powerful techniques, namely complex intuitionistic fuzzy sets and graph theory, and introduce the notion of complex intuitionistic fuzzy graphs. Then, we introduce certain notions including union, join and composition of complex intuitionistic fuzzy graphs, through which one can easily manipulate the complex intuitionistic fuzzy graphs in decision making problems. We elucidate these operations with some examples. We also describe the homomorphisms of complex intuitionistic fuzzy graphs. Finally, we provide an application in cellular network provider companies for the testing of our approach.

Rural Broadband Access via Clustered Collaborative Communication

Broadband penetration in rural areas of the developing countries is significantly low. The unique challenges in enabling rural connectivity are sparsity of population locations and modest income of the villagers, which induce low return on investment to the conventional cellular network providers. In this paper, we propose a novel cluster-based network architecture and protocols for efficient rural broadband coverage which requires minimal infrastructure setup by the service provider. Multiple customer premise equipments (CPEs) in a village form clusters and transmit collaboratively over unused television bands to the base station. A two-tier uplink access protocol is proposed and its performance in terms of network throughput and energy efficiency are obtained analytically. The cluster size is optimized to maximize the uplink network throughput and energy-efficiency. A distributed clustering algorithm is proposed for the CPEs to form clusters, while channels are allocated to the clusters using the proposed channel allocation algorithm to minimize inter-cluster interference. Via network simulation studies we demonstrate that the proposed architecture can be cost-effective and energy-efficient, while being scalable at the same time.

Your Apps Give You Away

Understanding mobile app usage has become instrumental to service providers to optimize their online services. Meanwhile, there is a growing privacy concern that users' app usage may uniquely reveal who they are. In this paper, we seek to understand how likely a user can be uniquely re-identified in the crowd by the apps she uses. We systematically quantify the uniqueness of app usage via large-scale empirical measurements. By collaborating with a major cellular network provider, we obtained a city-scale anonymized dataset on mobile app traffic (1.37 million users, 2000 apps, 9.4 billion network connection records). Through extensive analysis, we show that the set of apps that a user has installed is already highly unique. For users with more than 10 apps, 88% of them can be uniquely re-identified by 4 random apps. The uniqueness level is even higher if we consider when and where the apps are used. We also observe that user attributes (e.g., gender, social activity, and mobility patterns) all have an impact on the uniqueness of app usage. Our work takes the first step towards understanding the unique app usage patterns for a large user population, paving the way for further research to develop privacy-protection techniques and building personalized online services.

Determinan Loyalitas Pelanggan pada Operator Telepon Selular

The Determinants of Customers' Loyalty in Celuler Phone Provider For cellular network providers to create customer satisfaction and customer loyalty is very important and become the primary business objectives. The current competition is more on improving the quality of their products and competing with each other to enhance optimal service. This study aims to identify customer loyalty determination through product quality, switching cost, price perceived, and customer satisfaction. The subjects of this study are students of several universities using Indosat IM3 Ooredoo with the number of respondents 170 by using purposive sampling technique. Methods of data analysis using descriptive analysis and inferential analysis using SEM Path Analysis technique. The results showed that 18.1% customer satisfaction variable could be explained by variable switching cost and price perceived. Those variables have a positive impact on customer loyalty. On the other hand, 60.5% customer loyalty variables can explain by price perceived and customer satisfaction variables. DOI: 10.15408/ess.v8i2.7586

ULOOF: A User Level Online Offloading Framework for Mobile Edge Computing

Mobile devices are equipped with limited processing power and battery charge. A mobile computation offloading framework is a software that provides better user experience in terms of computation time and energy consumption, also taking profit from edge computing facilities. This article presents User-Level Online Offloading Framework (ULOOF), a lightweight and efficient framework for mobile computation offloading. ULOOF is equipped with a decision engine that minimizes remote execution overhead, while not requiring any modification in the device’s operating system. By means of real experiments with Android systems and simulations using large-scale data from a major cellular network provider, we show that ULOOF can offload up to 73 percent of computations, and improve the execution time by 50 percent while at the same time significantly reducing the energy consumption of mobile devices.

A new scale of brand lovemarks

Purpose of the paper: The current study attempts to provide a new lovemarks scale that predicts consumers ’ behavioral outcomes. This scale also bridges over some of the inconsistencies of the measurement of “brand love” that also measure “brand respect”. Methodology : In order to test the Lovemarks scale, 3 studies were conducted. Study 1 applied Exploratory Factor Analysis using Principal Component exploratory factor analysis. Study 2 used second-order confirmatory factor analysis (CFA) with a maximum likelihood fitting function of the two-component solution. Study 3 measured the nomological validity of the assessed Lovemarks scale by testing its relations with four other relevant scales. Results : Using EFA and CFA, the reliability and validity of the scale were divided into four different product categories: dairy companies, cellular network providers, banks, and fashion retail chains. The scale does have strong positive correlations with attitude, preference, price premium and recommendation. Research limitations : The main limitation of the current research is that Study 2 used CFA testing only for second-order factors and not third-order factors, which would have also enabled the testing of the antecedents of the scale ’ s items (such as intimacy or trust). Practical implications : This scale helps to predict consumer behavior and set an effective marketing strategy for the brand. It thus gives directions for product adjustments and establishes effective advertising, marketing communication strategies and brand pricing strategy. Originality of the paper : The current study is testing a new Lovemarks scale on the basis of four different product categories: dairy companies, cellular network providers, banks, and fashion retail chains.

LTE 펨토셀 네트워크를 위한 적응적 주기의 MLB 알고리즘

The number of users and data packets has increased in 4G cellular networks. Therefore, 4G cellular network providers suffer from the network capacity problem. In order to solve this problem, femtocell concept is suggested. It can reduce the coverage hole and enhance the QoS. However, only small number of femtocells experience the large amount of loads. To solve this problem, Mobility Load Balancing (MLB) algorithm is suggested, which is a kind of load balancing algorithm. To distribute the traffic load, MLB algorithm modifies the handover region. If the handover region is reduced by MLB algorithm, some cell edge users are compulsively handed over to neighbor femtocell. In this paper, we analyze the relation between MLB performing period and performance indicators. For example throughput and blocking probability is reduced, if period is decreased. On the contrast, if period is increased, the number of handover frequency is decreased. Using this relation, we suggest the adaptive periodic MLB algorithm. This algorithm includes the advantage of both long period and short period MLB algorithm, such as high throughput, the small number of handover frequency, and low blocking probability.

Taming the Mobile Data Deluge With Drop Zones

Human communication has changed by the advent of smartphones. Using commonplace mobile device features, they started uploading large amounts of content that increases. This increase in demand will overwhelm capacity and limits the providers' ability to provide the quality of service demanded by their users. In the absence of technical solutions, cellular network providers are considering changing billing plans to address this. Our contributions are twofold. First, by analyzing user content upload behavior, we find that the user-generated content problem is a user behavioral problem. Particularly, by analyzing user mobility and data logs of 2 million users of one of the largest US cellular providers, we find that: 1) users upload content from a small number of locations; 2) because such locations are different for users, we find that the problem appears ubiquitous. However, we find that: 3) there exists a significant lag between content generation and uploading times, and 4) with respect to users, it is always the same users to delay. Second, we propose a cellular network architecture. Our approach proposes capacity upgrades at a select number of locations called Drop Zones. Although not particularly popular for uploads originally, Drop Zones seamlessly fall within the natural movement patterns of a large number of users. They are therefore suited for uploading larger quantities of content in a postponed manner. We design infrastructure placement algorithms and demonstrate that by upgrading infrastructure in only 963 base stations across the entire US, it is possible to deliver 50% of content via Drop Zones.

A spatial model of received signal strength indicator values for automated collision notification technology

This paper reports on a spatial model that can be used to support evaluation of the effectiveness of automated collision notification (ACN) technology. Such systems rely on wireless phones to transmit their crash messages. However, cellular coverage exhibits great variation in hilly and rural areas, and these are regions where ACN devices provide their greatest benefits. In this context, the accurate prediction of cell phone signal strength is of paramount importance and hence, an in-depth analysis of the variables influencing the cellular signal is necessary. In this paper, we first model the spatial variation of cell phone coverage using an interpolation method known as kriging. We focus on how particular factors such as slope, altitude, distance to the cell tower and topographic obstruction are used to improve the prediction of cell phone signal strength. The effect of these factors is evaluated in a multiple regression framework, followed by an analysis of the residuals, providing valuable information for cellular network providers. However, since the residuals show some spatial structure, we therefore analyze the merits of a more suitable method, namely spatial regression. We illustrate the approach using data on cell phone coverage in a hilly section of Erie county, New York. The model should (a) enhance our knowledge of the effect of particular factors on signal strength intensity, and (b) result in improved estimates of RSSI at unknown locations. The latter is important in establishing the effectiveness of automated collision notification (ACN) technology.

Network selection in an integrated wireless LAN and UMTS environment using mathematical modeling and computing techniques

The increasing demand for broadband service, at least in hot spot areas, in today's wireless communications is causing cellular network providers to consider the integration of 3G cellular systems and wireless LAN. This has the particular advantage of high data rates and unlicensed spectrum. Consequently, network selection techniques play a vital role in ensuring quality of service in heterogeneous networks. In this article we develop a network selection scheme for an integrated cellular/wireless LAN system. The design goal is to provide the user the best available QoS at any time. The proposed scheme comprises two parts, with the first applying an analytic hierarchy process (AHP) to decide the relative weights of evaluative criteria set according to user preferences and service applications, while the second adopts grey relational analysis (GRA) to rank the network alternatives with faster and simpler implementation than AHP. Simulations conducted in a heterogeneous system with UMTS and wireless LAN reveal that the proposed network selection technique can effectively decide the optimum network through making trade-offs among network condition, user preference, and service application, while avoiding frequent handoffs.

Service provider oriented management systems over open cellular network infrastructures

An interesting concept for the open cellular communications world of the near future is to enable service providers (SPs) to dynamically find, and co-operate with, the best cellular network providers (NPs), i.e., those offering desired quality levels, in the most cost-efficient manner, at a given service area region and time zone. This concept calls for an evolution of legacy management paradigms. In this direction, this paper presents parts of a Service Management System (SMS) that adopts the perspective of an SP (SP-SMS). The paper provides elements of the system design (functionality layers, components in each layer, component distribution pattern and high level functionality) and a more detailed look at the functionality of some of the SP-SMS components. The SP-SMS will be decomposed in three layers, namely session configuration, local planning, and global planning. A component type will be introduced in each layer. The role of the different components in the SP-SMS layers will be described. Emphasis will be given to the description of the functionality of the components in the local planning layer. Specifically, the problem addressed at that layer enables an SP to allocate its service demand, at the best possible (desired) quality levels, and at the most cost-efficient cellular NPs, within a service area region and time-zone. Finally, results showing merits from the introduction of an SP-SMS are presented, and concluding remarks are made.